From 40d1c6824e71d232332063c1cdf44f96be4ce9ab Mon Sep 17 00:00:00 2001 From: govin08 Date: Mon, 5 Feb 2024 11:56:25 +0900 Subject: [PATCH] produces get_intermediates.py --- Analysis/0202_config_/config.ini | 8 + Analysis/0202_config_/config.json | 1 + Analysis/0202_config_/main.py | 34 + Data/networks/state_300.00.xml.gz | Bin 3759 -> 3757 bytes ...신호생성_단위테스트_내역서.hwp | Bin 0 -> 307712 bytes .../table_definition_v0.8.4.xlsx | Bin 75737 -> 75407 bytes Intermediates/histid/histid_1704389400.csv | 129 -- Intermediates/histid/histid_1704389700.csv | 141 --- Intermediates/histid/histid_1704390000.csv | 138 --- Intermediates/histid/histid_1704390300.csv | 129 -- Intermediates/histid/histid_1704390600.csv | 134 --- Intermediates/histid/histid_1704390900.csv | 129 -- Intermediates/histid/histid_1704391200.csv | 127 -- Intermediates/histid/histid_1704391500.csv | 132 -- Intermediates/histid/histid_1704391800.csv | 136 --- Intermediates/histid/histid_1704392100.csv | 135 --- 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Intermediates/histid/histid_1704398100.csv | 136 --- Intermediates/histid/histid_1704398400.csv | 136 --- Intermediates/histid/histid_1704398700.csv | 132 -- Intermediates/histid/histid_1704399000.csv | 134 --- Intermediates/histid/histid_1704399300.csv | 134 --- Intermediates/histid/histid_1704399600.csv | 132 -- Intermediates/histid/histid_1704399900.csv | 132 -- Intermediates/histid/histid_1704400200.csv | 141 --- Intermediates/histid/histid_1704400500.csv | 136 --- Intermediates/histid/histid_1704400800.csv | 126 -- Intermediates/histid/histid_1704401100.csv | 133 --- Intermediates/histid/histid_1704401400.csv | 134 --- Intermediates/histid/histid_1704401700.csv | 132 -- Intermediates/histid/histid_1704402000.csv | 132 -- Intermediates/histid/histid_1704402300.csv | 141 --- Intermediates/histid/histid_1704402600.csv | 132 -- Intermediates/histid/histid_1704402900.csv | 127 -- Intermediates/histid/histid_1704403200.csv | 134 --- Intermediates/histid/histid_1704403500.csv | 134 --- 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Intermediates/histid/histid_1704409500.csv | 118 -- Intermediates/histid/histid_1704409800.csv | 116 -- Intermediates/histid/histid_1704410100.csv | 123 -- Intermediates/histid/histid_1704410400.csv | 125 -- Intermediates/histid/histid_1704410700.csv | 128 -- Intermediates/histid/histid_1704411000.csv | 109 -- Intermediates/histid/histid_1704411300.csv | 102 -- Intermediates/histid/histid_1704411600.csv | 121 -- Intermediates/histid/histid_1704411900.csv | 558 ++++----- Intermediates/histid/histid_1704412200.csv | 562 ++++----- Intermediates/histid/histid_1704412500.csv | 117 -- Intermediates/histid/histid_1704412800.csv | 116 -- Intermediates/histid/histid_1704413100.csv | 119 -- Intermediates/histid/histid_1704413400.csv | 118 -- Intermediates/histid/histid_1704413700.csv | 111 -- Intermediates/histid/histid_1704414000.csv | 112 -- Intermediates/histid/histid_1704414300.csv | 114 -- Intermediates/histid/histid_1704414600.csv | 113 -- Intermediates/histid/histid_1704414900.csv | 105 -- Intermediates/histid/histid_1704415200.csv | 110 -- Intermediates/histid/histid_1704415500.csv | 120 -- Intermediates/histid/histid_1704415800.csv | 116 -- Intermediates/histid/histid_1704416100.csv | 109 -- Intermediates/histid/histid_1704416400.csv | 111 -- Intermediates/histid/histid_1704416700.csv | 112 -- Intermediates/histid/histid_1704417000.csv | 112 -- Intermediates/histid/histid_1704417300.csv | 108 -- Intermediates/histid/histid_1704417600.csv | 107 -- Intermediates/histid/histid_1704417900.csv | 115 -- Intermediates/histid/histid_1704418200.csv | 111 -- Intermediates/histid/histid_1704418500.csv | 111 -- Intermediates/histid/histid_1704418800.csv | 110 -- Intermediates/histid/histid_1704419100.csv | 107 -- Intermediates/histid/histid_1704419400.csv | 118 -- Intermediates/histid/histid_1704419700.csv | 112 -- Intermediates/histid/histid_1704420000.csv | 102 -- Intermediates/histid/histid_1704420300.csv | 116 -- Intermediates/histid/histid_1704420600.csv | 119 -- Intermediates/histid/histid_1704420900.csv | 111 -- Intermediates/histid/histid_1704421200.csv | 113 -- Intermediates/histid/histid_1704421500.csv | 108 -- Intermediates/histid/histid_1704421800.csv | 107 -- Intermediates/histid/histid_1704422100.csv | 120 -- Intermediates/histid/histid_1704422400.csv | 114 -- Intermediates/histid/histid_1704422700.csv | 102 -- Intermediates/histid/histid_1704423000.csv | 116 -- Intermediates/histid/histid_1704423300.csv | 117 -- Intermediates/histid/histid_1704423600.csv | 104 -- Intermediates/histid/histid_1704423900.csv | 111 -- Intermediates/histid/histid_1704424200.csv | 115 -- Intermediates/histid/histid_1704424500.csv | 111 -- Intermediates/histid/histid_1704424800.csv | 115 -- Intermediates/histid/histid_1704425100.csv | 110 -- Intermediates/histid/histid_1704425400.csv | 107 -- Intermediates/histid/histid_1704425700.csv | 118 -- Intermediates/histid/histid_1704426000.csv | 112 -- Intermediates/histid/histid_1704426300.csv | 106 -- Intermediates/histid/histid_1704426600.csv | 116 -- Intermediates/histid/histid_1704426900.csv | 115 -- Intermediates/histid/histid_1704427200.csv | 111 -- Intermediates/histid/histid_1704427500.csv | 115 -- Intermediates/histid/histid_1704427800.csv | 110 -- Intermediates/histid/histid_1704428100.csv | 105 -- Intermediates/histid/histid_1704428400.csv | 118 -- Intermediates/histid/histid_1704428700.csv | 114 -- Intermediates/histid/histid_1704429000.csv | 102 -- Intermediates/histid/histid_1704429300.csv | 111 -- Intermediates/histid/histid_1704429600.csv | 124 -- Intermediates/histid/histid_1704429900.csv | 106 -- Intermediates/histid/histid_1704430200.csv | 91 -- Intermediates/histid/histid_1704430500.csv | 107 -- Intermediates/histid/histid_1704430800.csv | 109 -- Intermediates/histid/histid_1704431100.csv | 116 -- Intermediates/histid/histid_1704431400.csv | 119 -- Intermediates/histid/histid_1704431700.csv | 107 -- Intermediates/histid/histid_1704432000.csv | 111 -- Intermediates/histid/histid_1704432300.csv | 118 -- Intermediates/histid/histid_1704432600.csv | 107 -- Intermediates/histid/histid_1704432900.csv | 110 -- Intermediates/histid/histid_1704433200.csv | 116 -- Intermediates/histid/histid_1704433500.csv | 106 -- Intermediates/histid/histid_1704433800.csv | 112 -- Intermediates/histid/histid_1704434100.csv | 119 -- Intermediates/histid/histid_1704434400.csv | 106 -- Intermediates/histid/histid_1704434700.csv | 111 -- Intermediates/histid/histid_1704435000.csv | 119 -- Intermediates/histid/histid_1704435300.csv | 105 -- Intermediates/histid/histid_1704435600.csv | 109 -- Intermediates/histid/histid_1704435900.csv | 118 -- Intermediates/histid/histid_1704436200.csv | 111 -- Intermediates/histid/histid_1704436500.csv | 112 -- Intermediates/histid/histid_1704436800.csv | 119 -- Intermediates/histid/histid_1704437100.csv | 106 -- Intermediates/histid/histid_1704437400.csv | 111 -- Intermediates/histid/histid_1704437700.csv | 117 -- Intermediates/histid/histid_1704438000.csv | 105 -- Intermediates/histid/histid_1704438300.csv | 111 -- Intermediates/histid/histid_1704438600.csv | 118 -- Intermediates/histid/histid_1704440400.csv | 211 ++++ Results/issues_intermediates.txt | 1 + Script/generate_signals.ipynb | 651 +++++++++- Script/get_intermediates.py | 587 +++++++++ Script/get_signals.py | 587 +++++++++ Script/preprocess.ipynb | 1062 +++++++++-------- Script/preprocess_5min.ipynb | 1004 ++++++++++++++++ Script/preprocess_daily.ipynb | 690 +++++++++++ Script/preprocess_daily.py | 431 +++++++ Script/preprocess_daily_1.py | 587 +++++++++ 181 files changed, 5793 insertions(+), 20773 deletions(-) create mode 100644 Analysis/0202_config_/config.ini create mode 100644 Analysis/0202_config_/config.json create mode 100644 Analysis/0202_config_/main.py create mode 100644 Documents/0202_unit_test/신호생성_단위테스트_내역서.hwp create mode 100644 Results/issues_intermediates.txt create mode 100644 Script/get_intermediates.py create mode 100644 Script/get_signals.py create mode 100644 Script/preprocess_5min.ipynb create mode 100644 Script/preprocess_daily.ipynb create mode 100644 Script/preprocess_daily.py create mode 100644 Script/preprocess_daily_1.py diff --git a/Analysis/0202_config_/config.ini b/Analysis/0202_config_/config.ini new file mode 100644 index 000000000..8ed11c80e --- /dev/null +++ b/Analysis/0202_config_/config.ini @@ -0,0 +1,8 @@ +[DEFAULT] +user = example_user +password = example_password + +[MySQL] +host = example_host +database = example_database + diff --git a/Analysis/0202_config_/config.json b/Analysis/0202_config_/config.json new file mode 100644 index 000000000..bfa332081 --- /dev/null +++ b/Analysis/0202_config_/config.json @@ -0,0 +1 @@ +{"user": "example_user", "password": "example_password", "host": "example_host", "database": "example_database"} \ No newline at end of file diff --git a/Analysis/0202_config_/main.py b/Analysis/0202_config_/main.py new file mode 100644 index 000000000..6a5258de3 --- /dev/null +++ b/Analysis/0202_config_/main.py @@ -0,0 +1,34 @@ +import os +import sys +from pathlib import Path + + +if __name__ == '__main__': + + # 현재 .py이 위치한 디렉토리 + script_dir = os.path.dirname(os.path.abspath(__file__)) + script_dir = Path(script_dir) + + # json 예시 + import json + + with open(script_dir / 'config.json', 'r') as config_file: + config = json.load(config_file) + + print('==== json 예시 ====') + print(config) + print() + + # ini 예시 + from configparser import ConfigParser + + config = ConfigParser() + config.read(script_dir / 'config.ini') + + user = config['DEFAULT']['User'] + password = config['DEFAULT']['Password'] + host = config['MySQL']['Host'] + database = config['MySQL']['Database'] + + print('==== ini 예시 ====') + print(user, password, host, database) diff --git a/Data/networks/state_300.00.xml.gz b/Data/networks/state_300.00.xml.gz index 609831f29240f31ad407f248c81bd88435f231db..4ea8b34256d717611a130680a4c88535ab1a5afb 100644 GIT binary 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4,\n", + " 'i6': 4,\n", + " 'i7': 4,\n", + " 'i8': 4,\n", + " 'i9': 4,\n", + " 'u00': 3,\n", + " 'u20': 4,\n", + " 'u30': 4,\n", + " 'u31': 4,\n", + " 'u32': 4,\n", + " 'u60': 4}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# node2num_cycles : A dictionary that maps a node_id to the number of cycles\n", "def get_node2num_cycles(plan, node_ids):\n", @@ -274,7 +298,6 @@ " node2numcycles = {node_id : inter2num_cycles[node2inter[node_id]] for node_id in node_ids}\n", " with open('../Intermediates/node2numcycles.json', 'w') as file:\n", " json.dump(node2numcycles, file, indent=4)\n", - "\n", " return node2numcycles\n", "node2num_cycles = get_node2num_cycles(plan, node_ids)\n", "node2num_cycles" @@ -305,14 +328,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "{'c30': 4,\n", + " 'i0': 3,\n", + " 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1. 데이터 불러오기 + def load_data(self): + self.load_networks() + self.load_tables() + self.check_networks() + self.check_tables() + print('1. 모든 데이터가 로드되었습니다.') + + # 1-1. 네트워크 불러오기 + def load_networks(self): + self.net = sumolib.net.readNet(os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml')) + print("1-1. 네트워크가 로드되었습니다.") + + # 1-2. 테이블 불러오기 + def load_tables(self): + # 모든 컬럼에 대하여 데이터타입 지정 + loading_dtype = { + 'inter_no':'int', 'start_hour':'int', 'start_minute':'int', 'cycle':'int','offset':'int', + 'node_id':'str', 'inter_type':'str', 'parent_id':'str','child_id':'str', + 'direction':'str', 'condition':'str', 'inc_edge':'str', 'out_edge':'str', + 'end_unix':'int', 'inter_name':'str', 'inter_lat':'float', 'inter_lon':'float', + 'group_no':'int', 'main_phase_no':'int', 'phase_no':'int','ring_type':'str' + } + for alph in ['A', 'B']: + for j in range(1,9): + loading_dtype[f'angle_{alph}{j}'] = 'str' + loading_dtype[f'dura_{alph}{j}'] = 'int' + + self.path_table = os.path.join(self.path_root, 'Data', 'tables') + + self.inter_info = pd.read_csv(os.path.join(self.path_table, 'inter_info.csv'), dtype=loading_dtype) + self.angle = pd.read_csv(os.path.join(self.path_table, 'angle.csv'), dtype=loading_dtype) + self.plan = pd.read_csv(os.path.join(self.path_table, 'plan.csv'), dtype=loading_dtype) + self.inter_node = pd.read_csv(os.path.join(self.path_table, 'inter_node.csv'), dtype=loading_dtype) + self.uturn = pd.read_csv(os.path.join(self.path_table, 'child_uturn.csv'), dtype=loading_dtype) + self.coord = pd.read_csv(os.path.join(self.path_table, 'child_coord.csv'), dtype=loading_dtype) + self.nema = pd.read_csv(os.path.join(self.path_table, 'nema.csv'), encoding='cp949', dtype=loading_dtype) + print("1-2. 테이블들이 로드되었습니다.") + + # 1-3. 테이블 불러오기 + def check_networks(self): + # https://sumo.dlr.de/docs/Netedit/neteditUsageExamples.html#simplify_tls_program_state_after_changing_connections + if 'SUMO_HOME' in os.environ: + tools = os.path.join(os.environ['SUMO_HOME'], 'tools') + if tools not in sys.path: + sys.path.append(tools) + else: + raise EnvironmentError("please declare environment variable 'SUMO_HOME'") + traci.start([sumolib.checkBinary('sumo'), "-n", os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml')]) + nodes = [node for node in self.net.getNodes() if node.getType()=='traffic_light'] + for node in nodes: + node_id = node.getID() + from_xml = len([c for c in node.getConnections() if c.getTLLinkIndex() >= 0]) + from_traci = len(traci.trafficlight.getRedYellowGreenState(node_id)) + if from_xml != from_traci: + sub = {'id': node_id, 'type': 'node', 'note': '유효하지 않은 연결이있음. netedit에서 clean states 필요.'} + self.issues.append(sub) + traci.close() + print("1-3. 네트워크의 모든 clean state requirement들을 체크했습니다.") + + # 1-4. 테이블의 무결성 검사 + def check_tables(self): + self.check_inter_info() + self.check_angle() + self.check_plan() + print("1-4. 모든 테이블들의 무결성을 검사했고 이상 없습니다.") + pass + + # 1-4-1. 교차로정보(inter_info) 검사 + def check_inter_info(self): + # 1-4-1-1. inter_lat, inter_lon 적절성 검사 + # self.inter_info.loc[0, 'inter_lat'] = 38.0 # 에러 발생을 위한 코드 + self.max_lon, self.min_lon = 127.207888, 127.012492 + self.max_lat, self.min_lat = 37.480693, 37.337112 + for _, row in self.inter_info.iterrows(): + latbool = self.min_lat <= row['inter_lat'] <= self.max_lat + lonbool = self.min_lon <= row['inter_lon'] <= self.max_lon + if not(latbool and lonbool): + msg = f"1-4-1-1. 위도 또는 경도가 범위를 벗어난 교차로가 있습니다: inter_no : {row['inter_no']}" + self.issues.append(msg) + # 교차로목록 정의 + self.inter_nos = sorted(self.inter_info.inter_no.unique()) + + # 1-4-2. 방위각정보(inter_info) 검사 + def check_angle(self): + # 1-4-2-1. inter_no 검사 + # self.angle.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드 + missing_inter_nos = set(self.angle.inter_no) - set(self.inter_nos) + if missing_inter_nos: + msg = f"1-4-2-1. angle의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}" + self.issues.append(msg) + + # 1-4-3. 신호계획(plan) 검사 + def check_plan(self): + # 1-4-3-1. inter_no 검사 + # self.plan.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드 + missing_inter_nos = set(self.plan.inter_no) - set(self.inter_nos) + if missing_inter_nos: + msg = f"1-4-3-1. plan의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}" + self.issues.append(msg) + + # 1-4-3-2. 시작시각 검사 + # self.plan.loc[0, 'start_hour'] = 27 # 에러 발생을 위한 코드 + for _, row in self.plan.iterrows(): + start_hour = row.start_hour + start_minute = row.start_minute + if not (0 <= start_hour <= 23) or not (0 <= start_minute <= 59): + msg = f"1-4-3-2. plan에 잘못된 형식의 start_time이 존재합니다: {start_hour, start_minute}" + self.issues.append(msg) + + # 1-4-3-3. 현시시간 검사 + # self.plan.loc[0, 'dura_A1'] = -2 # 에러 발생을 위한 코드 + durations = self.plan[[f'dura_{alph}{j}' for alph in ['A','B'] for j in range(1, 9)]] + valid_indices = ((durations >= 0) & (durations <= 200)).all(axis=1) + invalid_inter_nos = sorted(self.plan[~ valid_indices].inter_no.unique()) + if invalid_inter_nos: + msg = f"1-4-3-3. 음수이거나 200보다 큰 현시시간이 존재합니다. : {invalid_inter_nos}" + + # 1-4-3-4. 주기 일관성 검사 + # self.plan.loc[0, 'cycle'] = 50 # 에러 발생을 위한 코드 + inconsistent_cycle = self.plan.groupby(['inter_no', 'start_hour', 'start_minute'])['cycle'].nunique().gt(1) + if inconsistent_cycle.any(): + inc_inter_no, start_hour, start_minute = inconsistent_cycle[inconsistent_cycle].index[0] + msg = f"1-4-3-4. inter_no:{inc_inter_no}, start_hour:{start_minute}, start_hour:{start_minute}일 때, cycle이 유일하게 결정되지 않습니다." + self.issues.append(msg) + + # 1-4-3-5. 현시시간 / 주기 검사 + # self.plan.loc[0, 'duration'] = 10 # 에러 발생을 위한 코드 + right_duration = True + for (inter_no, start_hour, start_minute), group in self.plan.groupby(['inter_no', 'start_hour', 'start_minute']): + A_sum = group[[f'dura_A{j}' for j in range(1, 9)]].iloc[0].sum() + B_sum = group[[f'dura_B{j}' for j in range(1, 9)]].iloc[0].sum() + # A_sum = group[group['ring_type']=='A']['duration'].sum() + # B_sum = group[group['ring_type']=='B']['duration'].sum() + cycle = group['cycle'].unique()[0] + if not (A_sum == B_sum == cycle): + right_duration = False + inc_inter_no = inter_no + if not right_duration: + msg = f"1-4-4-5. inter_no:{inc_inter_no}, A링현시시간의 합과 B링현시시간의 합이 일치하지 않거나, 현시시간의 합과 주기가 일치하지 않습니다." + self.issues.append(msg) + + # 2. 중간산출물 만들기 + def get_intermediates(self): + self.get_matches() + # self.get_movements() + self.get_node2num_cycles() + + # 2-1 매칭테이블들 생성 + def get_matches(self): + self.make_match1() + self.make_match2() + self.make_match3() + self.make_match4() + self.make_match5() + self.make_match6() + self.make_matching() + + # 2-1-1 + def make_match1(self): + ''' + 신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다. + '../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다. + + return : 통합된 이동류정보 + - 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보 + + match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다. + ''' + # [이동류번호] 불러오기 (약 1분의 소요시간) + path_move = os.path.join(self.path_root, 'Data', 'tables', 'move') + csv_moves = os.listdir(path_move) + moves = [pd.read_csv(os.path.join(path_move, csv_move), index_col=0) for csv_move in tqdm(csv_moves, desc='이동류정보 불러오는 중 : match1')] + self.match1 = pd.concat(moves).drop_duplicates().sort_values(by=['inter_no','phas_A','phas_B']).reset_index(drop=True) + self.match1.to_csv(os.path.join(self.path_root, 'Intermediates', 'match1.csv')) + + # 2-1-2 + def make_match2(self): + ''' + match1을 계층화함. + - match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + ''' + # 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no') + matchA = self.match1[['inter_no', 'phas_A', 'move_A']].copy() + matchA.columns = ['inter_no', 'phase_no', 'move_no'] + matchA['ring_type'] = 'A' + matchB = self.match1[['inter_no', 'phas_B', 'move_B']].copy() + matchB.columns = ['inter_no', 'phase_no', 'move_no'] + matchB['ring_type'] = 'B' + self.match2 = pd.concat([matchA, matchB]).drop_duplicates() + self.match2 = self.match2[['inter_no', 'phase_no', 'ring_type', 'move_no']] + self.match2 = self.match2.sort_values(by=list(self.match2.columns)) + + # 2-1-3 + def make_match3(self): + ''' + 각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함. + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + + nema : + - 컬럼 : move_no, inc_dir, out_dir + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + # nema 정보 불러오기 및 병합 + self.match3 = pd.merge(self.match2, self.nema, how='left', on='move_no').drop_duplicates() + + # 2-1-4 + def make_match4(self): + ''' + 방위각 정보를 매칭시켜 추가함. + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + + angle_original : + - 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8) + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + + # 계층화 + angles = [] + for i, row in self.angle.iterrows(): + angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] + new = pd.DataFrame({'inter_no':[row.inter_no] * 16, 'phase_no':list(range(1, 9))*2, 'ring_type':['A'] * 8 + ['B'] * 8, 'angle_code':angle_codes.to_list()}) + angles.append(new) + angles = pd.concat(angles) + angles = angles.dropna().reset_index(drop=True) + + # 병합 + six_chars = angles.angle_code.apply(lambda x:len(x)==6) + angles.loc[six_chars,'inc_angle'] = angles.angle_code.apply(lambda x:x[:3]) + angles.loc[six_chars,'out_angle'] = angles.angle_code.apply(lambda x:x[3:]) + angles = angles.drop('angle_code', axis=1) + self.match4 = pd.merge(self.match3, angles, how='left', left_on=['inter_no', 'phase_no', 'ring_type'], + right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates() + + # 2-1-5 + def make_match5(self): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로). + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + - match5의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) net + - 성남시 정자동 부근의 샘플 네트워크 + (2) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (3) inter_info + - 교차로 정보. 