{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from datetime import datetime\n", "import os\n", "import random\n", "from tqdm import tqdm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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inter_nointer_nameinter_latinter_longroup_no
0175한솔삼거리(한솔마을3거리)37.36857127.114783
1176느티마을4단지37.36724127.114803
2177한솔6단지(정자동사무소)37.36619127.114803
3178신기4거리37.36346127.114803
4201느티마을4거리37.36854127.109843
5202느티마을 안촌유치원37.36855127.112413
6206상록마을 321동37.36483127.109813
7210상록4거리(상록마을입구4거리)37.36347127.109823
8211상록마을단일로37.36346127.112373
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" ], "text/plain": [ " inter_no inter_name inter_lat inter_lon group_no\n", "0 175 한솔삼거리(한솔마을3거리) 37.36857 127.11478 3\n", "1 176 느티마을4단지 37.36724 127.11480 3\n", "2 177 한솔6단지(정자동사무소) 37.36619 127.11480 3\n", "3 178 신기4거리 37.36346 127.11480 3\n", "4 201 느티마을4거리 37.36854 127.10984 3\n", "5 202 느티마을 안촌유치원 37.36855 127.11241 3\n", "6 206 상록마을 321동 37.36483 127.10981 3\n", "7 210 상록4거리(상록마을입구4거리) 37.36347 127.10982 3\n", "8 211 상록마을단일로 37.36346 127.11237 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 교차로정보\n", "inter_info = pd.read_csv(\"inter_info.csv\")\n", "inter_info['group_no'] = 3\n", "display(inter_info)\n", "inter_info.to_csv('../inter_info.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " inter_no main_phase mg1A mg1B mg2A mg2B mg3A mg3B mg4A mg4B mg5A \\\n", "0 175 2 24 27 33 12 15 39 15 19 0 \n", "1 176 2 21 31 33 30 16 0 0 0 0 \n", "2 177 2 35 18 36 36 0 0 38 13 0 \n", "3 178 2 18 19 35 26 36 29 17 37 0 \n", "4 201 2 25 20 21 32 31 28 31 17 27 \n", "5 202 2 12 30 0 0 0 0 0 0 0 \n", "6 206 2 36 29 0 0 31 31 0 0 0 \n", "7 210 2 19 0 12 13 21 37 12 22 0 \n", "8 211 2 32 35 0 0 0 0 0 0 0 \n", "\n", " mg5B mg6A mg6B mg7A mg7B mg8A mg8B r1A r1B r2A r2B r3A r3B \\\n", "0 0 0 0 0 0 0 0 1 1 1 1 1 1 \n", "1 0 0 0 0 0 0 0 1 1 1 1 1 0 \n", "2 0 0 0 0 0 0 0 1 1 1 1 1 0 \n", "3 0 0 0 0 0 0 0 1 1 1 1 1 1 \n", "4 27 0 0 0 0 0 0 1 1 1 1 1 1 \n", "5 0 0 0 0 0 0 0 1 1 1 0 0 0 \n", "6 0 0 0 0 0 0 0 1 1 1 0 1 1 \n", "7 0 0 0 0 0 0 0 1 0 1 1 1 1 \n", "8 0 0 0 0 0 0 0 1 1 1 0 0 0 \n", "\n", " r4A r4B r5A r5B r6A r6B r7A r7B r8A r8B y1A y1B y2A y2B y3A \\\n", "0 1 1 0 0 0 0 0 0 0 0 4 4 4 4 4 \n", "1 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 \n", "2 1 1 0 0 0 0 0 0 0 0 4 4 4 4 0 \n", "3 1 1 0 0 0 0 0 0 0 0 4 4 4 4 4 \n", "4 1 1 1 1 0 0 0 0 0 0 4 4 4 4 4 \n", "5 0 0 0 0 0 0 0 0 0 0 4 4 0 0 0 \n", "6 1 0 0 0 0 0 0 0 0 0 4 4 0 0 4 \n", "7 1 1 0 0 0 0 0 0 0 0 4 0 4 4 4 \n", "8 0 0 0 0 0 0 0 0 0 0 4 4 0 0 0 \n", "\n", " y3B y4A y4B y5A y5B y6A y6B y7A y7B y8A y8B \n", "0 4 4 4 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 0 0 0 0 0 \n", "2 0 4 4 0 0 0 0 0 0 0 0 \n", "3 4 4 4 0 0 0 0 0 0 0 0 \n", "4 4 4 4 4 4 0 0 0 0 0 0 \n", "5 0 0 0 0 0 0 0 0 0 0 0 \n", "6 4 0 0 0 0 0 0 0 0 0 0 \n", "7 4 4 4 0 0 0 0 0 0 0 0 \n", "8 0 0 0 0 0 0 0 0 0 0 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 현시구성\n", "phase_info = pd.read_csv(\"phase_info.csv\")\n", "phase_info.drop('flow_no', axis=1, inplace=True)\n", "# display(phase_info)\n", "mg = phase_info.pivot_table(index='inter_no', columns=['phase_no', 'ring_type'], values='min_green_dur').fillna(0).astype(int)\n", "y = phase_info.pivot_table(index='inter_no', columns=['phase_no', 'ring_type'], values='yellow_dur').fillna(0).astype(int)\n", "r = phase_info.pivot_table(index='inter_no', columns=['phase_no', 'ring_type'], values='red_dur').fillna(0).astype(int)\n", "max_phase_no = phase_info['phase_no'].max()\n", "new_columns = [f'{col}{phase}{ring}' for col in ['mg', 'y', 'r'] for phase in range(1, max_phase_no + 1) for ring in ['A', 'B']]\n", "phase_info = pd.concat([mg, y, r], axis=1)\n", "phase_info.columns = new_columns\n", "phase_info['main_phase'] = 2\n", "phase_info['mg6A'] = phase_info['mg6B'] = phase_info['mg7A'] = phase_info['mg7B'] = phase_info['mg8A'] = phase_info['mg8B']= 0\n", "phase_info['y6A'] = phase_info['y6B'] = phase_info['y7A'] = phase_info['y7B'] = phase_info['y8A'] = phase_info['y8B']= 0\n", "phase_info['r6A'] = phase_info['r6B'] = phase_info['r7A'] = phase_info['r7B'] = phase_info['r8A'] = phase_info['r8B']= 0\n", "phase_info = phase_info[sorted(phase_info.columns)]\n", "phase_info.reset_index(inplace=True)\n", "with pd.option_context('display.max_rows', None, 'display.max_columns', None): \n", " display(phase_info)\n", "phase_info.to_csv('../phase_info.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " inter_no angle_A1 angle_A2 angle_A3 angle_A4 angle_A5 angle_A6 angle_A7 \\\n", "0 175 179004 1095 90270 268000 NaN NaN NaN \n", "1 176 180000 180000 270356 NaN NaN NaN NaN \n", "2 177 180000 90 stop 268000 NaN NaN NaN \n", "3 178 180000 90 270000 90270 NaN NaN NaN \n", "4 201 180000 270000 90270 90270 90 NaN NaN \n", "5 202 90270 stop NaN NaN NaN NaN NaN \n", "6 206 180000 stop 180000 stop NaN NaN NaN \n", "7 210 90270 268000 359090 180000 NaN NaN NaN \n", "8 211 90270 stop NaN NaN NaN NaN NaN \n", "\n", " angle_A8 angle_B1 angle_B2 angle_B3 angle_B4 angle_B5 angle_B6 angle_B7 \\\n", "0 NaN 3176 179270 90180 270090 NaN NaN NaN \n", "1 NaN 359180 180270 NaN NaN NaN NaN NaN \n", "2 NaN 1176 179270 NaN 90180 NaN NaN NaN \n", "3 NaN 180 180270 270090 90180 NaN NaN NaN \n", "4 NaN 180270 270090 270090 90180 180 NaN NaN \n", "5 NaN 270090 NaN NaN NaN NaN NaN NaN \n", "6 NaN 180 NaN 180 NaN NaN NaN NaN \n", "7 NaN NaN 270090 180 180270 NaN NaN NaN \n", "8 NaN 270090 NaN NaN NaN NaN NaN NaN \n", "\n", " angle_B8 \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "7 NaN \n", "8 NaN " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 방위각정보\n", "flow_angle = pd.read_csv(\"flow_angle.csv\")\n", "angle = flow_angle.pivot_table(index='inter_no', columns=['phase_no', 'ring_type'], values='angle_code', aggfunc='first').fillna('NaN')\n", "new_columns = [f'angle_{ring}{phase}' for phase in range(1, 6) for ring in ['A', 'B']]\n", "angle.columns = new_columns\n", "angle['angle_A6'] = angle['angle_A7'] = angle['angle_A8'] = np.nan\n", "angle['angle_B6'] = angle['angle_B7'] = angle['angle_B8'] = np.nan\n", "angle = angle[sorted(angle.columns)]\n", "angle = angle.reset_index()\n", "display(angle)\n", "angle.to_csv('../angle.csv')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 17280/17280 [04:09<00:00, 69.13it/s]\n" ] } ], "source": [ "# 이동류번호\n", "plan = pd.read_csv(\"time_plan.csv\")\n", "phase = pd.read_csv('phase_info.csv')\n", "plan = plan[plan.ID.isin([1, 5, 8, 11])]\n", "ID2starttime = {1:\"00:00\", 5:\"07:00\", 8:\"09:00\", 11:\"18:30\"}\n", "plan['start_hour'] = plan.ID.apply(lambda id:ID2starttime[id].split(\":\")[0])\n", "plan['start_minute']=plan.ID.apply(lambda id:ID2starttime[id].split(\":\")[1])\n", "plan = plan[['inter_no', 'start_hour', 'start_minute', 'phase_no', 'ring_type', 'duration', 'cycle']]\n", "plan_ = pd.merge(plan, phase[['inter_no', 'phase_no', 'ring_type', 'flow_no']], on=['inter_no', 'phase_no', 'ring_type'], how='left')\n", "plan_['flow_no'] = plan_['flow_no'].fillna(18).astype(int)\n", "plan_['start_second'] = plan_.start_hour.astype(int)*3600 + plan_.start_minute.astype(int)*60\n", "plan_ = plan_[['inter_no', 'start_second', 'phase_no', 'ring_type', 'duration', 'flow_no', 'cycle']]\n", "cycle_dict = dict(zip(zip(plan_['inter_no'], plan_['start_second']), plan_['cycle']))\n", "df = plan_[['inter_no','start_second','cycle']].drop_duplicates()\n", "df = df.reset_index(drop=True)\n", "df['start_second_exact'] = 0\n", "for i in range(1, len(df)):\n", " if i % 4 != 0:\n", " prev = df.loc[i-1, 'start_second_exact']\n", " start_second = df.loc[i, 'start_second']\n", " cycle = df.loc[i-1, 'cycle']\n", " df.loc[i, 'start_second_exact'] = (start_second - prev) % cycle + start_second\n", "inter2start = {inter_no:list(df[df.inter_no==inter_no]['start_second_exact']) for inter_no in df.inter_no}\n", "plan_ = plan_.pivot_table(index=['inter_no', 'start_second', 'phase_no'],\n", " columns='ring_type',\n", " values=['duration', 'flow_no'],\n", " aggfunc='first').reset_index()\n", "plan_.columns = ['inter_no', 'start_second', 'phase_no', 'dura_A', 'dura_B', 'move_A', 'move_B']\n", "inter2no_phase = {inter_no:group.phase_no.unique().max() for (inter_no, group) in plan.groupby('inter_no')}\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", "for current_time in tqdm(range(midnight, next_day, 5)):\n", " sec = current_time - midnight\n", " the_start_second = np.max(df.start_second[df.start_second <= sec])\n", " move = []\n", " for inter_no in plan_.inter_no.unique():\n", " cycle = cycle_dict[(inter_no, the_start_second)]\n", " df = plan_[(plan_.inter_no==inter_no)&(plan_.start_second==the_start_second)].reset_index(drop=True)\n", " remainder = sec % cycle\n", " df['cums_A'] = df.copy().dura_A.cumsum()\n", " df['cums_B'] = df.copy().dura_B.cumsum()\n", " ind_A = len(df.cums_A[df.cums_A <= remainder]) % inter2no_phase[inter_no]\n", " ind_B = len(df.cums_B[df.cums_B <= remainder]) % inter2no_phase[inter_no]\n", " dura_A = df.loc[ind_A, 'dura_A']\n", " dura_B = df.loc[ind_B, 'dura_B']\n", " phas_A = df.loc[ind_A, 'phase_no']\n", " phas_B = df.loc[ind_B, 'phase_no']\n", " move_A = df.loc[ind_A, 'move_A']\n", " move_B = df.loc[ind_B, 'move_B']\n", " move.append(pd.DataFrame({'inter_no':[inter_no], 'phas_A':[phas_A], 'phas_B':[phas_B], 'move_A':[move_A], 'move_B':[move_B]}))\n", " move = pd.concat(move)\n", " move.to_csv(f'../moves/move_{current_time}.csv')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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inter_nostart_hourstart_minuteddur_1ddur_2ddur_3ddur_4ddur_5ddur_6ddur_7ddur_8cycleoffset
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1820109003336255818000170134
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23202183048122000000170103
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26206090045532626000015017
27206183046622626000016010
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\n", "
" ], "text/plain": [ " inter_no start_hour start_minute ddur_1 ddur_2 ddur_3 ddur_4 ddur_5 \\\n", "0 175 00 00 37 39 25 30 29 \n", "1 175 07 00 40 42 29 26 33 \n", "2 175 09 00 43 45 33 22 37 \n", "3 175 18 30 46 48 37 18 41 \n", "4 176 00 00 37 73 40 0 0 \n", "5 176 07 00 37 93 40 0 0 \n", "6 176 09 00 37 103 40 0 0 \n", "7 176 18 30 37 113 40 0 0 \n", "8 177 00 00 36 20 68 26 0 \n", "9 177 07 00 40 25 71 34 0 \n", "10 177 09 00 43 27 70 40 0 \n", "11 177 18 30 45 32 77 36 0 \n", "12 178 00 00 38 39 40 23 0 \n", "13 178 07 00 38 39 42 20 21 \n", "14 178 09 00 38 39 43 28 22 \n", "15 178 18 30 38 39 44 36 23 \n", "16 201 00 00 24 24 17 58 17 \n", "17 201 07 00 30 36 18 58 18 \n", "18 201 09 00 33 36 25 58 18 \n", "19 201 18 30 36 50 18 58 18 \n", "20 202 00 00 39 101 0 0 0 \n", "21 202 07 00 46 114 0 0 0 \n", "22 202 09 00 46 114 0 0 0 \n", "23 202 18 30 48 122 0 0 0 \n", "24 206 00 00 33 35 26 26 0 \n", "25 206 07 00 44 44 26 26 0 \n", "26 206 09 00 45 53 26 26 0 \n", "27 206 18 30 46 62 26 26 0 \n", "28 210 00 00 24 19 29 56 22 \n", "29 210 07 00 24 19 39 65 23 \n", "30 210 09 00 28 15 43 70 24 \n", "31 210 18 30 24 19 47 75 25 \n", "32 211 00 00 28 97 0 0 0 \n", "33 211 07 00 28 107 0 0 0 \n", "34 211 09 00 28 112 0 0 0 \n", "35 211 18 30 28 117 0 0 0 \n", "\n", " ddur_6 ddur_7 ddur_8 cycle offset \n", "0 0 0 0 160 57 \n", "1 0 0 0 170 40 \n", "2 0 0 0 180 28 \n", "3 0 0 0 190 18 \n", "4 0 0 0 150 131 \n", "5 0 0 0 170 153 \n", "6 0 0 0 180 169 \n", "7 0 0 0 190 185 \n", "8 0 0 0 150 35 \n", "9 0 0 0 170 33 \n", "10 0 0 0 180 41 \n", "11 0 0 0 190 49 \n", "12 0 0 0 140 50 \n", "13 0 0 0 160 90 \n", "14 0 0 0 170 80 \n", "15 0 0 0 180 75 \n", "16 0 0 0 140 133 \n", "17 0 0 0 160 132 \n", "18 0 0 0 170 134 \n", "19 0 0 0 180 137 \n", "20 0 0 0 140 103 \n", "21 0 0 0 160 103 \n", "22 0 0 0 160 103 \n", "23 0 0 0 170 103 \n", "24 0 0 0 120 10 \n", "25 0 0 0 140 7 \n", "26 0 0 0 150 17 \n", "27 0 0 0 160 10 \n", "28 0 0 0 150 115 \n", "29 0 0 0 170 131 \n", "30 0 0 0 180 137 \n", "31 0 0 0 190 143 \n", "32 0 0 0 125 45 \n", "33 0 0 0 135 45 \n", "34 0 0 0 140 55 \n", "35 0 0 0 145 55 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 신호계획\n", "plan = pd.read_csv(\"time_plan.csv\")\n", "plan = plan[plan.ID.isin([1, 5, 8, 11])]\n", "ID2starttime = {1:\"00:00\", 5:\"07:00\", 8:\"09:00\", 11:\"18:30\"}\n", "plan['start_hour'] = plan.ID.apply(lambda id:ID2starttime[id].split(\":\")[0])\n", "plan['start_minute']=plan.ID.apply(lambda id:ID2starttime[id].split(\":\")[1])\n", "plan = plan[['inter_no', 'start_hour', 'start_minute', 'phase_no', 'ring_type', 'duration', 'cycle', 'offset']]\n", "plan = plan.pivot_table(index=['inter_no', 'start_hour', 'start_minute', 'cycle', 'offset'], columns=['phase_no','ring_type'], values='duration', aggfunc='first').fillna(0).astype(int)\n", "new_columns = [f'dura_{ring}{phase}' for phase in range(1, 6) for ring in ['A', 'B']]\n", "plan.columns = new_columns\n", "new_columns = [f'dura_{ring}{phase}' for phase in range(1, 9) for ring in ['A', 'B']]\n", "for col in new_columns:\n", " if col not in plan.columns:\n", " plan[col] = 0\n", "plan.reset_index(inplace=True)\n", "plan = plan[list(plan.columns)[:3] + sorted(plan.columns)[1:-4] + list(plan.columns)[3:5]]\n", "plan.to_csv('../plan.csv') # plan original\n", "pland = plan.copy()\n", "cums_A = np.array(pland[[f'dura_A{k}' for k in range(1,9)]].cumsum(axis=1))\n", "cums_B = np.array(pland[[f'dura_B{k}' for k in range(1,9)]].cumsum(axis=1))\n", "detailed_cums = []\n", "i = 0\n", "for row_A, row_B in zip(cums_A, cums_B):\n", " combined_row = np.unique(np.concatenate((row_A, row_B)))\n", " ddur = np.concatenate(([combined_row[0]], np.diff(combined_row)))\n", " ddur = np.pad(ddur, (0, 8 - len(ddur)), constant_values=(0))\n", " detailed_cums.append(ddur)\n", " for j in range(8):\n", " pland.at[i, f'ddur_{j+1}'] = ddur[j]\n", " i+=1\n", "pland = pland[['inter_no', 'start_hour', 'start_minute'] + [f'ddur_{i}' for i in range(1,9)] + ['cycle', 'offset']] # plan detailed\n", "pland[[f'ddur_{i}' for i in range(1,9)]] = pland[[f'ddur_{i}' for i in range(1,9)]].astype(int)\n", "display(pland)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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inter_nostart_hourstart_minuteddur_1ddur_2ddur_3ddur_4ddur_5ddur_6ddur_7ddur_8cycleoffsetend_unix
02060000333526260000120101704380521
121100002897000000125451704380525
2201000024241758170001401331704380540
32020000391010000001401031704380540
41780000383940230000140501704380541
51770000362068260000150351704380550
61760000377340000001501311704380550
7210000024192956220001501151704380551
817500003739253029000160571704380560
92060000333526260000120101704380640
1021100002897000000125451704380645
111780000383940230000140501704380680
12201000024241758170001401331704380680
132020000391010000001401031704380680
141760000377340000001501311704380700
15210000024192956220001501151704380700
161770000362068260000150351704380700
1717500003739253029000160571704380721
182060000333526260000120101704380759
1921100002897000000125451704380765
