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1411 lines
84 KiB
1411 lines
84 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"import sumolib\n",
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"import random\n",
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"from tqdm import tqdm\n",
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"from datetime import datetime"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# A. 이동류 매칭"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_match1():\n",
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" '''\n",
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" 신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다.\n",
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" '../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다.\n",
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"\n",
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" return : 통합된 이동류정보\n",
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" - 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보\n",
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"\n",
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" match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다.\n",
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" '''\n",
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" # [이동류번호] 불러오기 (약 1분의 소요시간)\n",
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" path_move = '../Data/tables/move/'\n",
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" csv_moves = os.listdir(path_move)\n",
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" moves = [pd.read_csv(path_move + csv_move, index_col=0) for csv_move in tqdm(csv_moves)]\n",
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" match1 = pd.concat(moves).drop_duplicates().sort_values(by=['inter_no','phas_A','phas_B']).reset_index(drop=True)\n",
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" match1.to_csv('../Intermediates/match1.csv')\n",
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" return match1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_match2(match1):\n",
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" '''\n",
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" match1을 계층화함.\n",
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" - match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B\n",
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" - match2의 컬럼 : inter_no, phase_no, ring_type, move_no\n",
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" '''\n",
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" # 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no')\n",
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" matchA = match1[['inter_no', 'phas_A', 'move_A']].copy()\n",
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" matchA.columns = ['inter_no', 'phase_no', 'move_no']\n",
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" matchA['ring_type'] = 'A'\n",
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" matchB = match1[['inter_no', 'phas_B', 'move_B']].copy()\n",
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" matchB.columns = ['inter_no', 'phase_no', 'move_no']\n",
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" matchB['ring_type'] = 'B'\n",
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" match2 = pd.concat([matchA, matchB]).drop_duplicates()\n",
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" match2 = match2[['inter_no', 'phase_no', 'ring_type', 'move_no']]\n",
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" match2 = match2.sort_values(by=list(match2.columns))\n",
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" return match2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_match3(match2):\n",
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" '''\n",
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" 각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함.\n",
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" - match2의 컬럼 : inter_no, phase_no, ring_type, move_no\n",
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" - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir\n",
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"\n",
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" nema : \n",
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" - 컬럼 : move_no, inc_dir, out_dir\n",
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" - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블\n",
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" - 이동류번호 : 1 ~ 16, 17, 18, 21\n",
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" - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서\n",
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" '''\n",
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" # nema 정보 불러오기 및 병합\n",
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" nema = pd.read_csv('../Data/tables/nema.csv', encoding='cp949')\n",
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" match3 = pd.merge(match2, nema, how='left', on='move_no').drop_duplicates()\n",
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" return match3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_match4(match3):\n",
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" '''\n",
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" 방위각 정보를 매칭시켜 추가함.\n",
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" - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir\n",
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" - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle\n",
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"\n",
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" angle_original : \n",
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" - 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8)\n",
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" - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블\n",
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" - 이동류번호 : 1 ~ 16, 17, 18, 21\n",
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" - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서\n",
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" '''\n",
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"\n",