여기에서는 위도와 경도가 쓰임. + - 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no + + 진입엣지id, 진출엣지id를 얻는 과정 : + - match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함. + * 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음 + - 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장. + * inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge + - inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄. + - 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄. + - 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함. + ''' + + # parent node만 가져옴. + inter_node1 = self.inter_node[self.inter_node.inter_type == 'parent'].drop('inter_type', axis=1) + inter_info1 = self.inter_info[['inter_no', 'inter_lat', 'inter_lon']] + inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'], + right_on=['inter_no']).drop_duplicates() + + self.inter2node = dict(zip(inter['inter_no'], inter['node_id'])) + + self.match5 = self.match4.copy() + # 진입진출ID 매칭 + for index, row in self.match5.iterrows(): + node_id = self.inter2node[row.inter_no] + node = self.net.getNode(node_id) + # 교차로의 모든 (from / to) edges + inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges + out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges + # 교차로의 모든 (from / to) directions + inc_dirs = [] + for inc_edge in inc_edges: + start = inc_edge.getShape()[-2] + end = inc_edge.getShape()[-1] + inc_dir = np.array(end) - np.array(start) + inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5 + inc_dirs.append(inc_dir) + out_dirs = [] + for out_edge in out_edges: + start = out_edge.getShape()[0] + end = out_edge.getShape()[1] + out_dir = np.array(end) - np.array(start) + out_dir = out_dir / (out_dir ** 2).sum() ** 0.5 + out_dirs.append(out_dir) + # 진입각, 진출각 불러오기 + if not pd.isna(row.inc_angle): + inc_angle = int(row.inc_angle) + out_angle = int(row.out_angle) + # 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환 + inc_angle = (-90 - inc_angle) % 360 + inc_angle = inc_angle * np.pi / 180. + inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)]) + out_angle = (90 - out_angle) % 360 + out_angle = out_angle * np.pi / 180. + out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)]) + # 매칭 엣지 반환 + inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax() + out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax() + inc_edge_id = inc_edges[inc_index].getID() + out_edge_id = out_edges[out_index].getID() + self.match5.at[index, 'inc_edge'] = inc_edge_id + self.match5.at[index, 'out_edge'] = out_edge_id + self.match5['node_id'] = self.match5['inter_no'].map(self.inter2node) + self.match5 = self.match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True) + + # 2-1-6 + def make_match6(self): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로). + - match6의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (2) uturn (유턴정보) + - 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 유턴교차로id + - direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향) + - condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나 + - inc_edge, out_edge : 유턴에 대한 진입진출엣지 + (3) coord (연동교차로정보) + - 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 연동교차로id + - 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지 + + 설명 : + - match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음. + 여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함. + 유턴교차로 : + - directions를 정북기준 시계방향의 8방위로 정함. + - 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음. + - 예) 진입방향(direction)이 '북'일 때, + - 직진 : (북, 남) + * 남 : directions[(ind + 4) % len(directions)] + - 좌회전 : (북, 동) + * 동 : directions[(ind + 2) % len(directions)] + - 보행 : (서, 동) + * 서 : directions[(ind - 2) % len(directions)] + - uturn의 각 행을 순회하면서 아래 과정을 반복함 + - match5에서 parent_id에 해당하는 행들을 가져옴(cmatch). + - condition 별로 진입방향, 진출방향A, 진출방향B 정함. + - 상술한 directions를 활용하여 정함. + - (진입방향, 진출방향A, 진출방향B)을 고려하여 (현시, 링) 별로 진입엣지id, 진출엣지id를 정함. + - ex) cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + - 순회하면서 만든 cmatch를 cmatchs라는 리스트에 저장함. + + 연동교차로 : + - 연동교차로의 경우 coord에 (현시, 링)별 진입엣지ID, 진출엣지ID가 명시되어 있음. + - 'inc_dir', 'out_dir', 'inc_angle','out_angle'와 같은 열들은 np.nan을 지정해놓음. + - 이 열들은, 사실상 다음 스텝부터는 사용되지 않는 열들이기 때문에 np.nan으로 지정해놓아도 문제없음. + + match6 : + - 이렇게 얻은 match5, cmatchs, coord를 모두 pd.concat하여 match6을 얻어냄. + ''' + + self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no'])) + + child_ids = self.inter_node[self.inter_node.inter_type=='child'].node_id.unique() + ch2pa = {} # child to parent + for child_id in child_ids: + parent_no = self.inter_node[self.inter_node.node_id==child_id].inter_no.iloc[0] + sub_inter_node = self.inter_node[self.inter_node.inter_no==parent_no] + ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id + directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향 + + # 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여 + cmatches = [] + for _, row in self.uturn.iterrows(): + child_id = row.child_id + parent_id = row.parent_id + direction = row.direction + condition = row.condition + inc_edge_id = row.inc_edge + out_edge_id = row.out_edge + # match5에서 parent_id에 해당하는 행들을 가져옴 + cmatch = self.match5.copy()[self.match5.node_id==parent_id] # match dataframe for a child node + cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True) + cmatch['node_id'] = child_id + cmatch[['inc_edge', 'out_edge']] = np.nan + + # condition 별로 inc_dire, out_dire_A, out_dire_B를 정함 + ind = directions.index(direction) + if condition == "좌회전시": + inc_dire = direction + out_dire_A = out_dire_B = directions[(ind + 2) % len(directions)] + elif condition == "직진시": + inc_dire = direction + out_dire_A = out_dire_B = directions[(ind + 4) % len(directions)] + elif condition == "보행신호시": + inc_dire = directions[(ind + 2) % len(directions)] + out_dire_A = directions[(ind - 2) % len(directions)] + out_dire_B = directions[(ind - 2) % len(directions)] + + # (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함 + if condition == '보행신호시': + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 이동류번호가 17(보행신호)이면서 유턴노드방향으로 가는 신호가 없으면 (inc_edge_id, out_edge_id)를 부여한다. + cmatch.loc[(cmatch.move_no==17) & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + else: # '직진시', '좌회전시' + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 유턴신호의 이동류번호를 19로 부여한다. + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), 'move_no'] = 19 + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), 'move_no'] = 19 + cmatches.append(cmatch) + + # 각 coordination node에 대하여 (inc_edge_id, out_edge_id) 부여 + self.coord['inter_no'] = self.coord['parent_id'].map(self.node2inter) + self.coord = self.coord.rename(columns={'child_id':'node_id'}) + self.coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan + self.coord['move_no'] = 20 + self.coord = self.coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']] + + # display(coord) + cmatches = pd.concat(cmatches) + self.match6 = pd.concat([self.match5, cmatches, self.coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type']) + self.match6.to_csv(os.path.join(self.path_root, 'Intermediates', 'match6.csv')) + + # 2-1-7 + def make_matching(self): + ''' + 이동류 매칭 : 각 교차로에 대하여, 가능한 모든 이동류 (1~18, 21)에 대한 진입·진출엣지ID를 지정한다. + 모든 이동류에 대해 지정하므로, 시차제시 이전과 다른 이동류가 등장하더라도 항상 진입·진출 엣지 ID를 지정할 수 있다. + - matching의 컬럼 : inter_no, move_no, inc_dir, out_dir, inc_edge, out_edge, node_id + + 설명 : + - 필요한 리스트, 딕셔너리 등을 정의 + (1) 가능한 (진입방향, 진출방향) 목록 [리스트] + (2) 각 교차로별 방향 목록 : pdires (possible directions) [딕셔너리] + (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) [딕셔너리] + (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) [딕셔너리] + (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) [딕셔너리] + - matching은 빈 리스트로 지정. + - 모든 노드id에 대하여 다음 과정을 반복 + - 해당 노드id에 대한 모든 가능한 (진입방향, 진출방향)에 대하여 다음 과정을 반복 + - (노드id, 진입방향)으로부터 진입엣지id를 얻어냄. 마찬가지로 진출엣지id도 얻어냄 + - 얻어낸 정보를 바탕으로 한 행(new_row)을 만들고 이것을 matching에 append + ''' + + self.match7 = self.match6.copy() + self.match7 = self.match7[['inter_no', 'move_no', 'inc_dir', 'out_dir', 'inc_edge', 'out_edge', 'node_id']] + + parent_ids = sorted(self.inter_node[self.inter_node.inter_type=='parent'].node_id.unique()) + child_ids = sorted(self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()) + + # (1) 가능한 (진입방향, 진출방향) 목록 + flows = self.nema.dropna().apply(lambda row: (row['inc_dir'], row['out_dir']), axis=1).tolist() + # (2) 각 교차로별 방향 목록 : pdires (possible directions) + pdires = {} + for node_id in parent_ids: + dires = self.match7[self.match7.node_id == node_id][['inc_dir','out_dir']].values.flatten() + dires = {dire for dire in dires if type(dire)==str} + pdires[node_id] = dires + # (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) + inc2id = {} + for node_id in parent_ids: + for inc_dir in pdires[node_id]: + df = self.match7[(self.match7.node_id==node_id) & (self.match7.inc_dir==inc_dir)] + inc2id[(node_id, inc_dir)] = df.inc_edge.iloc[0] + # (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) + out2id = {} + for node_id in parent_ids: + for out_dir in pdires[node_id]: + df = self.match7[(self.match7.node_id==node_id) & (self.match7.out_dir==out_dir)] + out2id[(node_id, out_dir)] = df.out_edge.iloc[0] + # (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) + pflow = {} + for node_id in parent_ids: + pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])] + # (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching + # node2inter = dict(zip(self.match7['node_id'], self.match7['inter_no'])) + dires_right = ['북', '서', '남', '동', '북'] # ex (북, 서), (서, 남) 등은 우회전 flow + self.matching = [] + for node_id in parent_ids: + inter_no = self.node2inter[node_id] + # 좌회전과 직진(1 ~ 16) + for (inc_dir, out_dir) in pflow[node_id]: + move_no = self.nema[(self.nema.inc_dir==inc_dir) & (self.nema.out_dir==out_dir)].move_no.iloc[0] + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + self.matching.append(new_row) + # 보행신호(17), 전적색(18) + new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18], + 'inc_dir':[None]*2, 'out_dir':[None]*2, + 'inc_edge':[None]*2, 'out_edge':[None]*2, 'node_id':[node_id]*2}) + self.matching.append(new_row) + # 신호우회전(21) + for d in range(len(dires_right)-1): + inc_dir = dires_right[d] + out_dir = dires_right[d+1] + if {inc_dir, out_dir}.issubset(pdires[node_id]): + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + self.matching.append(new_row) + self.matching.append(self.match7[self.match7.node_id.isin(child_ids)]) + self.matching = pd.concat(self.matching) + self.matching = self.matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True) + self.matching['move_no'] = self.matching['move_no'].astype(int) + self.matching.to_csv(os.path.join(self.path_root, 'Intermediates', 'matching.csv')) + + # 2-2 + def get_movements(self): + movements_path = os.path.join(self.path_root, 'Intermediates', 'movement') + movements_list = [pd.read_csv(os.path.join(movements_path, file), index_col=0) for file in tqdm(os.listdir(movements_path), desc='이동류정보 불러오는 중 : movements')] + movements = pd.concat(movements_list) + movements = movements.drop(columns=['start_unix']) + movements = movements.drop_duplicates() + movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B']) + movements = movements.reset_index(drop=True) + movements.to_csv(os.path.join(self.path_root, 'Intermediates', 'movements.csv')) + return movements + + # 2-3 node2num_cycles : A dictionary that maps a node_id to the number of cycles + def get_node2num_cycles(self): + # node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no'])) + self.node_ids = sorted(self.inter_node.node_id.unique()) + + Aplan = self.plan.copy()[['inter_no'] + [f'dura_A{j}' for j in range(1,9)] + ['cycle']] + grouped = Aplan.groupby('inter_no') + df = grouped.agg({'cycle': 'min'}).reset_index() + df = df.rename(columns={'cycle': 'min_cycle'}) + df['num_cycle'] = 300 // df['min_cycle'] + 2 + inter2num_cycles = dict(zip(df['inter_no'], df['num_cycle'])) + node2numcycles = {node_id : inter2num_cycles[self.node2inter[node_id]] for node_id in self.node_ids} + with open(os.path.join('Intermediates','node2numcycles.json'), 'w') as file: + json.dump(node2numcycles, file, indent=4) + return node2numcycles + + # 3. 이슈사항 저장 + def write_issues(self): + path_issues = os.path.join(self.path_root, "Results", "issues_intermediates.txt") + with open(path_issues, "w", encoding="utf-8") as file: + for item in self.issues: + file.write(item + "\n") + if self.issues: + print("데이터 처리 중 발생한 특이사항은 다음과 같습니다. :") + for review in self.issues: + print(review) + + def main(self): + # 1. 데이터 불러오기 + self.load_data() + # 2. 중간산출물 만들기 + self.get_intermediates() + # 3. 이슈사항 저장 + self.write_issues() + +if __name__ == '__main__': + self = DailyPreprocess() + self.main() \ No newline at end of file diff --git a/Script/get_signals.py b/Script/get_signals.py new file mode 100644 index 000000000..df8f1c91f --- /dev/null +++ b/Script/get_signals.py @@ -0,0 +1,587 @@ +import pandas as pd +import numpy as np +import os, sys +import json +from tqdm import tqdm +from datetime import datetime + +class SignalGenerator(): + def __init__(self): + self.path_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + self.issues = [] + + # 1. 데이터 불러오기 + def load_data(self): + self.load_networks() + self.load_tables() + self.check_networks() + self.check_tables() + print('1. 모든 데이터가 로드되었습니다.') + + # 1-1. 네트워크 불러오기 + def load_networks(self): + self.net = sumolib.net.readNet(os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml')) + print("1-1. 네트워크가 로드되었습니다.") + + # 1-2. 테이블 불러오기 + def load_tables(self): + # 모든 컬럼에 대하여 데이터타입 지정 + loading_dtype = { + 'inter_no':'int', 'start_hour':'int', 'start_minute':'int', 'cycle':'int','offset':'int', + 'node_id':'str', 'inter_type':'str', 'parent_id':'str','child_id':'str', + 'direction':'str', 'condition':'str', 'inc_edge':'str', 'out_edge':'str', + 'end_unix':'int', 'inter_name':'str', 'inter_lat':'float', 'inter_lon':'float', + 'group_no':'int', 'main_phase_no':'int', 'phase_no':'int','ring_type':'str' + } + for alph in ['A', 'B']: + for j in range(1,9): + loading_dtype[f'angle_{alph}{j}'] = 'str' + loading_dtype[f'dura_{alph}{j}'] = 'int' + + self.path_table = os.path.join(self.path_root, 'Data', 'tables') + + self.inter_info = pd.read_csv(os.path.join(self.path_table, 'inter_info.csv'), dtype=loading_dtype) + self.angle = pd.read_csv(os.path.join(self.path_table, 'angle.csv'), dtype=loading_dtype) + self.plan = pd.read_csv(os.path.join(self.path_table, 'plan.csv'), dtype=loading_dtype) + self.inter_node = pd.read_csv(os.path.join(self.path_table, 'inter_node.csv'), dtype=loading_dtype) + self.uturn = pd.read_csv(os.path.join(self.path_table, 'child_uturn.csv'), dtype=loading_dtype) + self.coord = pd.read_csv(os.path.join(self.path_table, 'child_coord.csv'), dtype=loading_dtype) + self.nema = pd.read_csv(os.path.join(self.path_table, 'nema.csv'), encoding='cp949', dtype=loading_dtype) + print("1-2. 테이블들이 로드되었습니다.") + + # 1-3. 테이블 불러오기 + def check_networks(self): + # https://sumo.dlr.de/docs/Netedit/neteditUsageExamples.html#simplify_tls_program_state_after_changing_connections + if 'SUMO_HOME' in os.environ: + tools = os.path.join(os.environ['SUMO_HOME'], 'tools') + if tools not in sys.path: + sys.path.append(tools) + else: + raise EnvironmentError("please declare environment variable 'SUMO_HOME'") + traci.start([sumolib.checkBinary('sumo'), "-n", os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml')]) + nodes = [node for node in self.net.getNodes() if node.getType()=='traffic_light'] + for node in nodes: + node_id = node.getID() + from_xml = len([c for c in node.getConnections() if c.getTLLinkIndex() >= 0]) + from_traci = len(traci.trafficlight.getRedYellowGreenState(node_id)) + if from_xml != from_traci: + sub = {'id': node_id, 'type': 'node', 'note': '유효하지 않은 연결이있음. netedit에서 clean states 필요.'} + self.issues.append(sub) + traci.close() + print("1-3. 네트워크의 모든 clean state requirement들을 체크했습니다.") + + # 1-4. 테이블의 무결성 검사 + def check_tables(self): + self.check_inter_info() + self.check_angle() + self.check_plan() + print("1-4. 모든 테이블들의 무결성을 검사했고 이상 없습니다.") + pass + + # 1-4-1. 교차로정보(inter_info) 검사 + def check_inter_info(self): + # 1-4-1-1. inter_lat, inter_lon 적절성 검사 + # self.inter_info.loc[0, 'inter_lat'] = 38.0 # 에러 발생을 위한 코드 + self.max_lon, self.min_lon = 127.207888, 127.012492 + self.max_lat, self.min_lat = 37.480693, 37.337112 + for _, row in self.inter_info.iterrows(): + latbool = self.min_lat <= row['inter_lat'] <= self.max_lat + lonbool = self.min_lon <= row['inter_lon'] <= self.max_lon + if not(latbool and lonbool): + msg = f"1-4-1-1. 위도 또는 경도가 범위를 벗어난 교차로가 있습니다: inter_no : {row['inter_no']}" + self.issues.append(msg) + # 교차로목록 정의 + self.inter_nos = sorted(self.inter_info.inter_no.unique()) + + # 1-4-2. 방위각정보(inter_info) 검사 + def check_angle(self): + # 1-4-2-1. inter_no 검사 + # self.angle.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드 + missing_inter_nos = set(self.angle.inter_no) - set(self.inter_nos) + if missing_inter_nos: + msg = f"1-4-2-1. angle의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}" + self.issues.append(msg) + + # 1-4-3. 신호계획(plan) 검사 + def check_plan(self): + # 1-4-3-1. inter_no 검사 + # self.plan.