202020000391010000001401031704380820
21201000024241758170001401331704380820
221780000383940230000140501704380820
231760000377340000001501311704380850
241770000362068260000150351704380850
25210000024192956220001501151704380851
2617500003739253029000160571704380880
272060000333526260000120101704380880
2821100002897000000125451704380885
292020000391010000001401031704380959
\n", "
" ], "text/plain": [ " inter_no start_hour start_minute ddur_1 ddur_2 ddur_3 ddur_4 ddur_5 ddur_6 \\\n", "0 206 00 00 33 35 26 26 0 0 \n", "1 211 00 00 28 97 0 0 0 0 \n", "2 201 00 00 24 24 17 58 17 0 \n", "3 202 00 00 39 101 0 0 0 0 \n", "4 178 00 00 38 39 40 23 0 0 \n", "5 177 00 00 36 20 68 26 0 0 \n", "6 176 00 00 37 73 40 0 0 0 \n", "7 210 00 00 24 19 29 56 22 0 \n", "8 175 00 00 37 39 25 30 29 0 \n", "9 206 00 00 33 35 26 26 0 0 \n", "10 211 00 00 28 97 0 0 0 0 \n", "11 178 00 00 38 39 40 23 0 0 \n", "12 201 00 00 24 24 17 58 17 0 \n", "13 202 00 00 39 101 0 0 0 0 \n", "14 176 00 00 37 73 40 0 0 0 \n", "15 210 00 00 24 19 29 56 22 0 \n", "16 177 00 00 36 20 68 26 0 0 \n", "17 175 00 00 37 39 25 30 29 0 \n", "18 206 00 00 33 35 26 26 0 0 \n", "19 211 00 00 28 97 0 0 0 0 \n", "20 202 00 00 39 101 0 0 0 0 \n", "21 201 00 00 24 24 17 58 17 0 \n", "22 178 00 00 38 39 40 23 0 0 \n", "23 176 00 00 37 73 40 0 0 0 \n", "24 177 00 00 36 20 68 26 0 0 \n", "25 210 00 00 24 19 29 56 22 0 \n", "26 175 00 00 37 39 25 30 29 0 \n", "27 206 00 00 33 35 26 26 0 0 \n", "28 211 00 00 28 97 0 0 0 0 \n", "29 202 00 00 39 101 0 0 0 0 \n", "\n", " ddur_7 ddur_8 cycle offset end_unix \n", "0 0 0 120 10 1704380521 \n", "1 0 0 125 45 1704380525 \n", "2 0 0 140 133 1704380540 \n", "3 0 0 140 103 1704380540 \n", "4 0 0 140 50 1704380541 \n", "5 0 0 150 35 1704380550 \n", "6 0 0 150 131 1704380550 \n", "7 0 0 150 115 1704380551 \n", "8 0 0 160 57 1704380560 \n", "9 0 0 120 10 1704380640 \n", "10 0 0 125 45 1704380645 \n", "11 0 0 140 50 1704380680 \n", "12 0 0 140 133 1704380680 \n", "13 0 0 140 103 1704380680 \n", "14 0 0 150 131 1704380700 \n", "15 0 0 150 115 1704380700 \n", "16 0 0 150 35 1704380700 \n", "17 0 0 160 57 1704380721 \n", "18 0 0 120 10 1704380759 \n", "19 0 0 125 45 1704380765 \n", "20 0 0 140 103 1704380820 \n", "21 0 0 140 133 1704380820 \n", "22 0 0 140 50 1704380820 \n", "23 0 0 150 131 1704380850 \n", "24 0 0 150 35 1704380850 \n", "25 0 0 150 115 1704380851 \n", "26 0 0 160 57 1704380880 \n", "27 0 0 120 10 1704380880 \n", "28 0 0 125 45 1704380885 \n", "29 0 0 140 103 1704380959 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 신호이력\n", "midnight = int(datetime(2024, 1, 5, 0, 0, 0).timestamp())\n", "next_day = int(datetime(2024, 1, 6, 0, 0, 0).timestamp())\n", "pland['end_unix'] = pland['start_hour'].astype(int) * 3600 + plan['start_minute'].astype(int) * 60 + midnight\n", "history = []\n", "for inter_no in pland.inter_no.unique():\n", " aplan = pland[pland.inter_no == inter_no] # a (specific) plan\n", " rows = []\n", " current_time = midnight # 현재시각\n", " for i in range(len(aplan)):\n", " first_row = aplan.iloc[i]\n", " cycle = first_row['cycle']\n", " unix_end = aplan.iloc[i + 1]['end_unix'] if i < len(aplan) - 1 else next_day # 프로그램의 마지막 시점\n", " r = 0\n", " while current_time <= unix_end:\n", " remainder = current_time % 10\n", " if remainder != 0: # 현재시각의 일의자리가 0이 아니면 다음 현재시각의 일이자리가 0이 되도록 맞춰준다.