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" # 방위각 정보 불러오기\n",
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" dtype_dict = {f'angle_{alph}{j}':'str' for alph in ['A', 'B'] for j in range(1,9)}\n",
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" angle_original = pd.read_csv('../Data/tables/angle.csv', index_col=0, dtype = dtype_dict)\n",
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"\n",
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" # 계층화\n",
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" angle = []\n",
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" for i, row in angle_original.iterrows():\n",
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" angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]\n",
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" 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",
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" angle.append(new)\n",
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" angle = pd.concat(angle)\n",
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" angle = angle.dropna().reset_index(drop=True)\n",
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"\n",
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" # 병합\n",
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" six_chars = angle.angle_code.apply(lambda x:len(x)==6)\n",
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" angle.loc[six_chars,'inc_angle'] = angle.angle_code.apply(lambda x:x[:3])\n",
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" angle.loc[six_chars,'out_angle'] = angle.angle_code.apply(lambda x:x[3:])\n",
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" angle = angle.drop('angle_code', axis=1)\n",
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" match4 = pd.merge(match3, angle, how='left', left_on=['inter_no', 'phase_no', 'ring_type'],\n",
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" right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates()\n",
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" return match4"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_match5(match4):\n",
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" '''\n",
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" 진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로).\n",
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" - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle\n",
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" - 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",
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" \n",
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" 사용된 데이터 : \n",
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" (1) net\n",
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" - 성남시 정자동 부근의 샘플 네트워크\n",
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" (2) inter_node\n",
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" - 교차로번호와 노드id를 매칭시키는 테이블.\n",
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" - parent/child 정보도 포함되어 있음\n",
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" - 컬럼 : inter_no, node_id, inter_type\n",
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" (3) inter_info\n",
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" - 교차로 정보. 여기에서는 위도와 경도가 쓰임.\n",
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" - 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no\n",
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"\n",
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" 진입엣지id, 진출엣지id를 얻는 과정 :\n",
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" - match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함.\n",
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" * 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음\n",
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" - 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장.\n",
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" * inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge\n",
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" - inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄.\n",
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" - 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄.\n",
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" - 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함.\n",
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" '''\n",
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"\n",
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" # 네트워크 불러오기 \n",
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" net = sumolib.net.readNet('../Data/networks/sn.net.xml')\n",
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" # 교차로-노드 매칭 정보 불러오기\n",
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" inter_node = pd.read_csv('../Data/tables/inter_node.csv', index_col=0)\n",
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" # 교차로정보(위, 경도) 불러오기\n",
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" inter_info = pd.read_csv('../Data/tables/inter_info.csv', index_col=0)\n",
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"\n",
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" # parent node만 가져옴.\n",
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" inter_node1 = inter_node[inter_node.inter_type == 'parent'].drop('inter_type', axis=1)\n",
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" inter_info1 = inter_info[['inter_no', 'inter_lat', 'inter_lon']]\n",
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" inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'],\n",
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" right_on=['inter_no']).drop_duplicates()\n",
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"\n",
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" inter2node = dict(zip(inter['inter_no'], inter['node_id']))\n",
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"\n",
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" match5 = match4.copy()\n",
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" # 진입진출ID 매칭\n",
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" for index, row in match5.iterrows():\n",
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" node_id = inter2node[row.inter_no]\n",