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드 + missing_inter_nos = set(self.plan.inter_no) - set(self.inter_nos) + if missing_inter_nos: + msg = f"1-4-3-1. plan의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}" + self.issues.append(msg) + + # 1-4-3-2. 시작시각 검사 + # self.plan.loc[0, 'start_hour'] = 27 # 에러 발생을 위한 코드 + for _, row in self.plan.iterrows(): + start_hour = row.start_hour + start_minute = row.start_minute + if not (0 <= start_hour <= 23) or not (0 <= start_minute <= 59): + msg = f"1-4-3-2. plan에 잘못된 형식의 start_time이 존재합니다: {start_hour, start_minute}" + self.issues.append(msg) + + # 1-4-3-3. 현시시간 검사 + # self.plan.loc[0, 'dura_A1'] = -2 # 에러 발생을 위한 코드 + durations = self.plan[[f'dura_{alph}{j}' for alph in ['A','B'] for j in range(1, 9)]] + valid_indices = ((durations >= 0) & (durations <= 200)).all(axis=1) + invalid_inter_nos = sorted(self.plan[~ valid_indices].inter_no.unique()) + if invalid_inter_nos: + msg = f"1-4-3-3. 음수이거나 200보다 큰 현시시간이 존재합니다. : {invalid_inter_nos}" + + # 1-4-3-4. 주기 일관성 검사 + # self.plan.loc[0, 'cycle'] = 50 # 에러 발생을 위한 코드 + inconsistent_cycle = self.plan.groupby(['inter_no', 'start_hour', 'start_minute'])['cycle'].nunique().gt(1) + if inconsistent_cycle.any(): + inc_inter_no, start_hour, start_minute = inconsistent_cycle[inconsistent_cycle].index[0] + msg = f"1-4-3-4. inter_no:{inc_inter_no}, start_hour:{start_minute}, start_hour:{start_minute}일 때, cycle이 유일하게 결정되지 않습니다." + self.issues.append(msg) + + # 1-4-3-5. 현시시간 / 주기 검사 + # self.plan.loc[0, 'duration'] = 10 # 에러 발생을 위한 코드 + right_duration = True + for (inter_no, start_hour, start_minute), group in self.plan.groupby(['inter_no', 'start_hour', 'start_minute']): + A_sum = group[[f'dura_A{j}' for j in range(1, 9)]].iloc[0].sum() + B_sum = group[[f'dura_B{j}' for j in range(1, 9)]].iloc[0].sum() + # A_sum = group[group['ring_type']=='A']['duration'].sum() + # B_sum = group[group['ring_type']=='B']['duration'].sum() + cycle = group['cycle'].unique()[0] + if not (A_sum == B_sum == cycle): + right_duration = False + inc_inter_no = inter_no + if not right_duration: + msg = f"1-4-4-5. inter_no:{inc_inter_no}, A링현시시간의 합과 B링현시시간의 합이 일치하지 않거나, 현시시간의 합과 주기가 일치하지 않습니다." + self.issues.append(msg) + + # 2. 중간산출물 만들기 + def get_intermediates(self): + self.get_matches() + # self.get_movements() + self.get_node2num_cycles() + + # 2-1 매칭테이블들 생성 + def get_matches(self): + self.make_match1() + self.make_match2() + self.make_match3() + self.make_match4() + self.make_match5() + self.make_match6() + self.make_matching() + + # 2-1-1 + def make_match1(self): + ''' + 신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다. + '../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다. + + return : 통합된 이동류정보 + - 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보 + + match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다. + ''' + # [이동류번호] 불러오기 (약 1분의 소요시간) + path_move = os.path.join(self.path_root, 'Data', 'tables', 'move') + csv_moves = os.listdir(path_move) + moves = [pd.read_csv(os.path.join(path_move, csv_move), index_col=0) for csv_move in tqdm(csv_moves, desc='이동류정보 불러오는 중 : match1')] + self.match1 = pd.concat(moves).drop_duplicates().sort_values(by=['inter_no','phas_A','phas_B']).reset_index(drop=True) + self.match1.to_csv(os.path.join(self.path_root, 'Intermediates', 'match1.csv')) + + # 2-1-2 + def make_match2(self): + ''' + match1을 계층화함. + - match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + ''' + # 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no') + matchA = self.match1[['inter_no', 'phas_A', 'move_A']].copy() + matchA.columns = ['inter_no', 'phase_no', 'move_no'] + matchA['ring_type'] = 'A' + matchB = self.match1[['inter_no', 'phas_B', 'move_B']].copy() + matchB.columns = ['inter_no', 'phase_no', 'move_no'] + matchB['ring_type'] = 'B' + self.match2 = pd.concat([matchA, matchB]).drop_duplicates() + self.match2 = self.match2[['inter_no', 'phase_no', 'ring_type', 'move_no']] + self.match2 = self.match2.sort_values(by=list(self.match2.columns)) + + # 2-1-3 + def make_match3(self): + ''' + 각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함. + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + + nema : + - 컬럼 : move_no, inc_dir, out_dir + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + # nema 정보 불러오기 및 병합 + self.match3 = pd.merge(self.match2, self.nema, how='left', on='move_no').drop_duplicates() + + # 2-1-4 + def make_match4(self): + ''' + 방위각 정보를 매칭시켜 추가함. + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + + angle_original : + - 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8) + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + + # 계층화 + angles = [] + for i, row in self.angle.iterrows(): + angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] + new = pd.DataFrame({'inter_no':[row.inter_no] * 16, 'phase_no':list(range(1, 9))*2, 'ring_type':['A'] * 8 + ['B'] * 8, 'angle_code':angle_codes.to_list()}) + angles.append(new) + angles = pd.concat(angles) + angles = angles.dropna().reset_index(drop=True) + + # 병합 + six_chars = angles.angle_code.apply(lambda x:len(x)==6) + angles.loc[six_chars,'inc_angle'] = angles.angle_code.apply(lambda x:x[:3]) + angles.loc[six_chars,'out_angle'] = angles.angle_code.apply(lambda x:x[3:]) + angles = angles.drop('angle_code', axis=1) + self.match4 = pd.merge(self.match3, angles, how='left', left_on=['inter_no', 'phase_no', 'ring_type'], + right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates() + + # 2-1-5 + def make_match5(self): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로). + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + - match5의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) net + - 성남시 정자동 부근의 샘플 네트워크 + (2) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (3) inter_info + - 교차로 정보. 여기에서는 위도와 경도가 쓰임. + - 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no + + 진입엣지id, 진출엣지id를 얻는 과정 : + - match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함. + * 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음 + - 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장. + * inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge + - inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄. + - 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄. + - 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함. + ''' + + # parent node만 가져옴. + inter_node1 = self.inter_node[self.inter_node.inter_type == 'parent'].drop('inter_type', axis=1) + inter_info1 = self.inter_info[['inter_no', 'inter_lat', 'inter_lon']] + inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'], + right_on=['inter_no']).drop_duplicates() + + self.inter2node = dict(zip(inter['inter_no'], inter['node_id'])) + + self.match5 = self.match4.copy() + # 진입진출ID 매칭 + for index, row in self.match5.iterrows(): + node_id = self.inter2node[row.inter_no] + node = self.net.getNode(node_id) + # 교차로의 모든 (from / to) edges + inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges + out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges + # 교차로의 모든 (from / to) directions + inc_dirs = [] + for inc_edge in inc_edges: + start = inc_edge.getShape()[-2] + end = inc_edge.getShape()[-1] + inc_dir = np.array(end) - np.array(start) + inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5 + inc_dirs.append(inc_dir) + out_dirs = [] + for out_edge in out_edges: + start = out_edge.getShape()[0] + end = out_edge.getShape()[1] + out_dir = np.array(end) - np.array(start) + out_dir = out_dir / (out_dir ** 2).sum() ** 0.5 + out_dirs.append(out_dir) + # 진입각, 진출각 불러오기 + if not pd.isna(row.inc_angle): + inc_angle = int(row.inc_angle) + out_angle = int(row.out_angle) + # 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환 + inc_angle = (-90 - inc_angle) % 360 + inc_angle = inc_angle * np.pi / 180. + inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)]) + out_angle = (90 - out_angle) % 360 + out_angle = out_angle * np.pi / 180. + out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)]) + # 매칭 엣지 반환 + inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax() + out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax() + inc_edge_id = inc_edges[inc_index].getID() + out_edge_id = out_edges[out_index].getID() + self.match5.at[index, 'inc_edge'] = inc_edge_id + self.match5.at[index, 'out_edge'] = out_edge_id + self.match5['node_id'] = self.match5['inter_no'].map(self.inter2node) + self.match5 = self.match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True) + + # 2-1-6 + def make_match6(self): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로). + - match6의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (2) uturn (유턴정보) + - 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 유턴교차로id + - direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향) + - condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나 + - inc_edge, out_edge : 유턴에 대한 진입진출엣지 + (3) coord (연동교차로정보) + - 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 연동교차로id + - 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지 + + 설명 : + - match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음. + 여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함. + 유턴교차로 : + - directions를 정북기준 시계방향의 8방위로 정함. + - 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음. + - 예) 진입방향(direction)이 '북'일 때, + - 직진 : (북, 남) + * 남 : directions[(ind + 4) % len(directions)] + - 좌회전 : (북, 동) + * 동 : directions[(ind + 2) % len(directions)] + - 보행 : (서, 동) + * 서 : directions[(ind - 2) % len(directions)] + - uturn의 각 행을 순회하면서 아래 과정을 반복함 + - match5에서 parent_id에 해당하는 행들을 가져옴(cmatch). + - condition 별로 진입방향, 진출방향A, 진출방향B 정함. + - 상술한 directions를 활용하여 정함. + - (진입방향, 진출방향A, 진출방향B)을 고려하여 (현시, 링) 별로 진입엣지id, 진출엣지id를 정함. + - ex) cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + - 순회하면서 만든 cmatch를 cmatchs라는 리스트에 저장함. + + 연동교차로 : + - 연동교차로의 경우 coord에 (현시, 링)별 진입엣지ID, 진출엣지ID가 명시되어 있음. + - 'inc_dir', 'out_dir', 'inc_angle','out_angle'와 같은 열들은 np.nan을 지정해놓음. + - 이 열들은, 사실상 다음 스텝부터는 사용되지 않는 열들이기 때문에 np.nan으로 지정해놓아도 문제없음. + + match6 : + - 이렇게 얻은 match5, cmatchs, coord를 모두 pd.concat하여 match6을 얻어냄. + ''' + + self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no'])) + + child_ids = self.inter_node[self.inter_node.inter_type=='child'].node_id.unique() + ch2pa = {} # child to parent + for child_id in child_ids: + parent_no = self.inter_node[self.inter_node.node_id==child_id].inter_no.iloc[0] + sub_inter_node = self.inter_node[self.inter_node.inter_no==parent_no] + ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id + directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향 + + # 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여 + cmatches = [] + for _, row in self.uturn.iterrows(): + child_id = row.child_id + parent_id = row.parent_id + direction = row.direction + condition = row.condition + inc_edge_id = row.inc_edge + out_edge_id = row.out_edge + # match5에서 parent_id에 해당하는 행들을 가져옴 + cmatch = self.match5.copy()[self.match5.node_id==parent_id] # match dataframe for a child node + cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True) + cmatch['node_id'] = child_id + cmatch[['inc_edge', 'out_edge']] = np.nan + + # condition 별로 inc_dire, out_dire_A, out_dire_B를 정함 + ind = directions.index(direction) + if condition == "좌회전시": + inc_dire = direction + out_dire_A = out_dire_B = directions[(ind + 2) % len(directions)] + elif condition == "직진시": + inc_dire = direction + out_dire_A = out_dire_B = directions[(ind + 4) % len(directions)] + elif condition == "보행신호시": + inc_dire = directions[(ind + 2) % len(directions)] + out_dire_A = directions[(ind - 2) % len(directions)] + out_dire_B = directions[(ind - 2) % len(directions)] + + # (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함 + if condition == '보행신호시': + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 이동류번호가 17(보행신호)이면서 유턴노드방향으로 가는 신호가 없으면 (inc_edge_id, out_edge_id)를 부여한다. + cmatch.loc[(cmatch.move_no==17) & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + else: # '직진시', '좌회전시' + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 유턴신호의 이동류번호를 19로 부여한다. + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), 'move_no'] = 19 + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), 'move_no'] = 19 + cmatches.append(cmatch) + + # 각 coordination node에 대하여 (inc_edge_id, out_edge_id) 부여 + self.coord['inter_no'] = self.coord['parent_id'].map(self.node2inter) + self.coord = self.coord.rename(columns={'child_id':'node_id'}) + self.coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan + self.coord['move_no'] = 20 + self.coord = self.coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']] + + # display(coord) + cmatches = pd.concat(cmatches) + self.match6 = pd.concat([self.match5, cmatches, self.coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type']) + self.match6.to_csv(os.path.join(self.path_root, 'Intermediates', 'match6.csv')) + + # 2-1-7 + def make_matching(self): + ''' + 이동류 매칭 : 각 교차로에 대하여, 가능한 모든 이동류 (1~18, 21)에 대한 진입·진출엣지ID를 지정한다. + 모든 이동류에 대해 지정하므로, 시차제시 이전과 다른 이동류가 등장하더라도 항상 진입·진출 엣지 ID를 지정할 수 있다. + - matching의 컬럼 : inter_no, move_no, inc_dir, out_dir, inc_edge, out_edge, node_id + + 설명 : + - 필요한 리스트, 딕셔너리 등을 정의 + (1) 가능한 (진입방향, 진출방향) 목록 [리스트] + (2) 각 교차로별 방향 목록 : pdires (possible directions) [딕셔너리] + (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) [딕셔너리] + (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) [딕셔너리] + (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) [딕셔너리] + - matching은 빈 리스트로 지정. + - 모든 노드id에 대하여 다음 과정을 반복 + - 해당 노드id에 대한 모든 가능한 (진입방향, 진출방향)에 대하여 다음 과정을 반복 + - (노드id, 진입방향)으로부터 진입엣지id를 얻어냄. 마찬가지로 진출엣지id도 얻어냄 + - 얻어낸 정보를 바탕으로 한 행(new_row)을 만들고 이것을 matching에 append + ''' + + self.match7 = self.match6.copy() + self.match7 = self.match7[['inter_no', 'move_no', 'inc_dir', 'out_dir', 'inc_edge', 'out_edge', 'node_id']] + + parent_ids = sorted(self.inter_node[self.inter_node.inter_type=='parent'].node_id.unique()) + child_ids = sorted(self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()) + + # (1) 가능한 (진입방향, 진출방향) 목록 + flows = self.nema.dropna().apply(lambda row: (row['inc_dir'], row['out_dir']), axis=1).tolist() + # (2) 각 교차로별 방향 목록 : pdires (possible directions) + pdires = {} + for node_id in parent_ids: + dires = self.match7[self.match7.node_id == node_id][['inc_dir','out_dir']].values.flatten() + dires = {dire for dire in dires if type(dire)==str} + pdires[node_id] = dires + # (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) + inc2id = {} + for node_id in parent_ids: + for inc_dir in pdires[node_id]: + df = self.match7[(self.match7.node_id==node_id) & (self.match7.inc_dir==inc_dir)] + inc2id[(node_id, inc_dir)] = df.inc_edge.iloc[0] + # (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) + out2id = {} + for node_id in parent_ids: + for out_dir in pdires[node_id]: + df = self.match7[(self.match7.node_id==node_id) & (self.match7.out_dir==out_dir)] + out2id[(node_id, out_dir)] = df.out_edge.iloc[0] + # (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) + pflow = {} + for node_id in parent_ids: + pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])] + # (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching + # node2inter = dict(zip(self.match7['node_id'], self.match7['inter_no'])) + dires_right = ['북', '서', '남', '동', '북'] # ex (북, 서), (서, 남) 등은 우회전 flow + self.matching = [] + for node_id in parent_ids: + inter_no = self.node2inter[node_id] + # 좌회전과 직진(1 ~ 16) + for (inc_dir, out_dir) in pflow[node_id]: + move_no = self.nema[(self.nema.inc_dir==inc_dir) & (self.nema.out_dir==out_dir)].move_no.iloc[0] + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + self.matching.append(new_row) + # 보행신호(17), 전적색(18) + new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18], + 'inc_dir':[None]*2, 'out_dir':[None]*2, + 'inc_edge':[None]*2, 'out_edge':[None]*2, 'node_id':[node_id]*2}) + self.matching.append(new_row) + # 신호우회전(21) + for d in range(len(dires_right)-1): + inc_dir = dires_right[d] + out_dir = dires_right[d+1] + if {inc_dir, out_dir}.issubset(pdires[node_id]): + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + self.matching.append(new_row) + self.matching.append(self.match7[self.match7.node_id.isin(child_ids)]) + self.matching = pd.concat(self.matching) + self.matching = self.matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True) + self.matching['move_no'] = self.matching['move_no'].astype(int) + self.matching.to_csv(os.path.join(self.path_root, 'Intermediates', 'matching.csv')) + + # 2-2 + def get_movements(self): + movements_path = os.path.join(self.path_root, 'Intermediates', 'movement') + movements_list = [pd.read_csv(os.path.join(movements_path, file), index_col=0) for file in tqdm(os.listdir(movements_path), desc='이동류정보 불러오는 중 : movements')] + movements = pd.concat(movements_list) + movements = movements.drop(columns=['start_unix']) + movements = movements.drop_duplicates() + movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B']) + movements = movements.reset_index(drop=True) + movements.to_csv(os.path.join(self.path_root, 'Intermediates', 'movements.csv')) + return movements + + # 2-3 node2num_cycles : A dictionary that maps a node_id to the number of cycles + def get_node2num_cycles(self): + # node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no'])) + self.node_ids = sorted(self.inter_node.node_id.unique()) + + Aplan = self.plan.copy()[['inter_no'] + [f'dura_A{j}' for j in range(1,9)] + ['cycle']] + grouped = Aplan.groupby('inter_no') + df = grouped.agg({'cycle': 'min'}).reset_index() + df = df.rename(columns={'cycle': 'min_cycle'}) + df['num_cycle'] = 300 // df['min_cycle'] + 2 + inter2num_cycles = dict(zip(df['inter_no'], df['num_cycle'])) + node2numcycles = {node_id : inter2num_cycles[self.node2inter[node_id]] for node_id in self.node_ids} + with open(os.path.join('Intermediates','node2numcycles.json'), 'w') as file: + json.dump(node2numcycles, file, indent=4) + return node2numcycles + + # 3. 이슈사항 저장 + def write_issues(self): + path_issues = os.path.join(self.path_root, "Results", "issues_intermediates.txt") + with open(path_issues, "w", encoding="utf-8") as file: + for item in self.issues: + file.write(item + "\n") + if self.issues: + print("데이터 처리 중 발생한 특이사항은 다음과 같습니다. :") + for review in self.issues: + print(review) + + def main(self): + # 1. 데이터 불러오기 + self.load_data() + # 2. 중간산출물 만들기 + self.get_intermediates() + # 3. 이슈사항 저장 + self.write_issues() + +if __name__ == '__main__': + self = DailyPreprocess() + self.main() \ No newline at end of file diff --git a/Script/preprocess.ipynb b/Script/preprocess.ipynb index 5a6019f91..b22f4040b 100644 --- a/Script/preprocess.ipynb +++ b/Script/preprocess.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 103, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -234,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -360,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -457,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -470,6 +470,244 @@ "matching = make_matching(match6)" ] }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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....................................