\n", " r = (5 - remainder) % 10 - 5 # 1>-1, 2>-2, 3>-3, 4>-4, 5>-5, 6>4, 7>3, 8>2, 9>1\n", " else:\n", " r = random.choices([0, -1, 1], weights=[10, 1, 1])[0] # 측정오차\n", " # 0.002의 확률로 결측 발생\n", " if random.random() < 0.001: # 주기의 배수만큼 결측\n", " n = random.randint(0,100)\n", " current_time += n * cycle + r\n", " elif random.random() > 0.999:\n", " n = random.randint(0,100) # 주기의 배수가 아닌 값으로 결측\n", " current_time += n * cycle + int(random.random() * cycle) + r\n", " else:\n", " current_time += cycle + r # 결측 없음\n", " new_row = first_row.copy()\n", " new_row['end_unix'] = current_time\n", " rows.append(new_row)\n", " ahistory = pd.concat(rows, axis=1).transpose().reset_index(drop=True) # a (specific) history\n", " history.append(ahistory)\n", "history = pd.concat(history)\n", "history = history[history.end_unix <= next_day]\n", "history = history.sort_values(by='end_unix').reset_index(drop=True)\n", "history.to_csv('../history.csv')\n", "display(history[:30])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# df = history.sort_values(by=['inter_no','end_unix'])\n", "# df['diff'] = df.end_unix.diff()\n", "# with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n", "# display(df)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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inter_nonode_idinter_type
0175i0parent
1175u00child
2176i1parent
3177i2parent
4177u20child
5178i3parent
6178u30child
7178u31child
8178u32child
9201i8parent
10202i9parent
11206i7parent
12210i6parent
13210u60child
14211c30parent
\n", "
" ], "text/plain": [ " inter_no node_id inter_type\n", "0 175 i0 parent\n", "1 175 u00 child\n", "2 176 i1 parent\n", "3 177 i2 parent\n", "4 177 u20 child\n", "5 178 i3 parent\n", "6 178 u30 child\n", "7 178 u31 child\n", "8 178 u32 child\n", "9 201 i8 parent\n", "10 202 i9 parent\n", "11 206 i7 parent\n", "12 210 i6 parent\n", "13 210 u60 child\n", "14 211 c30 parent" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 교차로-노드 매칭\n", "uturn = pd.read_csv('U_TURN.csv')\n", "inter_node = pd.read_csv('inter.csv')\n", "inter_node = inter_node[['INT_NO', 'node_id', 'P/C']]\n", "# inter_node.drop(index=len(inter_node) - 1, inplace=True)\n", "inter_node.columns = ['inter_no', 'node_id', 'inter_type'] # parent : 주교차로, child : 부교차로\n", "inter_node['inter_no'] = inter_node['inter_no'].astype(int) # i : intersection, u : u-turn intersection, c : coupling intersection\n", "inter_node.iloc[-1] = pd.Series([211, 'c30', 'parent'])\n", "inter_node.to_csv('../inter_node.csv')\n", "display(inter_node)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# 부교차로 정보\n", "# 지금은 생략 (23. 1. 9. 09:32)" ] } ], "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 }