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" node = net.getNode(node_id)\n",
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" # 교차로의 모든 (from / to) edges\n",
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" inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges\n",
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" out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges\n",
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" # 교차로의 모든 (from / to) directions\n",
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" inc_dirs = []\n",
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" for inc_edge in inc_edges:\n",
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" start = inc_edge.getShape()[-2]\n",
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" end = inc_edge.getShape()[-1]\n",
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" inc_dir = np.array(end) - np.array(start)\n",
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" inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5\n",
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" inc_dirs.append(inc_dir)\n",
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" out_dirs = []\n",
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" for out_edge in out_edges:\n",
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" start = out_edge.getShape()[0]\n",
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" end = out_edge.getShape()[1]\n",
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" out_dir = np.array(end) - np.array(start)\n",
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" out_dir = out_dir / (out_dir ** 2).sum() ** 0.5\n",
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" out_dirs.append(out_dir)\n",
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" # 진입각, 진출각 불러오기\n",
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" if not pd.isna(row.inc_angle):\n",
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" inc_angle = int(row.inc_angle)\n",
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" out_angle = int(row.out_angle)\n",
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" # 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환\n",
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" inc_angle = (-90 - inc_angle) % 360\n",
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" inc_angle = inc_angle * np.pi / 180.\n",
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" inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)])\n",
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" out_angle = (90 - out_angle) % 360\n",
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" out_angle = out_angle * np.pi / 180.\n",
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" out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)])\n",
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" # 매칭 엣지 반환\n",
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" inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax()\n",
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" out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax()\n",
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" inc_edge_id = inc_edges[inc_index].getID()\n",
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" out_edge_id = out_edges[out_index].getID()\n",
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" match5.at[index, 'inc_edge'] = inc_edge_id\n",
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" match5.at[index, 'out_edge'] = out_edge_id\n",
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" match5['node_id'] = match5['inter_no'].map(inter2node)\n",
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" match5 = match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True)\n",
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" return match5"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_match6(match5):\n",
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" '''\n",
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" 진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로).\n",
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" - 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",
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" \n",
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" 사용된 데이터 : \n",
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" (1) inter_node\n",
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" - 교차로번호와 노드id를 매칭시키는 테이블.\n",
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" - parent/child 정보도 포함되어 있음\n",
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" - 컬럼 : inter_no, node_id, inter_type\n",
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" (2) uturn (유턴정보)\n",
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" - 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge\n",
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" - parent_id, child_id : 주교차로id, 유턴교차로id\n",
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" - direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향)\n",
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" - condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나\n",
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" - inc_edge, out_edge : 유턴에 대한 진입진출엣지\n",
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" (3) coord (연동교차로정보)\n",
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" - 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge\n",
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" - parent_id, child_id : 주교차로id, 연동교차로id\n",
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" - 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지\n",
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"\n",
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" 설명 :\n",
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" - match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음.\n",
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" 여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함.\n",
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" 유턴교차로 :\n",
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" - directions를 정북기준 시계방향의 8방위로 정함.\n",
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" - 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음.\n",