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52103B4북남000180NaNNaNu60
62104A8남북180000NaNNaNu60
72104B3남서180270NaNNaNu60
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116 rows × 11 columns

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" + ], + "text/plain": [ + " inter_no phase_no ring_type move_no inc_dir out_dir inc_angle out_angle \\\n", + "0 175 1 A 8 남 북 179 004 \n", + "1 175 1 B 4 북 남 003 176 \n", + "2 175 2 A 7 북 동 001 095 \n", + "3 175 2 B 3 남 서 179 270 \n", + "4 175 3 A 6 동 서 090 270 \n", + ".. ... ... ... ... ... ... ... ... \n", + "3 210 2 B 2 서 동 270 090 \n", + "4 210 3 A 7 북 동 359 090 \n", + "5 210 3 B 4 북 남 000 180 \n", + "6 210 4 A 8 남 북 180 000 \n", + "7 210 4 B 3 남 서 180 270 \n", + "\n", + " inc_edge out_edge node_id \n", + "0 -571542797_02 571500487_01 i0 \n", + "1 -571500487_01 571542797_02 i0 \n", + "2 -571500487_01 571545870_01 i0 \n", + "3 -571542797_02 571510153_01 i0 \n", + "4 571545870_02 571510153_01 i0 \n", + ".. ... ... ... \n", + "3 NaN NaN u60 \n", + "4 NaN NaN u60 \n", + "5 NaN NaN u60 \n", + "6 NaN NaN u60 \n", + "7 NaN NaN u60 \n", + "\n", + "[116 rows x 11 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "match6" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -479,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -492,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -550,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -568,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -616,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -645,7 +883,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -667,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -689,7 +927,7 @@ " row1['end_unix'] = program_start\n", " row2['end_unix'] = program_start + cycle\n", " rhistory = pd.concat([rhistory, row1, row2]).reset_index(drop=True)\n", - "\n", + " # present_time + adder 의 시각에 한 주기의 신호 추가\n", " for inter_no in set(whole_inter_nos):\n", " program_start, prow = load_prow(timetable, inter_no, present_time)\n", " cycle = prow.cycle.iloc[0]\n", @@ -698,7 +936,6 @@ " row3['end_unix'] = present_time + adder\n", " rhistory = pd.concat([rhistory, row3]).reset_index(drop=True)\n", "\n", - "\n", " # 2. 시작 유닉스 타임컬럼 생성 후 종류 유닉스 타임에서 현시별 현시기간 컬럼의 합을 뺀 값으로 입력\n", " # - 현시시간의 합을 뺀 시간의 +- 10초 이내에 이전 주기정보가 존재하면 그 유닉스 시간을 시작 유닉스시간 값으로 하고, 존재하지 않으면 현시시간의 합을 뺀 유닉스 시간을 시작 유닉스 시간으로 지정\n", " for i, row in rhistory.iterrows():\n", @@ -727,7 +964,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -834,7 +1071,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -872,7 +1109,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -891,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -925,11 +1162,16 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "def make_histid(present_time, movedur, inter2node):\n", + "def make_histid(hrhists, movement_updated, present_time, inter2node):\n", + " # movements and durations\n", + " movedur = pd.merge(hrhists, movement_updated, how='inner', on=['inter_no', 'start_unix', 'phas_A', 'phas_B'])\n", + " movedur = movedur.sort_values(by=['start_unix', 'inter_no', 'phas_A','phas_B'])\n", + " movedur = movedur[['inter_no', 'start_unix', 'phas_A', 'phas_B', 'move_A', 'move_B', 'duration']]\n", + "\n", " # 이동류 매칭 테이블에서 진입id, 진출id를 가져와서 붙임.\n", " for i, row in movedur.iterrows():\n", " inter_no = row.inter_no\n", @@ -938,7 +1180,7 @@ " move_A = row.move_A\n", " if move_A in [17, 18]:\n", " inc_edge_A = np.nan\n", - " out_edge_A = np.nan\n", + " outhedge_A = np.nan\n", " else:\n", " match_A = matching[(matching.inter_no == inter_no) & (matching.move_no == move_A)].iloc[0]\n", " inc_edge_A = match_A.inc_edge\n", @@ -961,19 +1203,15 @@ " histid = movedur.copy() # history with edge ids (incoming and outgoing edge ids)\n", " histid['node_id'] = histid['inter_no'].map(inter2node)\n", " histid = histid[['inter_no', 'node_id', 'start_unix', 'phas_A', 'phas_B', 'duration', 'inc_edge_A', 'out_edge_A', 'inc_edge_B', 'out_edge_B']]\n", - " histid_start = present_time - 1200\n", + " histid_start = present_time - 600\n", " histid = histid[histid.start_unix > histid_start]\n", " return histid\n", - "# movedur\n", - "movedur = pd.merge(movement_updated, hrhists, how='inner', on=['inter_no', 'start_unix', 'phas_A', 'phas_B']) # movements and durations\n", - "movedur = movedur.sort_values(by=['start_unix', 'inter_no', 'phas_A','phas_B'])\n", - "movedur = movedur[['inter_no', 'start_unix', 'phas_A', 'phas_B', 'move_A', 'move_B', 'duration']]\n", - "histid = make_histid(present_time, movedur, inter2node)" + "histid = make_histid(hrhists, movement_updated, present_time, inter2node)" ] }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1018,69 +1256,69 @@ " \n", " \n", " \n", - " 1621\n", - " 177\n", - " i2\n", - " 1704410710\n", + " 1768\n", + " 206\n", + " i7\n", + " 1704411320\n", " 1\n", " 1\n", - " 40\n", - " -571542809_01\n", - " 571542811_01\n", - " 571542811_02\n", - " 571542809_01\n", + " 44\n", + " -571511538_02\n", + " 571542073_02\n", + " 571542073_01\n", + " 571511538_02\n", " \n", " \n", - " 1622\n", - " 177\n", - " i2\n", - " 1704410710\n", + " 1769\n", + " 206\n", + " i7\n", + " 1704411320\n", " 2\n", " 2\n", - " 25\n", - " 571542811_02\n", - " 571542107_01\n", - " -571542809_01\n", - " 571542809_01\n", + " 44\n", + " NaN\n", + " 571542073_02\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 1623\n", - " 177\n", - " i2\n", - " 1704410710\n", + " 1770\n", + " 206\n", + " i7\n", + " 1704411320\n", " 3\n", " 3\n", - " 71\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 26\n", + " -571511538_02\n", + " 571542073_02\n", + " 571542073_01\n", + " 571511538_02\n", " \n", " \n", - " 1624\n", - " 177\n", - " i2\n", - " 1704410710\n", + " 1771\n", + " 206\n", + " i7\n", + " 1704411320\n", " 4\n", " 4\n", - " 34\n", - " -571542809_01\n", - " 571542811_01\n", - " 571542107_02\n", - " 571542809_01\n", + " 26\n", + " NaN\n", + " 571542073_02\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 876\n", - " 176\n", - " i1\n", - " 1704410720\n", + " 1772\n", + " 178\n", + " i3\n", + " 1704411350\n", " 1\n", " 1\n", - " 37\n", - " -571542810_01\n", - " -571542797_02.99\n", - " 571542797_02.99\n", - " 571542810_01\n", + " 38\n", + " 571540304_02\n", + " 571556450_01\n", + " 571556450_02\n", + " 571540304_01\n", " \n", " \n", " ...\n", @@ -1096,7 +1334,7 @@ " ...\n", " \n", " \n", - " 1671\n", + " 1978\n", " 201\n", " i8\n", " 1704412330\n", @@ -1109,7 +1347,7 @@ " 571500617_01\n", " \n", " \n", - " 1672\n", + " 1979\n", " 201\n", " i8\n", " 1704412330\n", @@ -1122,7 +1360,7 @@ " 571500569_01\n", " \n", " \n", - " 1673\n", + " 1980\n", " 201\n", " i8\n", " 1704412330\n", @@ -1135,7 +1373,7 @@ " 571500569_01\n", " \n", " \n", - " 1486\n", + " 1981\n", " 202\n", " i9\n", " 1704412340\n", @@ -1148,7 +1386,7 @@ " 571510152_01.65\n", " \n", " \n", - " 1487\n", + " 1982\n", " 202\n", " i9\n", " 1704412340\n", @@ -1156,46 +1394,46 @@ " 2\n", " 64\n", " NaN\n", - " NaN\n", + " -571510152_01\n", " NaN\n", " NaN\n", " \n", " \n", "\n", - "

343 rows × 10 columns

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215 rows × 10 columns

\n", "" ], "text/plain": [ " inter_no node_id start_unix phas_A phas_B duration inc_edge_A \\\n", - "1621 177 i2 1704410710 1 1 40 -571542809_01 \n", - "1622 177 i2 1704410710 2 2 25 571542811_02 \n", - "1623 177 i2 1704410710 3 3 71 NaN \n", - "1624 177 i2 1704410710 4 4 34 -571542809_01 \n", - "876 176 i1 1704410720 1 1 37 -571542810_01 \n", + "1768 206 i7 1704411320 1 1 44 -571511538_02 \n", + "1769 206 i7 1704411320 2 2 44 NaN \n", + "1770 206 i7 1704411320 3 3 26 -571511538_02 \n", + "1771 206 i7 1704411320 4 4 26 NaN \n", + "1772 178 i3 1704411350 1 1 38 571540304_02 \n", "... ... ... ... ... ... ... ... \n", - "1671 201 i8 1704412330 3 3 18 571500617_02 \n", - "1672 201 i8 1704412330 4 4 58 571500617_02 \n", - "1673 201 i8 1704412330 5 5 18 571500583_01 \n", - "1486 202 i9 1704412340 1 1 26 571510152_02 \n", - "1487 202 i9 1704412340 2 2 64 NaN \n", + "1978 201 i8 1704412330 3 3 18 571500617_02 \n", + "1979 201 i8 1704412330 4 4 58 571500617_02 \n", + "1980 201 i8 1704412330 5 5 18 571500583_01 \n", + "1981 202 i9 1704412340 1 1 26 571510152_02 \n", + "1982 202 i9 1704412340 2 2 64 NaN \n", "\n", - " out_edge_A inc_edge_B out_edge_B \n", - "1621 571542811_01 571542811_02 571542809_01 \n", - "1622 571542107_01 -571542809_01 571542809_01 \n", - "1623 NaN NaN NaN \n", - "1624 571542811_01 571542107_02 571542809_01 \n", - "876 -571542797_02.99 571542797_02.99 571542810_01 \n", - "... ... ... ... \n", - "1671 571500618_01 571500618_02 571500617_01 \n", - "1672 571500618_01 571500617_02 571500569_01 \n", - "1673 571500617_01 571500583_01 571500569_01 \n", - "1486 -571510152_01 571510152_01 571510152_01.65 \n", - "1487 NaN NaN NaN \n", + " out_edge_A inc_edge_B out_edge_B \n", + "1768 571542073_02 571542073_01 571511538_02 \n", + "1769 571542073_02 NaN NaN \n", + "1770 571542073_02 571542073_01 571511538_02 \n", + "1771 571542073_02 NaN NaN \n", + "1772 571556450_01 571556450_02 571540304_01 \n", + "... ... ... ... \n", + "1978 571500618_01 571500618_02 571500617_01 \n", + "1979 571500618_01 571500617_02 571500569_01 \n", + "1980 571500617_01 571500583_01 571500569_01 \n", + "1981 -571510152_01 571510152_01 571510152_01.65 \n", + "1982 -571510152_01 NaN NaN \n", "\n", - "[343 rows x 10 columns]" + "[215 rows x 10 columns]" ] }, - "execution_count": 124, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1275,7 +1513,7 @@ " movedur = movedur.sort_values(by=['start_unix', 'inter_no', 'phas_A','phas_B'])\n", " movedur = movedur[['inter_no', 'start_unix', 'phas_A', 'phas_B', 'move_A', 'move_B', 'duration']]\n", " # histid\n", - " histid = make_histid(present_time, movedur, inter2node)\n", + " histid = make_histid(hrhists, movement_updated, present_time, inter2node)\n", " histid.to_csv(f'../Intermediates/histid/histid_{fmins[m]}.csv')\n", " return histid\n", "preprocess(105)" @@ -1283,27 +1521,9 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 24, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-05 08:45:00\n" - ] - }, - { - "data": { - "image/png": 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", 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inter_nonode_idstart_unixphas_Aphas_Bdurationinc_edge_Aout_edge_Ainc_edge_Bout_edge_B
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" + ], + "text/plain": [ + " inter_no node_id start_unix phas_A phas_B duration inc_edge_A \\\n", + "1756 202 i9 1704411610 1 1 46 571510152_02 \n", + "1757 202 i9 1704411610 2 2 114 NaN \n", + "1758 175 i0 1704411629 1 1 40 -571542797_02 \n", + "1759 175 i0 1704411629 2 2 42 -571500487_01 \n", + "1760 175 i0 1704411629 3 3 29 571545870_02 \n", + "... ... ... ... ... ... ... ... \n", + "1972 201 i8 1704412640 5 5 17 571500583_01 \n", + "1973 206 i7 1704412660 1 1 25 -571511538_02 \n", + "1974 206 i7 1704412660 2 2 25 NaN \n", + "1975 206 i7 1704412660 3 3 15 -571511538_02 \n", + "1976 206 i7 1704412660 4 4 15 NaN \n", + "\n", + " out_edge_A inc_edge_B out_edge_B \n", + "1756 -571510152_01 571510152_01 571510152_01.65 \n", + "1757 -571510152_01 NaN NaN \n", + "1758 571500487_01 -571500487_01 571542797_02 \n", + "1759 571545870_01 -571542797_02 571510153_01 \n", + "1760 571510153_01 571545870_02 571542797_02 \n", + "... ... ... ... \n", + "1972 571500617_01 571500583_01 571500569_01 \n", + "1973 571542073_02 571542073_01 571511538_02 \n", + "1974 571542073_02 NaN NaN \n", + "1975 571542073_02 571542073_01 571511538_02 \n", + "1976 571542073_02 NaN NaN \n", + "\n", + "[221 rows x 10 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "for m in range(30, 288):\n", - " print(m)\n", - " histid = preprocess(m)" + "preprocess(105)\n", + "preprocess(106)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# for m in range(30, 288):\n", + "# print(m)\n", + "# histid = preprocess(m)" ] } ], diff --git a/Script/preprocess_5min.ipynb b/Script/preprocess_5min.ipynb new file mode 100644 index 000000000..1a7c91a6a --- /dev/null +++ b/Script/preprocess_5min.ipynb @@ -0,0 +1,1004 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "from tqdm import tqdm\n", + "from datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime(2024, 1, 5, 11, 55, 13, 99135)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "now = datetime.now()\n", + "now = now.replace(month=1, day=5)\n", + "now" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# 5초 단위로 이동류번호 저장 및 신호이력에서 유닉스시각 가져와서 표시, 한시간동안의 데이터만 보관\n", + "midnight = int(datetime(2024, 1, 5, 0, 0, 0).timestamp())\n", + "next_day = int(datetime(2024, 1, 6, 0, 0, 0).timestamp())\n", + "fsecs = range(midnight, next_day, 5) # fsecs : unix time by Five SECondS\n", + "fmins = range(midnight, next_day, 300) # fmins : unix time by Five MINuteS" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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inter_nophas_Aphas_Bmove_Amove_Bstart_unix
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" + ], + "text/plain": [ + " inter_no phas_A phas_B move_A move_B start_unix\n", + "0 177 1 1 8 4 1704408330\n", + "1 177 2 2 7 3 1704408330\n", + "2 177 3 3 17 18 1704408330\n", + "3 177 4 4 5 1 1704408330\n", + "4 201 1 1 8 3 1704408330\n", + ".. ... ... ... ... ... ...\n", + "700 178 4 4 6 1 1704411830\n", + "701 201 1 1 8 3 1704411850\n", + "702 201 4 4 6 1 1704411850\n", + "703 201 5 5 7 4 1704411850\n", + "704 206 2 2 17 18 1704411880\n", + "\n", + "[705 rows x 6 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "history = pd.read_csv('../Data/tables/history.csv', index_col=0)\n", + "m = 105\n", + "present_time = fmins[m]\n", + "\n", + "# - 아래 절차를 5초마다 반복\n", + "for fsec in range(midnight, present_time + 1, 5): # fsec : unix time by Five SECond\n", + " # 1. 상태 테이블 조회해서 전체 데이터중 필요데이터(교차로번호, A링 현시번호, A링 이동류번호, B링 현시번호, B링 이동류번호)만 수집 : A\n", + " # move = time2move[fsec]\n", + " move = pd.read_csv(f'../Data/tables/move/move_{fsec}.csv', index_col=0)\n", + " # 2. 이력 테이블 조회해서 교차로별로 유닉스시간 최대인 데이터(교차로변호, 종료유닉스타임)만 수집 : B\n", + " recent_histories = [group.iloc[-1:] for _, group in history[history['end_unix'] < fsec].groupby('inter_no')] # 교차로별로 유닉스시간이 최대인 행들\n", + " if not recent_histories:\n", + " rhistory = pd.DataFrame({'inter_no':[], 'end_unix':[]}) # recent history\n", + " else:\n", + " rhistory = pd.concat(recent_histories)\n", + " recent_unix = rhistory[['inter_no', 'end_unix']]\n", + " # 3. 상태 테이블 조회정보(A)와 이력 테이블 조회정보(B) 조인(키값 : 교차로번호) : C\n", + " move = pd.merge(move, recent_unix, how='left', on='inter_no')\n", + " move['end_unix'] = move['end_unix'].fillna(0).astype(int)\n", + " move = move.drop_duplicates()\n", + " # 4. C데이터 프레임에 신규 컬럼(시작 유닉스타임) 생성 후 종료유닉스 타임 값 입력, 종료 유닉스 타임 컬럼 제거\n", + " move = move.rename(columns = {'end_unix':'start_unix'})\n", + " # 5. 이동류 이력정보 READ\n", + " # - CSV 파일로 서버에 저장된 이동류정보를 읽어옴(파일이 없는 경우에는 데이터가 없는 프레임 D 생성)\n", + " try:\n", + " if isinstance(movement, pd.DataFrame): # movement가 존재할 경우 그걸 그대로 씀.\n", + " pass\n", + " else: \n", + " movement = pd.DataFrame()\n", + " except NameError: # movement가 존재하지 않는 경우 생성\n", + " movement = pd.DataFrame()\n", + " # 6. 이동류 이력정보 데이터테이블(D)에 C데이터 add\n", + " movement = pd.concat([movement, move])\n", + " # 7. D데이터 프레임에서 중복데이터 제거(교차로번호, 시작 유닉스타임, A링 현시번호, B링 현시번호 같은 행은 제거)\n", + " movement = movement.drop_duplicates(['inter_no','phas_A','phas_B','start_unix'])\n", + " # 8. D데이터 보관 시간 기준시간을 시작 유닉스 타임의 최대값 - 3600을 값으로 산출하고, 보관 시간 기준시간보다 작은 시작 유닉스 타임을 가진 행은 모두 제거(1시간 데이터만 보관)\n", + " movement = movement[movement.start_unix > fsec - 3600]\n", + " movement = movement.sort_values(by=['start_unix','inter_no','phas_A','phas_B']).reset_index(drop=True)\n", + "\n", + "display(movement)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def make_splits(plan):\n", + " # split, isplit : A,B 분리 혹은 통합시 사용될 수 있는 딕셔너리 \n", + " splits = {} # splits maps (inter_no, start_hour, start_minute) to split \n", + " for i, row in plan.iterrows():\n", + " inter_no = row.inter_no\n", + " start_hour = row.start_hour\n", + " start_minute = row.start_minute\n", + " cycle = row.cycle\n", + " cums_A = row[[f'dura_A{j}' for j in range(1,9)]].cumsum()\n", + " cums_B = row[[f'dura_B{j}' for j in range(1,9)]].cumsum()\n", + " splits[(inter_no, start_hour, start_minute)] = {} # split maps (phas_A, phas_B) to k\n", + " k = 0\n", + " for t in range(cycle):\n", + " new_phas_A = len(cums_A[cums_A < t]) + 1\n", + " new_phas_B = len(cums_B[cums_B < t]) + 1\n", + " if k == 0 or ((new_phas_A, new_phas_B) != (phas_A, phas_B)):\n", + " k += 1\n", + " phas_A = new_phas_A\n", + " phas_B = new_phas_B\n", + " splits[(inter_no, start_hour, start_minute)][(phas_A, phas_B)] = k\n", + "\n", + " isplits = {} # the inverse of splits\n", + " for i in splits:\n", + " isplits[i] = {splits[i][k]:k for k in splits[i]} # isplit maps k to (phas_A, phas_B)\n", + " return splits, isplits\n", + "\n", + "def make_timetable(plan):\n", + " # timetable\n", + " timetable = plan[['start_hour', 'start_minute']].drop_duplicates()\n", + " timetable['start_seconds'] = midnight + timetable['start_hour'] * 3600 + timetable['start_minute'] * 60\n", + " return timetable\n", + "\n", + "# inter2node\n", + "inter_node = pd.read_csv('../Data/tables/inter_node.csv', index_col=0)\n", + "inter_node = inter_node[inter_node.inter_type=='parent']\n", + "inter2node = dict(zip(inter_node['inter_no'], inter_node['node_id']))\n", + "\n", + "hours = np.array(range(midnight - 7200, next_day + 1, 3600)) # 정각에 해당하는 시각들 목록" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_DS(rhist, curr_unix, hours, timetable):\n", + " program_starts = np.array(timetable.start_seconds)\n", + " idx = (program_starts <= present_time).sum() - 1\n", + " program_start = program_starts[idx]\n", + " if list(hours[hours <= curr_unix]):\n", + " ghour_lt_curr_unix = hours[hours <= curr_unix].max() # the greatest hour less than or equal to curr_unix\n", + " else:\n", + " ghour_lt_curr_unix = program_start\n", + " start_unixes = rhist.start_unix.unique()\n", + " start_unixes_lt_ghour = np.sort(start_unixes[start_unixes < ghour_lt_curr_unix]) # start unixes less than ghour_lt_curr_unix\n", + " # 기준유닉스(base_unix) : curr_unix보다 작은 hour 중에서 가장 큰 값으로부터 다섯 번째로 작은 start_unix\n", + " if len(start_unixes_lt_ghour) > 5:\n", + " base_unix = start_unixes_lt_ghour[-5]\n", + " # start_unixes_lt_ghour의 길이가 5 미만일 경우에는 맨 앞 start_unix로 base_unix를 지정\n", + " else:\n", + " base_unix = rhist.start_unix.min()\n", + " D_n = curr_unix - base_unix\n", + " S_n_durs = rhist[(rhist.start_unix > base_unix) & (rhist.start_unix <= curr_unix)] \\\n", + " [[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]\n", + " S_n = S_n_durs.values.sum() // 2\n", + " return D_n, S_n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def load_prow(plan, timetable, inter_no, time):\n", + " '''\n", + " load planned row\n", + " '''\n", + " # 프로그램 시작시각\n", + " program_starts = np.array(timetable.start_seconds)\n", + " idx = (program_starts <= time).sum() - 1\n", + " program_start = program_starts[idx]\n", + "\n", + " # 최근 프로그램 시작시각에 대한 신호계획\n", + " start_hour = timetable.iloc[idx].start_hour\n", + " start_minute = timetable.iloc[idx].start_minute\n", + " prow = plan[(plan.inter_no==inter_no) & (plan.start_hour==start_hour) & (plan.start_minute==start_minute)] # planned row\n", + " return program_start, prow" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def make_rhistory(plan, timetable, history, present_time, adder):\n", + " # 1. 