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" - 예) 진입방향(direction)이 '북'일 때, \n",
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" - 직진 : (북, 남)\n",
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" * 남 : directions[(ind + 4) % len(directions)]\n",
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" - 좌회전 : (북, 동)\n",
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" * 동 : directions[(ind + 2) % len(directions)]\n",
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" - 보행 : (서, 동)\n",
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" * 서 : directions[(ind - 2) % len(directions)]\n",
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" - uturn의 각 행을 순회하면서 아래 과정을 반복함\n",
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" - match5에서 parent_id에 해당하는 행들을 가져옴(cmatch).\n",
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" - 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",
|
|
" inter_node = pd.read_csv('../Data/tables/inter_node.csv', index_col=0)\n",
|
|
" node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no']))\n",
|
|
"\n",
|
|
" uturn = pd.read_csv('../Data/tables/child_uturn.csv')\n",
|
|
" coord = pd.read_csv('../Data/tables/child_coord.csv')\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):\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",
|
|
" inter_node = pd.read_csv('../Data/tables/inter_node.csv', index_col=0)\n",
|
|
" nema = pd.read_csv('../Data/tables/nema.csv', encoding='cp949')\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": [
|
|
"match1 = pd.read_csv('../Intermediates/match1.csv', index_col=0)\n",
|
|
"match2 = make_match2(match1)\n",
|
|
"match3 = make_match3(match2)\n",
|
|
"match4 = make_match4(match3)\n",
|
|
"match5 = make_match5(match4)\n",
|
|
"match6 = make_match6(match5)\n",
|
|
"matching = make_matching(match6)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# B. 5초 간격으로 이동류번호 수집"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"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": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def save_movement():\n",
|
|
" # time2move = dict(zip(fsecs,moves)) # move : 어느 순간의 이동류정보\n",
|
|
" history = pd.read_csv('../Data/tables/history.csv', index_col=0)\n",
|
|
"\n",
|
|
" time2movement = {} # movement : 어느 순간의, 그 순간으로부터 한시간 동안의 (교차로번호 + 현시별이동류번호 + 시작시간)\n",
|
|
" # - 아래 절차를 5초마다 반복\n",
|
|
" for fsec in tqdm(fsecs): # 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",
|
|
" time2movement[fsec] = movement\n",
|
|
" movement.to_csv(f'../Intermediates/movement/movement_{fsec}.csv')\n",
|
|
"# save_movement()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# C. 5분 간격으로 신호이력 수집 및 통합테이블 생성"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"m = 105\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)\n",
|
|
"\n",
|
|
"# 현재시각\n",
|
|
"present_time = fmins[m]\n",
|
|
"\n",
|
|
"plan = pd.read_csv('../Data/tables/plan.csv', index_col=0)\n",
|
|
"history = pd.read_csv('../Data/tables/history.csv', index_col=0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"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",
|
|
"splits, isplits = make_splits(plan)\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",
|
|
"timetable = make_timetable(plan)\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']))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def calculate_DS(rhist, curr_unix, hours):\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 = midnight\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": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def make_rhistory(history, present_time):\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",
|
|
" # 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",
|
|
"\n",
|
|
"rhistory = make_rhistory(history, present_time)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def process(rhistory, 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)\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)\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": 17,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def make_rhists(rhistory, present_time):\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",
|
|
" # 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",
|
|
"\n",
|
|
" # 2-1. 참값 판단 프로세스\n",
|
|
" hours = np.array(range(midnight - 7200, next_day + 1, 3600)) # 정각에 해당하는 시각들 목록\n",
|
|
" rhists = process(rhistory, hours)\n",
|
|
" return rhists\n",
|
|
"rhists = make_rhists(rhistory, present_time)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"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\n",
|
|
"hrhists = make_hrhists(rhists, isplits, timetable)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"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\n",
|
|
"movements = pd.read_csv('../Intermediates/movements.csv')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"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\n",
|
|
"movement = pd.read_csv(f'../Intermediates/movement/movement_{present_time}.csv', index_col=0)\n",
|
|
"movement_updated = update_movement(hrhists, movement, movements)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def make_histid(present_time, movedur, inter2node):\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",
|
|
" out_edge_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 - 1200\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)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"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\n",
|
|
"\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",
|
|
"\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",
|
|
"\n",
|
|
" # 참고할 딕셔너리, 데이터프레임 등 목록\n",
|
|
" splits, isplits = make_splits(plan)\n",
|
|
" timetable = make_timetable(plan)\n",
|
|
"\n",
|
|
" # 현재시점\n",
|
|
" present_time = fmins[m]\n",
|
|
" print(datetime.fromtimestamp(present_time))\n",
|
|
"\n",
|
|
" # rhists, hrhists\n",
|
|
" rhists = make_rhists(rhistory, present_time)\n",
|
|
" hrhists = make_hrhists(rhists, isplits, timetable)\n",
|
|
"\n",
|
|
" # movements, movement, movement_updated\n",
|
|
" movements = pd.read_csv('../Intermediates/movements.csv', index_col=0)\n",
|
|
" movement = pd.read_csv(f'../Intermediates/movement/movement_{present_time}.csv', index_col=0)\n",
|
|
" movement_updated = update_movement(hrhists, movement, movements)\n",
|
|
"\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",