조회시점의 유닉스 타임 이전의 신호이력 수집\n", + " rhistory = history.copy() # recent history\n", + " rhistory = rhistory[(rhistory.end_unix <= present_time) & (rhistory.end_unix > present_time - 9000)] # 두 시간 반 전부터 현재까지의 신호이력을 가져옴. 9000 = 3600 * 2.5\n", + "\n", + " # rhistory에 모든 교차로번호가 존재하지 않으면 해당 교차로번호에 대한 신호이력을 추가함 (at 최근 프로그램 시작시각)\n", + " whole_inter_nos = sorted(history.inter_no.unique())\n", + " recent_inter_nos = sorted(rhistory.inter_no.unique())\n", + " if not whole_inter_nos==recent_inter_nos:\n", + " for inter_no in set(whole_inter_nos) - set(recent_inter_nos):\n", + " program_start, prow = load_prow(plan, timetable, inter_no, present_time - 9000)\n", + " cycle = prow.cycle.iloc[0]\n", + " row1 = prow.drop(['start_hour', 'start_minute'], axis=1).copy()\n", + " row2 = prow.drop(['start_hour', 'start_minute'], axis=1).copy()\n", + " # prow에서 필요한 부분을 rhistory에 추가\n", + " row1['end_unix'] = program_start\n", + " row2['end_unix'] = program_start + cycle\n", + " rhistory = pd.concat([rhistory, row1, row2]).reset_index(drop=True)\n", + " # present_time + adder 의 시각에 한 주기의 신호 추가\n", + " for inter_no in set(whole_inter_nos):\n", + " program_start, prow = load_prow(plan, timetable, inter_no, present_time)\n", + " cycle = prow.cycle.iloc[0]\n", + " row3 = prow.drop(['start_hour', 'start_minute'], axis=1).copy()\n", + " # prow에서 필요한 부분을 rhistory에 추가\n", + " row3['end_unix'] = present_time + adder\n", + " rhistory = pd.concat([rhistory, row3]).reset_index(drop=True)\n", + "\n", + " # 2. 시작 유닉스 타임컬럼 생성 후 종류 유닉스 타임에서 현시별 현시기간 컬럼의 합을 뺀 값으로 입력\n", + " # - 현시시간의 합을 뺀 시간의 +- 10초 이내에 이전 주기정보가 존재하면 그 유닉스 시간을 시작 유닉스시간 값으로 하고, 존재하지 않으면 현시시간의 합을 뺀 유닉스 시간을 시작 유닉스 시간으로 지정\n", + " for i, row in rhistory.iterrows():\n", + " inter_no = row.inter_no\n", + " end_unix = row.end_unix\n", + " elapsed_time = row[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]].sum() // 2 # 현시시간 합\n", + " # 이전 유닉스 존재하지 않음 : 현시시간 합의 차\n", + " start_unix = end_unix - elapsed_time\n", + " pre_rows = history[:i] # previous rows\n", + " if inter_no in pre_rows.inter_no.unique(): # 이전 유닉스 존재\n", + " pre_unix = pre_rows[pre_rows.inter_no == inter_no]['end_unix'].iloc[-1] # previous unix time\n", + " # 이전 유닉스 존재, abs < 10 : 이전 유닉스\n", + " if abs(pre_unix - start_unix) < 10:\n", + " start_unix = pre_unix\n", + " # 이전 유닉스 존재, abs >=10 : 현시시간 합의 차\n", + " else:\n", + " pass\n", + " rhistory.loc[i, 'start_unix'] = start_unix \n", + " rhistory[rhistory.isna()] = 0\n", + " rhistory['start_unix'] = rhistory['start_unix'].astype(int)\n", + " rhistory = rhistory[['inter_no', 'start_unix'] + [f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)] + ['cycle']]\n", + " return rhistory\n", + "adder = 600" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def processing(plan, rhistory, timetable, hours):\n", + " rhists = []\n", + " for inter_no in sorted(rhistory.inter_no.unique()):\n", + " rhist = rhistory.copy()[rhistory.inter_no==inter_no]\n", + " rhist = rhist.drop_duplicates(subset=['start_unix']).reset_index(drop=True)\n", + "\n", + " # D_n 및 S_n 값 정의\n", + " rhist['D_n'] = 0 # D_n : 시간차이\n", + " rhist['S_n'] = 0 # S_n : 현시시간합\n", + " for n in range(len(rhist)):\n", + " curr_unix = rhist.iloc[n].start_unix # current start_unix\n", + " rhist.loc[n, ['D_n', 'S_n']] = calculate_DS(rhist, curr_unix, hours, timetable)\n", + "\n", + " # 이전시각, 현재시각\n", + " prev_unix = rhist.loc[0, 'start_unix'] # previous start_unix\n", + " curr_unix = rhist.loc[1, 'start_unix'] # current start_unix\n", + "\n", + " # rhist의 마지막 행에 도달할 때까지 반복\n", + " while True:\n", + " n = rhist[rhist.start_unix==curr_unix].index[0]\n", + " cycle = rhist.loc[n, 'cycle']\n", + " D_n = rhist.loc[n, 'D_n']\n", + " S_n = rhist.loc[n, 'S_n']\n", + " # 참값인 경우\n", + " if (abs(D_n - S_n) <= 5):\n", + " pass\n", + " # 참값이 아닌 경우\n", + " else:\n", + " # 2-1-1. 결측치 처리 : 인접한 두 start_unix의 차이가 계획된 주기의 두 배보다 크면 결측이 일어났다고 판단, 신호계획의 현시시간으로 \"대체\"\n", + " if curr_unix - prev_unix >= 2 * cycle:\n", + " # prev_unix를 계획된 주기만큼 늘려가면서 한 행씩 채워나간다.\n", + " # (curr_unix와의 차이가 계획된 주기보다 작거나 같아질 때까지)\n", + " while curr_unix - prev_unix > cycle:\n", + " prev_unix += cycle\n", + " # 신호 계획(prow) 불러오기\n", + " start_seconds = np.array(timetable.start_seconds)\n", + " idx = (start_seconds <= prev_unix).sum() - 1\n", + " start_hour = timetable.iloc[idx].start_hour\n", + " start_minute = timetable.iloc[idx].start_minute\n", + " prow = plan.copy()[(plan.inter_no==inter_no) & (plan.start_hour==start_hour) & (plan.start_minute==start_minute)] # planned row\n", + " # prow에서 필요한 부분을 rhist에 추가\n", + " prow['start_unix'] = prev_unix\n", + " prow = prow.drop(['start_hour', 'start_minute', 'offset'], axis=1)\n", + " cycle = prow.iloc[0].cycle\n", + " rhist = pd.concat([rhist, prow])\n", + " rhist = rhist.sort_values(by='start_unix').reset_index(drop=True)\n", + " n += 1\n", + "\n", + " # 2-1-2. 이상치 처리 : 비율에 따라 해당 행을 \"삭제\"(R_n <= 0.5) 또는 \"조정\"(R_n > 0.5)한다\n", + " R_n = (curr_unix - prev_unix) / cycle # R_n : 비율\n", + " # R_n이 0.5보다 작거나 같으면 해당 행을 삭제\n", + " if R_n <= 0.5:\n", + " rhist = rhist.drop(index=n).reset_index(drop=True)\n", + " if n >= rhist.index[-1]:\n", + " break\n", + " # 행삭제에 따른 curr_unix, R_n 재정의\n", + " curr_unix = rhist.loc[n, 'start_unix']\n", + " R_n = (curr_unix - prev_unix) / cycle # R_n : 비율\n", + "\n", + " # R_n이 0.5보다 크면 해당 행 조정 (비율을 유지한 채로 현시시간 대체)\n", + " if R_n > 0.5:\n", + " # 신호 계획(prow) 불러오기\n", + " start_seconds = np.array(timetable.start_seconds)\n", + " idx = (start_seconds <= curr_unix).sum() - 1\n", + " start_hour = timetable.iloc[idx].start_hour\n", + " start_minute = timetable.iloc[idx].start_minute\n", + " prow = plan[(plan.inter_no==inter_no) & (plan.start_hour==start_hour) & (plan.start_minute==start_minute)] # planned row\n", + " # 조정된 현시시간 (prow에 R_n을 곱하고 정수로 바꿈)\n", + " adjusted_dur = prow.copy()[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] * R_n\n", + " int_parts = adjusted_dur.iloc[0].apply(lambda x: int(x))\n", + " frac_parts = adjusted_dur.iloc[0] - int_parts\n", + " difference = round(adjusted_dur.iloc[0].sum()) - int_parts.sum()\n", + " for _ in range(difference): # 소수 부분이 가장 큰 상위 'difference'개의 값에 대해 올림 처리\n", + " max_frac_index = frac_parts.idxmax()\n", + " int_parts[max_frac_index] += 1\n", + " frac_parts[max_frac_index] = 0 # 이미 처리된 항목은 0으로 설정\n", + " # rhist에 조정된 현시시간을 반영\n", + " rhist.loc[n, [f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] = int_parts.values\n", + " rhist.loc[n, 'cycle'] = int_parts.sum().sum() // 2\n", + "\n", + " if n >= rhist.index[-1]:\n", + " break\n", + " prev_unix = curr_unix\n", + " curr_unix = rhist.loc[n+1, 'start_unix']\n", + "\n", + " # 생략해도 무방할 코드\n", + " rhist = rhist.reset_index(drop=True)\n", + " rhist = rhist.sort_values(by=['start_unix'])\n", + "\n", + " # D_n 및 S_n 값 재정의\n", + " for n in range(len(rhist)):\n", + " curr_unix = rhist.iloc[n].start_unix # current start_unix\n", + " rhist.loc[n, ['D_n', 'S_n']] = calculate_DS(rhist, curr_unix, hours, timetable)\n", + " rhists.append(rhist)\n", + " rhists = pd.concat(rhists).sort_values(by=['start_unix','inter_no'])\n", + " rhists = rhists[rhists.start_unix >= present_time - 3600]\n", + " rhists = rhists.drop(columns=['D_n', 'S_n'])\n", + " return rhists" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def make_hrhists(rhists, isplits, timetable):\n", + " # 계층화된 형태로 변환\n", + " hrhists = [] # hierarchied recent history\n", + " for i, row in rhists.iterrows():\n", + " inter_no = row.inter_no\n", + " start_unix = row.start_unix\n", + "\n", + " ind = (timetable['start_seconds'] <= row.start_unix).sum() - 1\n", + " start_hour = timetable.iloc[ind].start_hour\n", + " start_minute = timetable.iloc[ind].start_minute\n", + " isplit = isplits[(inter_no, start_hour, start_minute)]\n", + " phas_As = [isplit[j][0] for j in isplit.keys()]\n", + " phas_Bs = [isplit[j][1] for j in isplit.keys()]\n", + " durs_A = row[[f'dura_A{j}' for j in range(1,9)]]\n", + " durs_B = row[[f'dura_B{j}' for j in range(1,9)]]\n", + " durations = []\n", + " for j in range(1, len(isplit)+1):\n", + " ja = isplit[j][0]\n", + " jb = isplit[j][1]\n", + " if ja == jb:\n", + " durations.append(min(durs_A[ja-1], durs_B[jb-1]))\n", + " else:\n", + " durations.append(abs(durs_A[ja-1] - durs_B[ja-1]))\n", + " new_rows = pd.DataFrame({'inter_no':[inter_no] * len(durations), 'start_unix':[start_unix] * len(durations),\n", + " 'phas_A':phas_As, 'phas_B':phas_Bs, 'duration':durations})\n", + " hrhists.append(new_rows)\n", + " hrhists = pd.concat(hrhists)\n", + " hrhists = hrhists.sort_values(by = ['start_unix', 'inter_no', 'phas_A', 'phas_B']).reset_index(drop=True)\n", + " return hrhists" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def update_movement(hrhists, movement, movements):\n", + " # 중복을 제거하고 (inter_no, start_unix) 쌍을 만듭니다.\n", + " hrhists_inter_unix = set(hrhists[['inter_no', 'start_unix']].drop_duplicates().itertuples(index=False, name=None))\n", + " movement_inter_unix = set(movement[['inter_no', 'start_unix']].drop_duplicates().itertuples(index=False, name=None))\n", + "\n", + " # hrhists에는 있지만 movement에는 없는 (inter_no, start_unix) 쌍을 찾습니다.\n", + " missing_in_movement = hrhists_inter_unix - movement_inter_unix\n", + "\n", + " # 새로운 행들을 생성합니다.\n", + " new_rows = []\n", + " if missing_in_movement:\n", + " for inter_no, start_unix in missing_in_movement:\n", + " # movements에서 해당 inter_no의 데이터를 찾습니다.\n", + " new_row = movements[movements['inter_no'] == inter_no].copy()\n", + " # start_unix 값을 설정합니다.\n", + " new_row['start_unix'] = start_unix\n", + " new_rows.append(new_row)\n", + "\n", + " # 새로운 데이터프레임을 생성하고 기존 movement 데이터프레임과 합칩니다.\n", + " new_movement = pd.concat(new_rows, ignore_index=True)\n", + " movement_updated = pd.concat([movement, new_movement], ignore_index=True)\n", + " else:\n", + " movement_updated = movement\n", + " return movement_updated" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def make_movements():\n", + " movements_path = '../Intermediates/movement/'\n", + " movements_list = [pd.read_csv(movements_path + file, index_col=0) for file in tqdm(os.listdir(movements_path))]\n", + " movements = pd.concat(movements_list)\n", + " movements = movements.drop(columns=['start_unix'])\n", + " movements = movements.drop_duplicates()\n", + " movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B'])\n", + " movements = movements.reset_index(drop=True)\n", + " movements.to_csv('../Intermediates/movements.csv')\n", + " return movements" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def make_histid(present_time, hrhists, movement_updated, inter2node, matching):\n", + " # movements and durations\n", + " movedur = pd.merge(hrhists, movement_updated, how='inner', on=['inter_no', 'start_unix', 'phas_A', 'phas_B'])\n", + " movedur = movedur.sort_values(by=['start_unix', 'inter_no', 'phas_A','phas_B'])\n", + " movedur = movedur[['inter_no', 'start_unix', 'phas_A', 'phas_B', 'move_A', 'move_B', 'duration']]\n", + "\n", + " # 이동류 매칭 테이블에서 진입id, 진출id를 가져와서 붙임.\n", + " for i, row in movedur.iterrows():\n", + " inter_no = row.inter_no\n", + " start_unix = row.start_unix\n", + " # incoming and outgoing edges A\n", + " move_A = row.move_A\n", + " if move_A in [17, 18]:\n", + " inc_edge_A = np.nan\n", + " outhedge_A = np.nan\n", + " else:\n", + " match_A = matching[(matching.inter_no == inter_no) & (matching.move_no == move_A)].iloc[0]\n", + " inc_edge_A = match_A.inc_edge\n", + " out_edge_A = match_A.out_edge\n", + " movedur.loc[i, ['inc_edge_A', 'out_edge_A']] = [inc_edge_A, out_edge_A]\n", + " # incoming and outgoing edges B\n", + " move_B = row.move_B\n", + " if move_B in [17, 18]:\n", + " inc_edge_B = np.nan\n", + " out_edge_B = np.nan\n", + " else:\n", + " match_B = matching[(matching.inter_no == inter_no) & (matching.move_no == move_B)].iloc[0]\n", + " inc_edge_B = match_B.inc_edge\n", + " out_edge_B = match_B.out_edge\n", + " movedur.loc[i, ['inc_edge_B', 'out_edge_B']] = [inc_edge_B, out_edge_B]\n", + "\n", + " # 이동류 컬럼 제거\n", + " movedur = movedur.drop(['move_A', 'move_B'], axis=1)\n", + "\n", + " histid = movedur.copy() # history with edge ids (incoming and outgoing edge ids)\n", + " histid['node_id'] = histid['inter_no'].map(inter2node)\n", + " histid = histid[['inter_no', 'node_id', 'start_unix', 'phas_A', 'phas_B', 'duration', 'inc_edge_A', 'out_edge_A', 'inc_edge_B', 'out_edge_B']]\n", + " histid_start = present_time - 600\n", + " histid = histid[histid.start_unix > histid_start]\n", + " return histid" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def preprocess(m):\n", + " '''\n", + " 통합테이블(histid)를 만드는 함수\n", + "\n", + " input : m\n", + " - m ranges from 0 to 287, but 0 makes an error where 288 = 86400//300\n", + " - present_time = fmins[m] : 현재시점\n", + "\n", + " output : histid (통합테이블, HISTory with edge_IDs)\n", + " - 컬럼 : inter_no, node_id, start_unix, phas_A, phas_B, duration, inc_edge_A, out_edge_A, inc_edge_B, out_edge_B\n", + "\n", + " 주요 데이터, 중간산출물 및 결과물 :\n", + " # 데이터\n", + " - history : 신호이력 (inter_no, end_unix, dura_Aj, dura_Bj, cycle, offset)\n", + " - plan : 신호계획 (inter_no, start_hour, start_minute, dura_Aj, dura_Bj cycle, offset)\n", + " # 중간산출물\n", + " - rhists (recent history)\n", + " - history에서 현재 시각 이전의 데이터를 가져옴.\n", + " - end_unix를 start_unix로 변환\n", + " - 참값판단 프로세스(결측·이상치 처리)\n", + " - 컬럼 : inter_no, start_unix, dura_Aj, dura_Bj, cycle\n", + " - hrhists (hierarchized recent history)\n", + " - rhists를 계층화\n", + " - 컬럼 : inter_no, start_unix, phas_A, phas_B, duration\n", + " - movements\n", + " - 각 교차로에 대하여 현시별로 이동류를 정해놓음.\n", + " - join시 사용하기 위함.\n", + " - 한 번 만들어놓고 두고두고 사용함.\n", + " - 컬럼 : inter_no, phas_A, phas_B, move_A, move_B\n", + " - movement\n", + " - 현재 시점에서의 이동류정보\n", + " - 컬럼 : inter_no, phas_A, phas_B, move_A, move_B, start_unix\n", + " - movement_updated\n", + " - movement와 hrhists를 join하기 전에, movement에는 없지만 hrhists에는 있는 start_unix에 대한 이동류 정보를 가져와 movement에 붙임\n", + " - 이동류정보는 앞서 정의한 movements에서 가져옴.\n", + " - 컬럼 : inter_no, phas_A, phas_B, move_A, move_B, start_unix\n", + " - movedur\n", + " - hrhists와 movement_updated를 join\n", + " - 컬럼 : inter_no, phas_A, phas_B, move_A, move_B, start_unix, duration\n", + " # 결과 : histid\n", + " - 신호생성에 직접적으로 사용되는 데이터프레임\n", + " - 컬럼 : inter_no, node_id, start_unix, phas_A, phas_B, duration, inc_edge_A, out_edge_A, inc_edge_B, out_edge_B\n", + " - 한글컬럼 : 교차로번호, 노드id, 시작유닉스, A현시번호, B현시번호, 현시시간, 진입엣지(A), 진출엣지(A), 진입엣지(B), 진출엣지(B)\n", + " '''\n", + " midnight = int(datetime(2024, 1, 5, 0, 0, 0).timestamp())\n", + " next_day = int(datetime(2024, 1, 6, 0, 0, 0).timestamp())\n", + " fmins = range(midnight, next_day, 300) # fmins : unix time by Five MINuteS\n", + " # 현재시각\n", + " present_time = fmins[m]\n", + " print(datetime.fromtimestamp(present_time))\n", + " # 사용할 표준 테이블 목록\n", + " plan = pd.read_csv('../Data/tables/plan.csv', index_col=0)\n", + " history = pd.read_csv('../Data/tables/history.csv', index_col=0)\n", + " matching = pd.read_csv('../Intermediates/matching.csv', index_col=0)\n", + " # 참고할 딕셔너리, 데이터프레임, 리스트 등 목록\n", + " splits, isplits = make_splits(plan)\n", + " timetable = make_timetable(plan)\n", + " inter_node = pd.read_csv('../Data/tables/inter_node.csv', index_col=0)\n", + " inter_node = inter_node[inter_node.inter_type=='parent']\n", + " inter2node = dict(zip(inter_node['inter_no'], inter_node['node_id']))\n", + " hours = np.array(range(midnight - 7200, next_day + 1, 3600)) # 정각에 해당하는 시각들 목록\n", + " # rhistory, rhists, hrhists\n", + " adder = 600\n", + " rhistory = make_rhistory(plan, timetable, history, present_time, adder)\n", + " rhists = processing(plan, rhistory, timetable, hours)\n", + " hrhists = make_hrhists(rhists, isplits, timetable)\n", + " # movements, movement, movement_updated\n", + " movements = pd.read_csv('../Intermediates/movements.csv')\n", + " movement = pd.read_csv(f'../Intermediates/movement/movement_{present_time}.csv', index_col=0)\n", + " movement_updated = update_movement(hrhists, movement, movements)\n", + " # movedur\n", + " movedur = pd.merge(movement_updated, hrhists, how='inner', on=['inter_no', 'start_unix', 'phas_A', 'phas_B']) # movements and durations\n", + " movedur = movedur.sort_values(by=['start_unix', 'inter_no', 'phas_A','phas_B'])\n", + " movedur = movedur[['inter_no', 'start_unix', 'phas_A', 'phas_B', 'move_A', 'move_B', 'duration']]\n", + " # histid\n", + " histid = make_histid(present_time, hrhists, movement_updated, inter2node, matching)\n", + " histid.to_csv(f'../Intermediates/histid/histid_{fmins[m]}.csv')\n", + " return histid" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-01-05 08:45:00\n", + "2024-01-05 08:50:00\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " inter_no node_id start_unix phas_A phas_B duration inc_edge_A \\\n", + "655 202 i9 1704411610 1 1 46 571510152_02 \n", + "656 202 i9 1704411610 2 2 114 NaN \n", + "657 175 i0 1704411629 1 1 40 -571542797_02 \n", + "658 175 i0 1704411629 2 2 42 -571500487_01 \n", + "659 175 i0 1704411629 3 3 29 571545870_02 \n", + ".. ... ... ... ... ... ... ... \n", + "871 201 i8 1704412640 5 5 17 571500583_01 \n", + "872 206 i7 1704412660 1 1 25 -571511538_02 \n", + "873 206 i7 1704412660 2 2 25 NaN \n", + "874 206 i7 1704412660 3 3 15 -571511538_02 \n", + "875 206 i7 1704412660 4 4 15 NaN \n", + "\n", + " out_edge_A inc_edge_B out_edge_B \n", + "655 -571510152_01 571510152_01 571510152_01.65 \n", + "656 -571510152_01 NaN NaN \n", + "657 571500487_01 -571500487_01 571542797_02 \n", + "658 571545870_01 -571542797_02 571510153_01 \n", + "659 571510153_01 571545870_02 571542797_02 \n", + ".. ... ... ... \n", + "871 571500617_01 571500583_01 571500569_01 \n", + "872 571542073_02 571542073_01 571511538_02 \n", + "873 571542073_02 NaN NaN \n", + "874 571542073_02 571542073_01 571511538_02 \n", + "875 571542073_02 NaN NaN \n", + "\n", + "[221 rows x 10 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocess(105)\n", + "preprocess(106)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# for m in range(30, 288):\n", + "# print(m)\n", + "# histid = preprocess(m)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rts", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Script/preprocess_daily.ipynb b/Script/preprocess_daily.ipynb new file mode 100644 index 000000000..796421874 --- /dev/null +++ b/Script/preprocess_daily.ipynb @@ -0,0 +1,690 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import json\n", + "import sumolib\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def make_match1(path_move):\n", + " '''\n", + " 신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다.\n", + " '../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다.\n", + "\n", + " return : 통합된 이동류정보\n", + " - 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보\n", + "\n", + " match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다.\n", + " '''\n", + " # [이동류번호] 불러오기 (약 1분의 소요시간)\n", + " csv_moves = os.listdir(path_move)\n", + " moves = [pd.read_csv(path_move + csv_move, index_col=0) for csv_move in tqdm(csv_moves)]\n", + " match1 = pd.concat(moves).drop_duplicates().sort_values(by=['inter_no','phas_A','phas_B']).reset_index(drop=True)\n", + " match1.to_csv('../Intermediates/match1.csv')\n", + " return match1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def make_match2(match1):\n", + " '''\n", + " match1을 계층화함.\n", + " - match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B\n", + " - match2의 컬럼 : inter_no, phase_no, ring_type, move_no\n", + " '''\n", + " # 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no')\n", + " matchA = match1[['inter_no', 'phas_A', 'move_A']].copy()\n", + " matchA.columns = ['inter_no', 'phase_no', 'move_no']\n", + " matchA['ring_type'] = 'A'\n", + " matchB = match1[['inter_no', 'phas_B', 'move_B']].copy()\n", + " matchB.columns = ['inter_no', 'phase_no', 'move_no']\n", + " matchB['ring_type'] = 'B'\n", + " match2 = pd.concat([matchA, matchB]).drop_duplicates()\n", + " match2 = match2[['inter_no', 'phase_no', 'ring_type', 'move_no']]\n", + " match2 = match2.sort_values(by=list(match2.columns))\n", + " return match2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def make_match3(match2, nema):\n", + " '''\n", + " 각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함.\n", + " - match2의 컬럼 : inter_no, phase_no, ring_type, move_no\n", + " - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir\n", + "\n", + " nema : \n", + " - 컬럼 : move_no, inc_dir, out_dir\n", + " - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블\n", + " - 이동류번호 : 1 ~ 16, 17, 18, 21\n", + " - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서\n", + " '''\n", + " # nema 정보 불러오기 및 병합\n", + " match3 = pd.merge(match2, nema, how='left', on='move_no').drop_duplicates()\n", + " return match3" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def make_match4(match3, angle):\n", + " '''\n", + " 방위각 정보를 매칭시켜 추가함.\n", + " - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir\n", + " - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle\n", + "\n", + " angle_original : \n", + " - 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8)\n", + " - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블\n", + " - 이동류번호 : 1 ~ 16, 17, 18, 21\n", + " - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서\n", + " '''\n", + "\n", + " # 계층화\n", + " angles = []\n", + " for i, row in angle.iterrows():\n", + " angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]\n", + " new = pd.DataFrame({'inter_no':[row.inter_no] * 16, 'phase_no':list(range(1, 9))*2, 'ring_type':['A'] * 8 + ['B'] * 8, 'angle_code':angle_codes.to_list()})\n", + " angles.append(new)\n", + " angles = pd.concat(angles)\n", + " angles = angles.dropna().reset_index(drop=True)\n", + "\n", + " # 병합\n", + " six_chars = angles.angle_code.apply(lambda x:len(x)==6)\n", + " angles.loc[six_chars,'inc_angle'] = angles.angle_code.apply(lambda x:x[:3])\n", + " angles.loc[six_chars,'out_angle'] = angles.angle_code.apply(lambda x:x[3:])\n", + " angles = angles.drop('angle_code', axis=1)\n", + " match4 = pd.merge(match3, angles, how='left', left_on=['inter_no', 'phase_no', 'ring_type'],\n", + " right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates()\n", + " return match4" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def make_match5(match4, net, inter_node, inter_info):\n", + " '''\n", + " 진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로).\n", + " - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle\n", + " - match5의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id\n", + " \n", + " 사용된 데이터 : \n", + " (1) net\n", + " - 성남시 정자동 부근의 샘플 네트워크\n", + " (2) inter_node\n", + " - 교차로번호와 노드id를 매칭시키는 테이블.\n", + " - parent/child 정보도 포함되어 있음\n", + " - 컬럼 : inter_no, node_id, inter_type\n", + " (3) inter_info\n", + " - 교차로 정보. 여기에서는 위도와 경도가 쓰임.\n", + " - 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no\n", + "\n", + " 진입엣지id, 진출엣지id를 얻는 과정 :\n", + " - match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함.\n", + " * 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음\n", + " - 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장.\n", + " * inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge\n", + " - inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄.\n", + " - 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄.\n", + " - 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함.\n", + " '''\n", + "\n", + " # parent node만 가져옴.\n", + " inter_node1 = inter_node[inter_node.inter_type == 'parent'].drop('inter_type', axis=1)\n", + " inter_info1 = inter_info[['inter_no', 'inter_lat', 'inter_lon']]\n", + " inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'],\n", + " right_on=['inter_no']).drop_duplicates()\n", + "\n", + " inter2node = dict(zip(inter['inter_no'], inter['node_id']))\n", + "\n", + " match5 = match4.copy()\n", + " # 진입진출ID 매칭\n", + " for index, row in match5.iterrows():\n", + " node_id = inter2node[row.inter_no]\n", + " node = net.getNode(node_id)\n", + " # 교차로의 모든 (from / to) edges\n", + " inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges\n", + " out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges\n", + " # 교차로의 모든 (from / to) directions\n", + " inc_dirs = []\n", + " for inc_edge in inc_edges:\n", + " start = inc_edge.getShape()[-2]\n", + " end = inc_edge.getShape()[-1]\n", + " inc_dir = np.array(end) - np.array(start)\n", + " inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5\n", + " inc_dirs.append(inc_dir)\n", + " out_dirs = []\n", + " for out_edge in out_edges:\n", + " start = out_edge.getShape()[0]\n", + " end = out_edge.getShape()[1]\n", + " out_dir = np.array(end) - np.array(start)\n", + " out_dir = out_dir / (out_dir ** 2).sum() ** 0.5\n", + " out_dirs.append(out_dir)\n", + " # 진입각, 진출각 불러오기\n", + " if not pd.isna(row.inc_angle):\n", + " inc_angle = int(row.inc_angle)\n", + " out_angle = int(row.out_angle)\n", + " # 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환\n", + " inc_angle = (-90 - inc_angle) % 360\n", + " inc_angle = inc_angle * np.pi / 180.\n", + " inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)])\n", + " out_angle = (90 - out_angle) % 360\n", + " out_angle = out_angle * np.pi / 180.\n", + " out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)])\n", + " # 매칭 엣지 반환\n", + " inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax()\n", + " out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax()\n", + " inc_edge_id = inc_edges[inc_index].getID()\n", + " out_edge_id = out_edges[out_index].getID()\n", + " match5.at[index, 'inc_edge'] = inc_edge_id\n", + " match5.at[index, 'out_edge'] = out_edge_id\n", + " match5['node_id'] = match5['inter_no'].map(inter2node)\n", + " match5 = match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True)\n", + " return match5" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def make_match6(match5, inter_node, uturn, coord):\n", + " '''\n", + " 진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로).\n", + " - match6의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id\n", + " \n", + " 사용된 데이터 : \n", + " (1) inter_node\n", + " - 교차로번호와 노드id를 매칭시키는 테이블.\n", + " - parent/child 정보도 포함되어 있음\n", + " - 컬럼 : inter_no, node_id, inter_type\n", + " (2) uturn (유턴정보)\n", + " - 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge\n", + " - parent_id, child_id : 주교차로id, 유턴교차로id\n", + " - direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향)\n", + " - condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나\n", + " - inc_edge, out_edge : 유턴에 대한 진입진출엣지\n", + " (3) coord (연동교차로정보)\n", + " - 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge\n", + " - parent_id, child_id : 주교차로id, 연동교차로id\n", + " - 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지\n", + "\n", + " 설명 :\n", + " - match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음.\n", + " 여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함.\n", + " 유턴교차로 :\n", + " - directions를 정북기준 시계방향의 8방위로 정함.\n", + " - 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음.\n", + " - 예) 진입방향(direction)이 '북'일 때, \n", + " - 직진 : (북, 남)\n", + " * 남 : directions[(ind + 4) % len(directions)]\n", + " - 좌회전 : (북, 동)\n", + " * 동 : directions[(ind + 2) % len(directions)]\n", + " - 보행 : (서, 동)\n", + " * 서 : directions[(ind - 2) % len(directions)]\n", + " - uturn의 각 행을 순회하면서 아래 과정을 반복함\n", + " - match5에서 parent_id에 해당하는 행들을 가져옴(cmatch).\n", + " - condition 별로 진입방향, 진출방향A, 진출방향B 정함.\n", + " - 상술한 directions를 활용하여 정함.\n", + " - (진입방향, 진출방향A, 진출방향B)을 고려하여 (현시, 링) 별로 진입엣지id, 진출엣지id를 정함.\n", + " - ex) cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]\n", + " - 순회하면서 만든 cmatch를 cmatchs라는 리스트에 저장함.\n", + "\n", + " 연동교차로 :\n", + " - 연동교차로의 경우 coord에 (현시, 링)별 진입엣지ID, 진출엣지ID가 명시되어 있음.\n", + " - 'inc_dir', 'out_dir', 'inc_angle','out_angle'와 같은 열들은 np.nan을 지정해놓음.\n", + " - 이 열들은, 사실상 다음 스텝부터는 사용되지 않는 열들이기 때문에 np.nan으로 지정해놓아도 문제없음.\n", + "\n", + " match6 :\n", + " - 이렇게 얻은 match5, cmatchs, coord를 모두 pd.concat하여 match6을 얻어냄.\n", + " '''\n", + "\n", + " node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no']))\n", + "\n", + " child_ids = inter_node[inter_node.inter_type=='child'].node_id.unique()\n", + " ch2pa = {} # child to parent\n", + " for child_id in child_ids:\n", + " parent_no = inter_node[inter_node.node_id==child_id].inter_no.iloc[0]\n", + " sub_inter_node = inter_node[inter_node.inter_no==parent_no]\n", + " ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id\n", + " directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향\n", + "\n", + " # 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여\n", + " cmatches = []\n", + " for _, row in uturn.iterrows():\n", + " child_id = row.child_id\n", + " parent_id = row.parent_id\n", + " direction = row.direction\n", + " condition = row.condition\n", + " inc_edge_id = row.inc_edge\n", + " out_edge_id = row.out_edge\n", + " # match5에서 parent_id에 해당하는 행들을 가져옴\n", + " cmatch = match5.copy()[match5.node_id==parent_id] # match dataframe for a child node\n", + " cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True)\n", + " cmatch['node_id'] = child_id\n", + " cmatch[['inc_edge', 'out_edge']] = np.nan\n", + "\n", + " # condition 별로 inc_dire, out_dire_A, out_dire_B를 정함\n", + " ind = directions.index(direction)\n", + " if condition == \"좌회전시\":\n", + " inc_dire = direction\n", + " out_dire_A = out_dire_B = directions[(ind + 2) % len(directions)]\n", + " elif condition == \"직진시\":\n", + " inc_dire = direction\n", + " out_dire_A = out_dire_B = directions[(ind + 4) % len(directions)]\n", + " elif condition == \"보행신호시\":\n", + " inc_dire = directions[(ind + 2) % len(directions)]\n", + " out_dire_A = directions[(ind - 2) % len(directions)]\n", + " out_dire_B = directions[(ind - 2) % len(directions)]\n", + "\n", + " # (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함\n", + " if condition == '보행신호시':\n", + " cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]\n", + " cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]\n", + " # 이동류번호가 17(보행신호)이면서 유턴노드방향으로 가는 신호가 없으면 (inc_edge_id, out_edge_id)를 부여한다.\n", + " cmatch.loc[(cmatch.move_no==17) & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]\n", + " else: # '직진시', '좌회전시'\n", + " cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]\n", + " cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]\n", + " # 유턴신호의 이동류번호를 19로 부여한다.\n", + " cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), 'move_no'] = 19\n", + " cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), 'move_no'] = 19\n", + " cmatches.append(cmatch)\n", + "\n", + " # 각 coordination node에 대하여 (inc_edge_id, out_edge_id) 부여\n", + " coord['inter_no'] = coord['parent_id'].map(node2inter)\n", + " coord = coord.rename(columns={'child_id':'node_id'})\n", + " coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan\n", + " coord['move_no'] = 20\n", + " coord = coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']]\n", + " \n", + " # display(coord)\n", + " cmatches = pd.concat(cmatches)\n", + " match6 = pd.concat([match5, cmatches, coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type'])\n", + " match6.to_csv('../Intermediates/match6.csv')\n", + " return match6" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def make_matching(match6, inter_node, nema):\n", + " '''\n", + " 이동류 매칭 : 각 교차로에 대하여, 가능한 모든 이동류 (1~18, 21)에 대한 진입·진출엣지ID를 지정한다.\n", + " 모든 이동류에 대해 지정하므로, 시차제시 이전과 다른 이동류가 등장하더라도 항상 진입·진출 엣지 ID를 지정할 수 있다. \n", + " - matching의 컬럼 : inter_no, move_no, inc_dir, out_dir, inc_edge, out_edge, node_id\n", + " \n", + " 설명 : \n", + " - 필요한 리스트, 딕셔너리 등을 정의\n", + " (1) 가능한 (진입방향, 진출방향) 목록 [리스트]\n", + " (2) 각 교차로별 방향 목록 : pdires (possible directions) [딕셔너리]\n", + " (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) [딕셔너리]\n", + " (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) [딕셔너리]\n", + " (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) [딕셔너리]\n", + " - matching은 빈 리스트로 지정.\n", + " - 모든 노드id에 대하여 다음 과정을 반복\n", + " - 해당 노드id에 대한 모든 가능한 (진입방향, 진출방향)에 대하여 다음 과정을 반복\n", + " - (노드id, 진입방향)으로부터 진입엣지id를 얻어냄. 마찬가지로 진출엣지id도 얻어냄\n", + " - 얻어낸 정보를 바탕으로 한 행(new_row)을 만들고 이것을 matching에 append\n", + " '''\n", + "\n", + " match7 = match6.copy()\n", + " match7 = match7[['inter_no', 'move_no', 'inc_dir', 'out_dir', 'inc_edge', 'out_edge', 'node_id']]\n", + "\n", + " parent_ids = sorted(inter_node[inter_node.inter_type=='parent'].node_id.unique())\n", + " child_ids = sorted(inter_node[inter_node.inter_type=='child'].node_id.unique())\n", + "\n", + " # (1) 가능한 (진입방향, 진출방향) 목록 \n", + " flows = nema.dropna().apply(lambda row: (row['inc_dir'], row['out_dir']), axis=1).tolist()\n", + " # (2) 각 교차로별 방향 목록 : pdires (possible directions)\n", + " pdires = {}\n", + " for node_id in parent_ids:\n", + " dires = match7[match7.node_id == node_id][['inc_dir','out_dir']].values.flatten()\n", + " dires = {dire for dire in dires if type(dire)==str}\n", + " pdires[node_id] = dires\n", + " # (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id)\n", + " inc2id = {}\n", + " for node_id in parent_ids:\n", + " for inc_dir in pdires[node_id]:\n", + " df = match7[(match7.node_id==node_id) & (match7.inc_dir==inc_dir)]\n", + " inc2id[(node_id, inc_dir)] = df.inc_edge.iloc[0]\n", + " # (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id)\n", + " out2id = {}\n", + " for node_id in parent_ids:\n", + " for out_dir in pdires[node_id]:\n", + " df = match7[(match7.node_id==node_id) & (match7.out_dir==out_dir)]\n", + " out2id[(node_id, out_dir)] = df.out_edge.iloc[0]\n", + " # (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows)\n", + " pflow = {}\n", + " for node_id in parent_ids:\n", + " pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])]\n", + " # (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching\n", + " node2inter = dict(zip(match7['node_id'], match7['inter_no']))\n", + " dires_right = ['북', '서', '남', '동', '북'] # ex (북, 서), (서, 남) 등은 우회전 flow\n", + " matching = []\n", + " for node_id in parent_ids:\n", + " inter_no = node2inter[node_id]\n", + " # 좌회전과 직진(1 ~ 16)\n", + " for (inc_dir, out_dir) in pflow[node_id]:\n", + " move_no = nema[(nema.inc_dir==inc_dir) & (nema.out_dir==out_dir)].move_no.iloc[0]\n", + " inc_edge = inc2id[(node_id, inc_dir)]\n", + " out_edge = out2id[(node_id, out_dir)]\n", + " new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no],\n", + " 'inc_dir':[inc_dir], 'out_dir':[out_dir],\n", + " 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]})\n", + " matching.append(new_row)\n", + " # 보행신호(17), 전적색(18)\n", + " new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18],\n", + " 'inc_dir':[None]*2, 'out_dir':[None]*2,\n", + " 'inc_edge':[None]*2, 'out_edge':[None]*2, 'node_id':[node_id]*2})\n", + " matching.append(new_row)\n", + " # 신호우회전(21)\n", + " for d in range(len(dires_right)-1):\n", + " inc_dir = dires_right[d]\n", + " out_dir = dires_right[d+1]\n", + " if {inc_dir, out_dir}.issubset(pdires[node_id]):\n", + " inc_edge = inc2id[(node_id, inc_dir)]\n", + " out_edge = out2id[(node_id, out_dir)]\n", + " new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21],\n", + " 'inc_dir':[inc_dir], 'out_dir':[out_dir],\n", + " 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]})\n", + " matching.append(new_row)\n", + " matching.append(match7[match7.node_id.isin(child_ids)])\n", + " matching = pd.concat(matching)\n", + " matching = matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True)\n", + " matching['move_no'] = matching['move_no'].astype(int)\n", + " matching.to_csv('../Intermediates/matching.csv')\n", + " return matching" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def make_movements():\n", + " movements_path = '../Intermediates/movement/'\n", + " movements_list = [pd.read_csv(movements_path + file, index_col=0) for file in tqdm(os.listdir(movements_path))]\n", + " movements = pd.concat(movements_list)\n", + " movements = movements.drop(columns=['start_unix'])\n", + " movements = movements.drop_duplicates()\n", + " movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B'])\n", + " movements = movements.reset_index(drop=True)\n", + " movements.to_csv('../Intermediates/movements.csv')\n", + " return movements" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# node2num_cycles : A dictionary that maps a node_id to the number of cycles\n", + "def get_node2num_cycles(plan, inter_node):\n", + " node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no']))\n", + " node_ids = sorted(inter_node.node_id.unique())\n", + "\n", + " Aplan = plan.copy()[['inter_no'] + [f'dura_A{j}' for j in range(1,9)] + ['cycle']]\n", + " grouped = Aplan.groupby('inter_no')\n", + " df = grouped.agg({'cycle': 'min'}).reset_index()\n", + " df = df.rename(columns={'cycle': 'min_cycle'})\n", + " df['num_cycle'] = 300 // df['min_cycle'] + 2\n", + " inter2num_cycles = dict(zip(df['inter_no'], df['num_cycle']))\n", + " node2numcycles = {node_id : inter2num_cycles[node2inter[node_id]] for node_id in node_ids}\n", + " with open('../Intermediates/node2numcycles.json', 'w') as file:\n", + " json.dump(node2numcycles, file, indent=4)\n", + " return node2numcycles" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def preprocess_daily():\n", + " path_move = '../Data/tables/move/'\n", + "\n", + " inter_info = pd.read_csv('../Data/tables/inter_info.csv', index_col=0)\n", + " angle = pd.read_csv('../Data/tables/angle.csv', index_col=0, dtype = {f'angle_{alph}{j}':'str' for alph in ['A', 'B'] for j in range(1,9)})\n", + " plan = pd.read_csv('../Data/tables/plan.csv', index_col=0)\n", + " inter_node = pd.read_csv('../Data/tables/inter_node.csv', index_col=0)\n", + " uturn = pd.read_csv('../Data/tables/child_uturn.csv')\n", + " coord = pd.read_csv('../Data/tables/child_coord.csv')\n", + " nema = pd.read_csv('../Data/tables/nema.csv', encoding='cp949')\n", + "\n", + " net = sumolib.net.readNet('../Data/networks/sn.net.xml')\n", + "\n", + " match1 = make_match1(path_move)\n", + " match2 = make_match2(match1)\n", + " match3 = make_match3(match2, nema)\n", + " match4 = make_match4(match3, angle)\n", + " match5 = make_match5(match4, net, inter_node, inter_info)\n", + " match6 = make_match6(match5, inter_node, uturn, coord)\n", + " matching = make_matching(match6, inter_node, nema)\n", + " movements = make_movements()\n", + " node2num_cycles = get_node2num_cycles(plan, inter_node)\n", + " return match6, matching, movements, node2num_cycles" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/17280 [00:00= 0) & (durations <= 100)).all(axis=1)\n", + "sorted(plan[~valid_indices].inter_no.unique())\n", + "# durations" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rts", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Script/preprocess_daily.py b/Script/preprocess_daily.py new file mode 100644 index 000000000..f0de51881 --- /dev/null +++ b/Script/preprocess_daily.py @@ -0,0 +1,431 @@ +import pandas as pd +import numpy as np +import os +import json +import sumolib +from tqdm import tqdm + +def check_inter_info(inter_info): + print(inter_info) + print('check') + +def make_match1(path_root): + ''' + 신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다. + '../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다. + + return : 통합된 이동류정보 + - 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보 + + match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다. + ''' + # [이동류번호] 불러오기 (약 1분의 소요시간) + path_move = os.path.join(path_root, 'Data', 'tables', 'move') + csv_moves = os.listdir(path_move) + moves = [pd.read_csv(os.path.join(path_move, csv_move), index_col=0) for csv_move in tqdm(csv_moves, desc='이동류정보 불러오는 중 : match1')] + match1 = pd.concat(moves).drop_duplicates().sort_values(by=['inter_no','phas_A','phas_B']).reset_index(drop=True) + match1.to_csv(os.path.join(path_root, 'Intermediates', 'match1.csv')) + return match1 + +def make_match2(match1): + ''' + match1을 계층화함. + - match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + ''' + # 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no') + matchA = match1[['inter_no', 'phas_A', 'move_A']].copy() + matchA.columns = ['inter_no', 'phase_no', 'move_no'] + matchA['ring_type'] = 'A' + matchB = match1[['inter_no', 'phas_B', 'move_B']].copy() + matchB.columns = ['inter_no', 'phase_no', 'move_no'] + matchB['ring_type'] = 'B' + match2 = pd.concat([matchA, matchB]).drop_duplicates() + match2 = match2[['inter_no', 'phase_no', 'ring_type', 'move_no']] + match2 = match2.sort_values(by=list(match2.columns)) + return match2 + +def make_match3(match2, nema): + ''' + 각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함. + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + + nema : + - 컬럼 : move_no, inc_dir, out_dir + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + # nema 정보 불러오기 및 병합 + match3 = pd.merge(match2, nema, how='left', on='move_no').drop_duplicates() + return match3 + +def make_match4(match3, angle): + ''' + 방위각 정보를 매칭시켜 추가함. + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + + angle_original : + - 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8) + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + + # 계층화 + angles = [] + for i, row in angle.iterrows(): + angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] + new = pd.DataFrame({'inter_no':[row.inter_no] * 16, 'phase_no':list(range(1, 9))*2, 'ring_type':['A'] * 8 + ['B'] * 8, 'angle_code':angle_codes.to_list()}) + angles.append(new) + angles = pd.concat(angles) + angles = angles.dropna().reset_index(drop=True) + + # 병합 + six_chars = angles.angle_code.apply(lambda x:len(x)==6) + angles.loc[six_chars,'inc_angle'] = angles.angle_code.apply(lambda x:x[:3]) + angles.loc[six_chars,'out_angle'] = angles.angle_code.apply(lambda x:x[3:]) + angles = angles.drop('angle_code', axis=1) + match4 = pd.merge(match3, angles, how='left', left_on=['inter_no', 'phase_no', 'ring_type'], + right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates() + return match4 + +def make_match5(match4, net, inter_node, inter_info): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로). + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + - match5의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) net + - 성남시 정자동 부근의 샘플 네트워크 + (2) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (3) inter_info + - 교차로 정보. 여기에서는 위도와 경도가 쓰임. + - 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no + + 진입엣지id, 진출엣지id를 얻는 과정 : + - match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함. + * 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음 + - 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장. + * inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge + - inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄. + - 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄. + - 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함. + ''' + + # parent node만 가져옴. + inter_node1 = inter_node[inter_node.inter_type == 'parent'].drop('inter_type', axis=1) + inter_info1 = inter_info[['inter_no', 'inter_lat', 'inter_lon']] + inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'], + right_on=['inter_no']).drop_duplicates() + + inter2node = dict(zip(inter['inter_no'], inter['node_id'])) + + match5 = match4.copy() + # 진입진출ID 매칭 + for index, row in match5.iterrows(): + node_id = inter2node[row.inter_no] + node = net.getNode(node_id) + # 교차로의 모든 (from / to) edges + inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges + out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges + # 교차로의 모든 (from / to) directions + inc_dirs = [] + for inc_edge in inc_edges: + start = inc_edge.getShape()[-2] + end = inc_edge.getShape()[-1] + inc_dir = np.array(end) - np.array(start) + inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5 + inc_dirs.append(inc_dir) + out_dirs = [] + for out_edge in out_edges: + start = out_edge.getShape()[0] + end = out_edge.getShape()[1] + out_dir = np.array(end) - np.array(start) + out_dir = out_dir / (out_dir ** 2).sum() ** 0.5 + out_dirs.append(out_dir) + # 진입각, 진출각 불러오기 + if not pd.isna(row.inc_angle): + inc_angle = int(row.inc_angle) + out_angle = int(row.out_angle) + # 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환 + inc_angle = (-90 - inc_angle) % 360 + inc_angle = inc_angle * np.pi / 180. + inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)]) + out_angle = (90 - out_angle) % 360 + out_angle = out_angle * np.pi / 180. + out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)]) + # 매칭 엣지 반환 + inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax() + out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax() + inc_edge_id = inc_edges[inc_index].getID() + out_edge_id = out_edges[out_index].getID() + match5.at[index, 'inc_edge'] = inc_edge_id + match5.at[index, 'out_edge'] = out_edge_id + match5['node_id'] = match5['inter_no'].map(inter2node) + match5 = match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True) + return match5 + +def make_match6(match5, inter_node, uturn, coord, path_root): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로). + - match6의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (2) uturn (유턴정보) + - 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 유턴교차로id + - direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향) + - condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나 + - inc_edge, out_edge : 유턴에 대한 진입진출엣지 + (3) coord (연동교차로정보) + - 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 연동교차로id + - 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지 + + 설명 : + - match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음. + 여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함. + 유턴교차로 : + - directions를 정북기준 시계방향의 8방위로 정함. + - 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음. + - 예) 진입방향(direction)이 '북'일 때, + - 직진 : (북, 남) + * 남 : directions[(ind + 4) % len(directions)] + - 좌회전 : (북, 동) + * 동 : directions[(ind + 2) % len(directions)] + - 보행 : (서, 동) + * 서 : directions[(ind - 2) % len(directions)] + - uturn의 각 행을 순회하면서 아래 과정을 반복함 + - match5에서 parent_id에 해당하는 행들을 가져옴(cmatch). + - condition 별로 진입방향, 진출방향A, 진출방향B 정함. + - 상술한 directions를 활용하여 정함. + - (진입방향, 진출방향A, 진출방향B)을 고려하여 (현시, 링) 별로 진입엣지id, 진출엣지id를 정함. + - ex) cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + - 순회하면서 만든 cmatch를 cmatchs라는 리스트에 저장함. + + 연동교차로 : + - 연동교차로의 경우 coord에 (현시, 링)별 진입엣지ID, 진출엣지ID가 명시되어 있음. + - 'inc_dir', 'out_dir', 'inc_angle','out_angle'와 같은 열들은 np.nan을 지정해놓음. + - 이 열들은, 사실상 다음 스텝부터는 사용되지 않는 열들이기 때문에 np.nan으로 지정해놓아도 문제없음. + + match6 : + - 이렇게 얻은 match5, cmatchs, coord를 모두 pd.concat하여 match6을 얻어냄. + ''' + + node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no'])) + + child_ids = inter_node[inter_node.inter_type=='child'].node_id.unique() + ch2pa = {} # child to parent + for child_id in child_ids: + parent_no = inter_node[inter_node.node_id==child_id].inter_no.iloc[0] + sub_inter_node = inter_node[inter_node.inter_no==parent_no] + ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id + directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향 + + # 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여 + cmatches = [] + for _, row in uturn.iterrows(): + child_id = row.child_id + parent_id = row.parent_id + direction = row.direction + condition = row.condition + inc_edge_id = row.inc_edge + out_edge_id = row.out_edge + # match5에서 parent_id에 해당하는 행들을 가져옴 + cmatch = match5.copy()[match5.node_id==parent_id] # match dataframe for a child node + cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True) + cmatch['node_id'] = child_id + cmatch[['inc_edge', 'out_edge']] = np.nan + + # condition 별로 inc_dire, out_dire_A, out_dire_B를 정함 + ind = directions.index(direction) + if condition == "좌회전시": + inc_dire = direction + out_dire_A = out_dire_B = directions[(ind + 2) % len(directions)] + elif condition == "직진시": + inc_dire = direction + out_dire_A = out_dire_B = directions[(ind + 4) % len(directions)] + elif condition == "보행신호시": + inc_dire = directions[(ind + 2) % len(directions)] + out_dire_A = directions[(ind - 2) % len(directions)] + out_dire_B = directions[(ind - 2) % len(directions)] + + # (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함 + if condition == '보행신호시': + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 이동류번호가 17(보행신호)이면서 유턴노드방향으로 가는 신호가 없으면 (inc_edge_id, out_edge_id)를 부여한다. + cmatch.loc[(cmatch.move_no==17) & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + else: # '직진시', '좌회전시' + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 유턴신호의 이동류번호를 19로 부여한다. + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), 'move_no'] = 19 + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), 'move_no'] = 19 + cmatches.append(cmatch) + + # 각 coordination node에 대하여 (inc_edge_id, out_edge_id) 부여 + coord['inter_no'] = coord['parent_id'].map(node2inter) + coord = coord.rename(columns={'child_id':'node_id'}) + coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan + coord['move_no'] = 20 + coord = coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']] + + # display(coord) + cmatches = pd.concat(cmatches) + match6 = pd.concat([match5, cmatches, coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type']) + match6.to_csv(os.path.join(path_root, 'Intermediates', 'match6.csv')) + return match6 + +def make_matching(match6, inter_node, nema, path_root): + ''' + 이동류 매칭 : 각 교차로에 대하여, 가능한 모든 이동류 (1~18, 21)에 대한 진입·진출엣지ID를 지정한다. + 모든 이동류에 대해 지정하므로, 시차제시 이전과 다른 이동류가 등장하더라도 항상 진입·진출 엣지 ID를 지정할 수 있다. + - matching의 컬럼 : inter_no, move_no, inc_dir, out_dir, inc_edge, out_edge, node_id + + 설명 : + - 필요한 리스트, 딕셔너리 등을 정의 + (1) 가능한 (진입방향, 진출방향) 목록 [리스트] + (2) 각 교차로별 방향 목록 : pdires (possible directions) [딕셔너리] + (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) [딕셔너리] + (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) [딕셔너리] + (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) [딕셔너리] + - matching은 빈 리스트로 지정. + - 모든 노드id에 대하여 다음 과정을 반복 + - 해당 노드id에 대한 모든 가능한 (진입방향, 진출방향)에 대하여 다음 과정을 반복 + - (노드id, 진입방향)으로부터 진입엣지id를 얻어냄. 마찬가지로 진출엣지id도 얻어냄 + - 얻어낸 정보를 바탕으로 한 행(new_row)을 만들고 이것을 matching에 append + ''' + + match7 = match6.copy() + match7 = match7[['inter_no', 'move_no', 'inc_dir', 'out_dir', 'inc_edge', 'out_edge', 'node_id']] + + parent_ids = sorted(inter_node[inter_node.inter_type=='parent'].node_id.unique()) + child_ids = sorted(inter_node[inter_node.inter_type=='child'].node_id.unique()) + + # (1) 가능한 (진입방향, 진출방향) 목록 + flows = nema.dropna().apply(lambda row: (row['inc_dir'], row['out_dir']), axis=1).tolist() + # (2) 각 교차로별 방향 목록 : pdires (possible directions) + pdires = {} + for node_id in parent_ids: + dires = match7[match7.node_id == node_id][['inc_dir','out_dir']].values.flatten() + dires = {dire for dire in dires if type(dire)==str} + pdires[node_id] = dires + # (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) + inc2id = {} + for node_id in parent_ids: + for inc_dir in pdires[node_id]: + df = match7[(match7.node_id==node_id) & (match7.inc_dir==inc_dir)] + inc2id[(node_id, inc_dir)] = df.inc_edge.iloc[0] + # (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) + out2id = {} + for node_id in parent_ids: + for out_dir in pdires[node_id]: + df = match7[(match7.node_id==node_id) & (match7.out_dir==out_dir)] + out2id[(node_id, out_dir)] = df.out_edge.iloc[0] + # (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) + pflow = {} + for node_id in parent_ids: + pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])] + # (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching + node2inter = dict(zip(match7['node_id'], match7['inter_no'])) + dires_right = ['북', '서', '남', '동', '북'] # ex (북, 서), (서, 남) 등은 우회전 flow + matching = [] + for node_id in parent_ids: + inter_no = node2inter[node_id] + # 좌회전과 직진(1 ~ 16) + for (inc_dir, out_dir) in pflow[node_id]: + move_no = nema[(nema.inc_dir==inc_dir) & (nema.out_dir==out_dir)].move_no.iloc[0] + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + matching.append(new_row) + # 보행신호(17), 전적색(18) + new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18], + 'inc_dir':[None]*2, 'out_dir':[None]*2, + 'inc_edge':[None]*2, 'out_edge':[None]*2, 'node_id':[node_id]*2}) + matching.append(new_row) + # 신호우회전(21) + for d in range(len(dires_right)-1): + inc_dir = dires_right[d] + out_dir = dires_right[d+1] + if {inc_dir, out_dir}.issubset(pdires[node_id]): + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + matching.append(new_row) + matching.append(match7[match7.node_id.isin(child_ids)]) + matching = pd.concat(matching) + matching = matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True) + matching['move_no'] = matching['move_no'].astype(int) + matching.to_csv(os.path.join(path_root, 'Intermediates', 'matching.csv')) + return matching + +def make_movements(path_root): + movements_path = os.path.join(path_root, 'Intermediates', 'movement') + movements_list = [pd.read_csv(os.path.join(movements_path, file), index_col=0) for file in tqdm(os.listdir(movements_path), desc='이동류정보 불러오는 중 : movements')] + movements = pd.concat(movements_list) + movements = movements.drop(columns=['start_unix']) + movements = movements.drop_duplicates() + movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B']) + movements = movements.reset_index(drop=True) + movements.to_csv(os.path.join(path_root, 'Intermediates', 'movements.csv')) + return movements + +# node2num_cycles : A dictionary that maps a node_id to the number of cycles +def get_node2num_cycles(plan, inter_node, path_root): + node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no'])) + node_ids = sorted(inter_node.node_id.unique()) + + Aplan = plan.copy()[['inter_no'] + [f'dura_A{j}' for j in range(1,9)] + ['cycle']] + grouped = Aplan.groupby('inter_no') + df = grouped.agg({'cycle': 'min'}).reset_index() + df = df.rename(columns={'cycle': 'min_cycle'}) + df['num_cycle'] = 300 // df['min_cycle'] + 2 + inter2num_cycles = dict(zip(df['inter_no'], df['num_cycle'])) + node2numcycles = {node_id : inter2num_cycles[node2inter[node_id]] for node_id in node_ids} + with open(os.path.join('Intermediates','node2numcycles.json'), 'w') as file: + json.dump(node2numcycles, file, indent=4) + return node2numcycles + +def main(): + path_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + inter_info = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'inter_info.csv')) + check_inter_info(inter_info) + angle = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'angle.csv'), dtype = {f'angle_{alph}{j}':'str' for alph in ['A', 'B'] for j in range(1,9)}) + plan = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'plan.csv')) + inter_node = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'inter_node.csv')) + uturn = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'child_uturn.csv')) + coord = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'child_coord.csv')) + nema = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'nema.csv'), encoding='cp949') + + net = sumolib.net.readNet(os.path.join(path_root, 'Data', 'networks', 'sn.net.xml')) + + match1 = make_match1(path_root) + match2 = make_match2(match1) + match3 = make_match3(match2, nema) + match4 = make_match4(match3, angle) + match5 = make_match5(match4, net, inter_node, inter_info) + match6 = make_match6(match5, inter_node, uturn, coord, path_root) + matching = make_matching(match6, inter_node, nema, path_root) + movements = make_movements(path_root) + node2num_cycles = get_node2num_cycles(plan, inter_node, path_root) + +if __name__ == '__main__': + main() diff --git a/Script/preprocess_daily_1.py b/Script/preprocess_daily_1.py new file mode 100644 index 000000000..23f80587a --- /dev/null +++ b/Script/preprocess_daily_1.py @@ -0,0 +1,587 @@ +import pandas as pd +import numpy as np +import os, sys, traci +import json +import sumolib +from tqdm import tqdm + +class DailyPreprocessor(): + def __init__(self): + self.path_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + self.issues = [] + + # 1. 데이터 불러오기 + def load_data(self): + self.load_networks() + self.load_tables() + self.check_networks() + self.check_tables() + print('1. 모든 데이터가 로드되었습니다.') + + # 1-1. 네트워크 불러오기 + def load_networks(self): + self.net = sumolib.net.readNet(os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml')) + print("1-1. 네트워크가 로드되었습니다.") + + # 1-2. 테이블 불러오기 + def load_tables(self): + # 모든 컬럼에 대하여 데이터타입 지정 + loading_dtype = { + 'inter_no':'int', 'start_hour':'int', 'start_minute':'int', 'cycle':'int','offset':'int', + 'node_id':'str', 'inter_type':'str', 'parent_id':'str','child_id':'str', + 'direction':'str', 'condition':'str', 'inc_edge':'str', 'out_edge':'str', + 'end_unix':'int', 'inter_name':'str', 'inter_lat':'float', 'inter_lon':'float', + 'group_no':'int', 'main_phase_no':'int', 'phase_no':'int','ring_type':'str' + } + for alph in ['A', 'B']: + for j in range(1,9): + loading_dtype[f'angle_{alph}{j}'] = 'str' + loading_dtype[f'dura_{alph}{j}'] = 'int' + + self.path_table = os.path.join(self.path_root, 'Data', 'tables') + + self.inter_info = pd.read_csv(os.path.join(self.path_table, 'inter_info.csv'), dtype=loading_dtype) + self.angle = pd.read_csv(os.path.join(self.path_table, 'angle.csv'), dtype=loading_dtype) + self.plan = pd.read_csv(os.path.join(self.path_table, 'plan.csv'), dtype=loading_dtype) + self.inter_node = pd.read_csv(os.path.join(self.path_table, 'inter_node.csv'), dtype=loading_dtype) + self.uturn = pd.read_csv(os.path.join(self.path_table, 'child_uturn.csv'), dtype=loading_dtype) + self.coord = pd.read_csv(os.path.join(self.path_table, 'child_coord.csv'), dtype=loading_dtype) + self.nema = pd.read_csv(os.path.join(self.path_table, 'nema.csv'), encoding='cp949', dtype=loading_dtype) + print("1-2. 테이블들이 로드되었습니다.") + + # 1-3. 테이블 불러오기 + def check_networks(self): + # https://sumo.dlr.de/docs/Netedit/neteditUsageExamples.html#simplify_tls_program_state_after_changing_connections + if 'SUMO_HOME' in os.environ: + tools = os.path.join(os.environ['SUMO_HOME'], 'tools') + if tools not in sys.path: + sys.path.append(tools) + else: + raise EnvironmentError("please declare environment variable 'SUMO_HOME'") + traci.start([sumolib.checkBinary('sumo'), "-n", os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml')]) + nodes = [node for node in self.net.getNodes() if node.getType()=='traffic_light'] + for node in nodes: + node_id = node.getID() + from_xml = len([c for c in node.getConnections() if c.getTLLinkIndex() >= 0]) + from_traci = len(traci.trafficlight.getRedYellowGreenState(node_id)) + if from_xml != from_traci: + sub = {'id': node_id, 'type': 'node', 'note': '유효하지 않은 연결이있음. netedit에서 clean states 필요.'} + self.issues.append(sub) + traci.close() + print("1-3. 네트워크의 모든 clean state requirement들을 체크했습니다.") + + # 1-4. 테이블의 무결성 검사 + def check_tables(self): + self.check_inter_info() + self.check_angle() + self.check_plan() + print("1-4. 모든 테이블들의 무결성을 검사했고 이상 없습니다.") + pass + + # 1-4-1. 