|
|
"\n",
|
|
" # histid\n",
|
|
" histid = make_histid(present_time, movedur, inter2node)\n",
|
|
" histid.to_csv(f'../Intermediates/histid/histid_{fmins[m]}.csv')\n",
|
|
"\n",
|
|
" return histid"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"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": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
|
" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>inter_no</th>\n",
|
|
" <th>node_id</th>\n",
|
|
" <th>start_unix</th>\n",
|
|
" <th>phas_A</th>\n",
|
|
" <th>phas_B</th>\n",
|
|
" <th>duration</th>\n",
|
|
" <th>inc_edge_A</th>\n",
|
|
" <th>out_edge_A</th>\n",
|
|
" <th>inc_edge_B</th>\n",
|
|
" <th>out_edge_B</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
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" <tr>\n",
|
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" <th>458</th>\n",
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" <td>206</td>\n",
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" <td>i7</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>-571511538_02</td>\n",
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" <td>571542073_02</td>\n",
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" <td>571542073_01</td>\n",
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" <td>571511538_02</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>459</th>\n",
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|
|
" <tr>\n",
|
|
" <th>460</th>\n",
|
|
" <td>206</td>\n",
|
|
" <td>i7</td>\n",
|
|
" <td>1704411040</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>-571511538_02</td>\n",
|
|
" <td>571542073_02</td>\n",
|
|
" <td>571542073_01</td>\n",
|
|
" <td>571511538_02</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>461</th>\n",
|
|
" <td>206</td>\n",
|
|
" <td>i7</td>\n",
|
|
" <td>1704411040</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>446</th>\n",
|
|
" <td>177</td>\n",
|
|
" <td>i2</td>\n",
|
|
" <td>1704411050</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>-571542809_01</td>\n",
|
|
" <td>571542811_01</td>\n",
|
|
" <td>571542811_02</td>\n",
|
|
" <td>571542809_01</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>...</th>\n",
|
|
" <td>...</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>...</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>204</th>\n",
|
|
" <td>201</td>\n",
|
|
" <td>i8</td>\n",
|
|
" <td>1704412010</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>18</td>\n",
|
|
" <td>571500583_01</td>\n",
|
|
" <td>571500617_01</td>\n",
|
|
" <td>571500583_01</td>\n",
|
|
" <td>571500569_01</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>580</th>\n",
|
|
" <td>206</td>\n",
|
|
" <td>i7</td>\n",
|
|
" <td>1704412020</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>-571511538_02</td>\n",
|
|
" <td>571542073_02</td>\n",
|
|
" <td>571542073_01</td>\n",
|
|
" <td>571511538_02</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>581</th>\n",
|
|
" <td>206</td>\n",
|
|
" <td>i7</td>\n",
|
|
" <td>1704412020</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>582</th>\n",
|
|
" <td>206</td>\n",
|
|
" <td>i7</td>\n",
|
|
" <td>1704412020</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>-571511538_02</td>\n",
|
|
" <td>571542073_02</td>\n",
|
|
" <td>571542073_01</td>\n",
|
|
" <td>571511538_02</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>583</th>\n",
|
|
" <td>206</td>\n",
|
|
" <td>i7</td>\n",
|
|
" <td>1704412020</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"<p>209 rows × 10 columns</p>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" inter_no node_id start_unix phas_A phas_B duration inc_edge_A \\\n",
|
|
"458 206 i7 1704411040 1 1 44 -571511538_02 \n",
|
|
"459 206 i7 1704411040 2 2 44 NaN \n",
|
|
"460 206 i7 1704411040 3 3 26 -571511538_02 \n",
|
|
"461 206 i7 1704411040 4 4 26 NaN \n",
|
|
"446 177 i2 1704411050 1 1 40 -571542809_01 \n",
|
|
".. ... ... ... ... ... ... ... \n",
|
|
"204 201 i8 1704412010 5 5 18 571500583_01 \n",
|
|
"580 206 i7 1704412020 1 1 44 -571511538_02 \n",
|
|
"581 206 i7 1704412020 2 2 44 NaN \n",
|
|
"582 206 i7 1704412020 3 3 26 -571511538_02 \n",
|
|
"583 206 i7 1704412020 4 4 26 NaN \n",
|
|
"\n",
|
|
" out_edge_A inc_edge_B out_edge_B \n",
|
|
"458 571542073_02 571542073_01 571511538_02 \n",
|
|
"459 NaN NaN NaN \n",
|
|
"460 571542073_02 571542073_01 571511538_02 \n",
|
|
"461 NaN NaN NaN \n",
|
|
"446 571542811_01 571542811_02 571542809_01 \n",
|
|
".. ... ... ... \n",
|
|
"204 571500617_01 571500583_01 571500569_01 \n",
|
|
"580 571542073_02 571542073_01 571511538_02 \n",
|
|
"581 NaN NaN NaN \n",
|
|
"582 571542073_02 571542073_01 571511538_02 \n",
|
|
"583 NaN NaN NaN \n",
|
|
"\n",
|
|
"[209 rows x 10 columns]"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"preprocess(105)\n",
|
|
"preprocess(106)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2024-01-05 13:40:00\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"histid = preprocess(164)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[175, 176, 177, 178, 201, 202, 206, 210]\n",
|
|
"[175, 176, 177, 178, 201, 202, 206]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(sorted(history.inter_no.unique()))\n",
|
|
"print(sorted(histid.inter_no.unique()))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1704411900\n",
|
|
"175\n",
|
|
"900\n",
|
|
"False True\n",
|
|
"176\n",
|
|
"900\n",
|
|
"False True\n",
|
|
"177\n",
|
|
"823\n",
|
|
"False True\n",
|
|
"178\n",
|
|
"509\n",
|
|
"False True\n",
|
|
"201\n",
|
|
"850\n",
|
|
"False True\n",
|
|
"202\n",
|
|
"960\n",
|
|
"False True\n",
|
|
"206\n",
|
|
"750\n",
|
|
"False True\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"print(present_time)\n",
|
|
"k = 0\n",
|
|
"for inter_no in sorted(histid.inter_no.unique()):\n",
|
|
" k += 1\n",
|
|
" df = histid[histid.inter_no==inter_no]\n",
|
|
" M = df.start_unix.max()\n",
|
|
" m = df.start_unix.min()\n",
|
|
" print(inter_no)\n",
|
|
" print(M - m)\n",
|
|
" plt.scatter([M, m], [k] * 2, c='y')\n",
|
|
" plt.scatter([present_time], [k], c='b')\n",
|
|
" plt.axvline(x=histid.start_unix.max(), c='r')\n",
|
|
" plt.axvline(x=histid.start_unix.min(), c='r')\n",
|
|
" print(m < present_time, present_time < M)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# for m in range(100, 200):\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
|
|
}
|