교차로정보(inter_info) 검사 + def check_inter_info(self): + # 1-4-1-1. inter_lat, inter_lon 적절성 검사 + self.inter_info.loc[0, 'inter_lat'] = 38.0 # 에러 발생을 위한 코드 + self.max_lon, self.min_lon = 127.207888, 127.012492 + self.max_lat, self.min_lat = 37.480693, 37.337112 + for _, row in self.inter_info.iterrows(): + latbool = self.min_lat <= row['inter_lat'] <= self.max_lat + lonbool = self.min_lon <= row['inter_lon'] <= self.max_lon + if not(latbool and lonbool): + msg = f"1-4-1-1. 위도 또는 경도가 범위를 벗어난 교차로가 있습니다: inter_no : {row['inter_no']}" + self.issues.append(msg) + # 교차로목록 정의 + self.inter_nos = sorted(self.inter_info.inter_no.unique()) + + # 1-4-2. 방위각정보(inter_info) 검사 + def check_angle(self): + # 1-4-2-1. inter_no 검사 + # self.angle.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드 + missing_inter_nos = set(self.angle.inter_no) - set(self.inter_nos) + if missing_inter_nos: + msg = f"1-4-2-1. angle의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}" + self.issues.append(msg) + + # 1-4-3. 신호계획(plan) 검사 + def check_plan(self): + # 1-4-3-1. inter_no 검사 + # self.plan.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드 + missing_inter_nos = set(self.plan.inter_no) - set(self.inter_nos) + if missing_inter_nos: + msg = f"1-4-3-1. plan의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}" + self.issues.append(msg) + + # 1-4-3-2. 시작시각 검사 + # self.plan.loc[0, 'start_hour'] = 27 # 에러 발생을 위한 코드 + for _, row in self.plan.iterrows(): + start_hour = row.start_hour + start_minute = row.start_minute + if not (0 <= start_hour <= 23) or not (0 <= start_minute <= 59): + msg = f"1-4-3-2. plan에 잘못된 형식의 start_time이 존재합니다: {start_hour, start_minute}" + self.issues.append(msg) + + # 1-4-3-3. 현시시간 검사 + # self.plan.loc[0, 'dura_A1'] = -2 # 에러 발생을 위한 코드 + durations = self.plan[[f'dura_{alph}{j}' for alph in ['A','B'] for j in range(1, 9)]] + valid_indices = ((durations >= 0) & (durations <= 200)).all(axis=1) + invalid_inter_nos = sorted(self.plan[~ valid_indices].inter_no.unique()) + if invalid_inter_nos: + msg = f"1-4-3-3. 음수이거나 200보다 큰 현시시간이 존재합니다. : {invalid_inter_nos}" + + # 1-4-3-4. 주기 일관성 검사 + # self.plan.loc[0, 'cycle'] = 50 # 에러 발생을 위한 코드 + inconsistent_cycle = self.plan.groupby(['inter_no', 'start_hour', 'start_minute'])['cycle'].nunique().gt(1) + if inconsistent_cycle.any(): + inc_inter_no, start_hour, start_minute = inconsistent_cycle[inconsistent_cycle].index[0] + msg = f"1-4-3-4. inter_no:{inc_inter_no}, start_hour:{start_minute}, start_hour:{start_minute}일 때, cycle이 유일하게 결정되지 않습니다." + self.issues.append(msg) + + # 1-4-3-5. 현시시간 / 주기 검사 + # self.plan.loc[0, 'duration'] = 10 # 에러 발생을 위한 코드 + right_duration = True + for (inter_no, start_hour, start_minute), group in self.plan.groupby(['inter_no', 'start_hour', 'start_minute']): + A_sum = group[[f'dura_A{j}' for j in range(1, 9)]].iloc[0].sum() + B_sum = group[[f'dura_B{j}' for j in range(1, 9)]].iloc[0].sum() + # A_sum = group[group['ring_type']=='A']['duration'].sum() + # B_sum = group[group['ring_type']=='B']['duration'].sum() + cycle = group['cycle'].unique()[0] + if not (A_sum == B_sum == cycle): + right_duration = False + inc_inter_no = inter_no + if not right_duration: + msg = f"1-4-4-5. inter_no:{inc_inter_no}, A링현시시간의 합과 B링현시시간의 합이 일치하지 않거나, 현시시간의 합과 주기가 일치하지 않습니다." + self.issues.append(msg) + + # 2. 중간산출물 만들기 + def get_intermediates(self): + self.get_matches() + # self.get_movements() + self.get_node2num_cycles() + + # 2-1 매칭테이블들 생성 + def get_matches(self): + self.make_match1() + self.make_match2() + self.make_match3() + self.make_match4() + self.make_match5() + self.make_match6() + self.make_matching() + + # 2-1-1 + def make_match1(self): + ''' + 신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다. + '../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다. + + return : 통합된 이동류정보 + - 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보 + + match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다. + ''' + # [이동류번호] 불러오기 (약 1분의 소요시간) + path_move = os.path.join(self.path_root, 'Data', 'tables', 'move') + csv_moves = os.listdir(path_move) + moves = [pd.read_csv(os.path.join(path_move, csv_move), index_col=0) for csv_move in tqdm(csv_moves, desc='이동류정보 불러오는 중 : match1')] + self.match1 = pd.concat(moves).drop_duplicates().sort_values(by=['inter_no','phas_A','phas_B']).reset_index(drop=True) + self.match1.to_csv(os.path.join(self.path_root, 'Intermediates', 'match1.csv')) + + # 2-1-2 + def make_match2(self): + ''' + match1을 계층화함. + - match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + ''' + # 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no') + matchA = self.match1[['inter_no', 'phas_A', 'move_A']].copy() + matchA.columns = ['inter_no', 'phase_no', 'move_no'] + matchA['ring_type'] = 'A' + matchB = self.match1[['inter_no', 'phas_B', 'move_B']].copy() + matchB.columns = ['inter_no', 'phase_no', 'move_no'] + matchB['ring_type'] = 'B' + self.match2 = pd.concat([matchA, matchB]).drop_duplicates() + self.match2 = self.match2[['inter_no', 'phase_no', 'ring_type', 'move_no']] + self.match2 = self.match2.sort_values(by=list(self.match2.columns)) + + # 2-1-3 + def make_match3(self): + ''' + 각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함. + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + + nema : + - 컬럼 : move_no, inc_dir, out_dir + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + # nema 정보 불러오기 및 병합 + self.match3 = pd.merge(self.match2, self.nema, how='left', on='move_no').drop_duplicates() + + # 2-1-4 + def make_match4(self): + ''' + 방위각 정보를 매칭시켜 추가함. + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + + angle_original : + - 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8) + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + + # 계층화 + angles = [] + for i, row in self.angle.iterrows(): + angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] + new = pd.DataFrame({'inter_no':[row.inter_no] * 16, 'phase_no':list(range(1, 9))*2, 'ring_type':['A'] * 8 + ['B'] * 8, 'angle_code':angle_codes.to_list()}) + angles.append(new) + angles = pd.concat(angles) + angles = angles.dropna().reset_index(drop=True) + + # 병합 + six_chars = angles.angle_code.apply(lambda x:len(x)==6) + angles.loc[six_chars,'inc_angle'] = angles.angle_code.apply(lambda x:x[:3]) + angles.loc[six_chars,'out_angle'] = angles.angle_code.apply(lambda x:x[3:]) + angles = angles.drop('angle_code', axis=1) + self.match4 = pd.merge(self.match3, angles, how='left', left_on=['inter_no', 'phase_no', 'ring_type'], + right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates() + + # 2-1-5 + def make_match5(self): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로). + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + - match5의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) net + - 성남시 정자동 부근의 샘플 네트워크 + (2) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (3) inter_info + - 교차로 정보. 여기에서는 위도와 경도가 쓰임. + - 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no + + 진입엣지id, 진출엣지id를 얻는 과정 : + - match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함. + * 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음 + - 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장. + * inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge + - inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄. + - 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄. + - 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함. + ''' + + # parent node만 가져옴. + inter_node1 = self.inter_node[self.inter_node.inter_type == 'parent'].drop('inter_type', axis=1) + inter_info1 = self.inter_info[['inter_no', 'inter_lat', 'inter_lon']] + inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'], + right_on=['inter_no']).drop_duplicates() + + self.inter2node = dict(zip(inter['inter_no'], inter['node_id'])) + + self.match5 = self.match4.copy() + # 진입진출ID 매칭 + for index, row in self.match5.iterrows(): + node_id = self.inter2node[row.inter_no] + node = self.net.getNode(node_id) + # 교차로의 모든 (from / to) edges + inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges + out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges + # 교차로의 모든 (from / to) directions + inc_dirs = [] + for inc_edge in inc_edges: + start = inc_edge.getShape()[-2] + end = inc_edge.getShape()[-1] + inc_dir = np.array(end) - np.array(start) + inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5 + inc_dirs.append(inc_dir) + out_dirs = [] + for out_edge in out_edges: + start = out_edge.getShape()[0] + end = out_edge.getShape()[1] + out_dir = np.array(end) - np.array(start) + out_dir = out_dir / (out_dir ** 2).sum() ** 0.5 + out_dirs.append(out_dir) + # 진입각, 진출각 불러오기 + if not pd.isna(row.inc_angle): + inc_angle = int(row.inc_angle) + out_angle = int(row.out_angle) + # 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환 + inc_angle = (-90 - inc_angle) % 360 + inc_angle = inc_angle * np.pi / 180. + inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)]) + out_angle = (90 - out_angle) % 360 + out_angle = out_angle * np.pi / 180. + out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)]) + # 매칭 엣지 반환 + inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax() + out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax() + inc_edge_id = inc_edges[inc_index].getID() + out_edge_id = out_edges[out_index].getID() + self.match5.at[index, 'inc_edge'] = inc_edge_id + self.match5.at[index, 'out_edge'] = out_edge_id + self.match5['node_id'] = self.match5['inter_no'].map(self.inter2node) + self.match5 = self.match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True) + + # 2-1-6 + def make_match6(self): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로). + - match6의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (2) uturn (유턴정보) + - 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 유턴교차로id + - direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향) + - condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나 + - inc_edge, out_edge : 유턴에 대한 진입진출엣지 + (3) coord (연동교차로정보) + - 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 연동교차로id + - 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지 + + 설명 : + - match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음. + 여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함. + 유턴교차로 : + - directions를 정북기준 시계방향의 8방위로 정함. + - 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음. + - 예) 진입방향(direction)이 '북'일 때, + - 직진 : (북, 남) + * 남 : directions[(ind + 4) % len(directions)] + - 좌회전 : (북, 동) + * 동 : directions[(ind + 2) % len(directions)] + - 보행 : (서, 동) + * 서 : directions[(ind - 2) % len(directions)] + - uturn의 각 행을 순회하면서 아래 과정을 반복함 + - match5에서 parent_id에 해당하는 행들을 가져옴(cmatch). + - condition 별로 진입방향, 진출방향A, 진출방향B 정함. + - 상술한 directions를 활용하여 정함. + - (진입방향, 진출방향A, 진출방향B)을 고려하여 (현시, 링) 별로 진입엣지id, 진출엣지id를 정함. + - ex) cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + - 순회하면서 만든 cmatch를 cmatchs라는 리스트에 저장함. + + 연동교차로 : + - 연동교차로의 경우 coord에 (현시, 링)별 진입엣지ID, 진출엣지ID가 명시되어 있음. + - 'inc_dir', 'out_dir', 'inc_angle','out_angle'와 같은 열들은 np.nan을 지정해놓음. + - 이 열들은, 사실상 다음 스텝부터는 사용되지 않는 열들이기 때문에 np.nan으로 지정해놓아도 문제없음. + + match6 : + - 이렇게 얻은 match5, cmatchs, coord를 모두 pd.concat하여 match6을 얻어냄. + ''' + + self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no'])) + + child_ids = self.inter_node[self.inter_node.inter_type=='child'].node_id.unique() + ch2pa = {} # child to parent + for child_id in child_ids: + parent_no = self.inter_node[self.inter_node.node_id==child_id].inter_no.iloc[0] + sub_inter_node = self.inter_node[self.inter_node.inter_no==parent_no] + ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id + directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향 + + # 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여 + cmatches = [] + for _, row in self.uturn.iterrows(): + child_id = row.child_id + parent_id = row.parent_id + direction = row.direction + condition = row.condition + inc_edge_id = row.inc_edge + out_edge_id = row.out_edge + # match5에서 parent_id에 해당하는 행들을 가져옴 + cmatch = self.match5.copy()[self.match5.node_id==parent_id] # match dataframe for a child node + cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True) + cmatch['node_id'] = child_id + cmatch[['inc_edge', 'out_edge']] = np.nan + + # condition 별로 inc_dire, out_dire_A, out_dire_B를 정함 + ind = directions.index(direction) + if condition == "좌회전시": + inc_dire = direction + out_dire_A = out_dire_B = directions[(ind + 2) % len(directions)] + elif condition == "직진시": + inc_dire = direction + out_dire_A = out_dire_B = directions[(ind + 4) % len(directions)] + elif condition == "보행신호시": + inc_dire = directions[(ind + 2) % len(directions)] + out_dire_A = directions[(ind - 2) % len(directions)] + out_dire_B = directions[(ind - 2) % len(directions)] + + # (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함 + if condition == '보행신호시': + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 이동류번호가 17(보행신호)이면서 유턴노드방향으로 가는 신호가 없으면 (inc_edge_id, out_edge_id)를 부여한다. + cmatch.loc[(cmatch.move_no==17) & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + else: # '직진시', '좌회전시' + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 유턴신호의 이동류번호를 19로 부여한다. + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), 'move_no'] = 19 + cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), 'move_no'] = 19 + cmatches.append(cmatch) + + # 각 coordination node에 대하여 (inc_edge_id, out_edge_id) 부여 + self.coord['inter_no'] = self.coord['parent_id'].map(self.node2inter) + self.coord = self.coord.rename(columns={'child_id':'node_id'}) + self.coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan + self.coord['move_no'] = 20 + self.coord = self.coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']] + + # display(coord) + cmatches = pd.concat(cmatches) + self.match6 = pd.concat([self.match5, cmatches, self.coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type']) + self.match6.to_csv(os.path.join(self.path_root, 'Intermediates', 'match6.csv')) + + # 2-1-7 + def make_matching(self): + ''' + 이동류 매칭 : 각 교차로에 대하여, 가능한 모든 이동류 (1~18, 21)에 대한 진입·진출엣지ID를 지정한다. + 모든 이동류에 대해 지정하므로, 시차제시 이전과 다른 이동류가 등장하더라도 항상 진입·진출 엣지 ID를 지정할 수 있다. + - matching의 컬럼 : inter_no, move_no, inc_dir, out_dir, inc_edge, out_edge, node_id + + 설명 : + - 필요한 리스트, 딕셔너리 등을 정의 + (1) 가능한 (진입방향, 진출방향) 목록 [리스트] + (2) 각 교차로별 방향 목록 : pdires (possible directions) [딕셔너리] + (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) [딕셔너리] + (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) [딕셔너리] + (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) [딕셔너리] + - matching은 빈 리스트로 지정. + - 모든 노드id에 대하여 다음 과정을 반복 + - 해당 노드id에 대한 모든 가능한 (진입방향, 진출방향)에 대하여 다음 과정을 반복 + - (노드id, 진입방향)으로부터 진입엣지id를 얻어냄. 마찬가지로 진출엣지id도 얻어냄 + - 얻어낸 정보를 바탕으로 한 행(new_row)을 만들고 이것을 matching에 append + ''' + + self.match7 = self.match6.copy() + self.match7 = self.match7[['inter_no', 'move_no', 'inc_dir', 'out_dir', 'inc_edge', 'out_edge', 'node_id']] + + parent_ids = sorted(self.inter_node[self.inter_node.inter_type=='parent'].node_id.unique()) + child_ids = sorted(self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()) + + # (1) 가능한 (진입방향, 진출방향) 목록 + flows = self.nema.dropna().apply(lambda row: (row['inc_dir'], row['out_dir']), axis=1).tolist() + # (2) 각 교차로별 방향 목록 : pdires (possible directions) + pdires = {} + for node_id in parent_ids: + dires = self.match7[self.match7.node_id == node_id][['inc_dir','out_dir']].values.flatten() + dires = {dire for dire in dires if type(dire)==str} + pdires[node_id] = dires + # (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) + inc2id = {} + for node_id in parent_ids: + for inc_dir in pdires[node_id]: + df = self.match7[(self.match7.node_id==node_id) & (self.match7.inc_dir==inc_dir)] + inc2id[(node_id, inc_dir)] = df.inc_edge.iloc[0] + # (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) + out2id = {} + for node_id in parent_ids: + for out_dir in pdires[node_id]: + df = self.match7[(self.match7.node_id==node_id) & (self.match7.out_dir==out_dir)] + out2id[(node_id, out_dir)] = df.out_edge.iloc[0] + # (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) + pflow = {} + for node_id in parent_ids: + pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])] + # (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching + # node2inter = dict(zip(self.match7['node_id'], self.match7['inter_no'])) + dires_right = ['북', '서', '남', '동', '북'] # ex (북, 서), (서, 남) 등은 우회전 flow + self.matching = [] + for node_id in parent_ids: + inter_no = self.node2inter[node_id] + # 좌회전과 직진(1 ~ 16) + for (inc_dir, out_dir) in pflow[node_id]: + move_no = self.nema[(self.nema.inc_dir==inc_dir) & (self.nema.out_dir==out_dir)].move_no.iloc[0] + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + self.matching.append(new_row) + # 보행신호(17), 전적색(18) + new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18], + 'inc_dir':[None]*2, 'out_dir':[None]*2, + 'inc_edge':[None]*2, 'out_edge':[None]*2, 'node_id':[node_id]*2}) + self.matching.append(new_row) + # 신호우회전(21) + for d in range(len(dires_right)-1): + inc_dir = dires_right[d] + out_dir = dires_right[d+1] + if {inc_dir, out_dir}.issubset(pdires[node_id]): + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + self.matching.append(new_row) + self.matching.append(self.match7[self.match7.node_id.isin(child_ids)]) + self.matching = pd.concat(self.matching) + self.matching = self.matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True) + self.matching['move_no'] = self.matching['move_no'].astype(int) + self.matching.to_csv(os.path.join(self.path_root, 'Intermediates', 'matching.csv')) + + # 2-2 + def get_movements(self): + movements_path = os.path.join(self.path_root, 'Intermediates', 'movement') + movements_list = [pd.read_csv(os.path.join(movements_path, file), index_col=0) for file in tqdm(os.listdir(movements_path), desc='이동류정보 불러오는 중 : movements')] + movements = pd.concat(movements_list) + movements = movements.drop(columns=['start_unix']) + movements = movements.drop_duplicates() + movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B']) + movements = movements.reset_index(drop=True) + movements.to_csv(os.path.join(self.path_root, 'Intermediates', 'movements.csv')) + return movements + + # 2-3 node2num_cycles : A dictionary that maps a node_id to the number of cycles + def get_node2num_cycles(self): + # node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no'])) + self.node_ids = sorted(self.inter_node.node_id.unique()) + + Aplan = self.plan.copy()[['inter_no'] + [f'dura_A{j}' for j in range(1,9)] + ['cycle']] + grouped = Aplan.groupby('inter_no') + df = grouped.agg({'cycle': 'min'}).reset_index() + df = df.rename(columns={'cycle': 'min_cycle'}) + df['num_cycle'] = 300 // df['min_cycle'] + 2 + inter2num_cycles = dict(zip(df['inter_no'], df['num_cycle'])) + node2numcycles = {node_id : inter2num_cycles[self.node2inter[node_id]] for node_id in self.node_ids} + with open(os.path.join('Intermediates','node2numcycles.json'), 'w') as file: + json.dump(node2numcycles, file, indent=4) + return node2numcycles + + # 3. 이슈사항 저장 + def write_issues(self): + path_issues = os.path.join(self.path_root, "Results", "issues_intermediates.txt") + with open(path_issues, "w", encoding="utf-8") as file: + for item in self.issues: + file.write(item + "\n") + if self.issues: + print("데이터 처리 중 발생한 특이사항은 다음과 같습니다. :") + for review in self.issues: + print(review) + + def main(self): + # 1. 데이터 불러오기 + self.load_data() + # 2. 중간산출물 만들기 + self.get_intermediates() + # 3. 이슈사항 저장 + self.write_issues() + +if __name__ == '__main__': + self = DailyPreprocessor() + self.main() \ No newline at end of file