From 8d5f71e175592644897624d314f735a8ace3dd83 Mon Sep 17 00:00:00 2001 From: govin08 Date: Wed, 7 Feb 2024 23:06:28 +0900 Subject: [PATCH] revised generate_signal.py for speed issue --- .../0207_unit_test_on_generate_signal.ipynb | 5771 ++++++++ Analysis/0207_unit_test/histids.csv | 369 + Analysis/0207_unit_test/hrhists.csv | 313 + Analysis/0207_unit_test/sigtable.csv | 369 + Analysis/0207_unit_test/ssigtable.csv | 239 + .../0207_uturn_code_change.ipynb | 548 + Archives/Scripts/generate_signals_2.py | 826 ++ Archives/Scripts/preprocess_daily.ipynb | 2 +- Data/tables/20240207_140433/S_INT_CONFIG.csv | 759 ++ .../20240207_140433/S_INT_PHASE_CONFIG.csv | 3909 ++++++ Data/tables/20240207_140433/S_INT_TPLAN.csv | 5 + .../20240207_140433/S_SA_CYCLE_PLAN.csv | 8 + Data/tables/20240207_140433/S_SA_DPLAN.csv | 18 + Data/tables/20240207_140433/S_SA_WPLAN.csv | 18 + Data/tables/20240207_140433/S_TOD_HIS.csv | 11099 ++++++++++++++++ Data/tables/20240207_140459/S_INT_CONFIG.csv | 759 ++ 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changed, 74415 insertions(+), 1840 deletions(-) create mode 100644 Analysis/0207_unit_test/0207_unit_test_on_generate_signal.ipynb create mode 100644 Analysis/0207_unit_test/histids.csv create mode 100644 Analysis/0207_unit_test/hrhists.csv create mode 100644 Analysis/0207_unit_test/sigtable.csv create mode 100644 Analysis/0207_unit_test/ssigtable.csv create mode 100644 Analysis/0207_uturn_code_change/0207_uturn_code_change.ipynb create mode 100644 Archives/Scripts/generate_signals_2.py create mode 100644 Data/tables/20240207_140433/S_INT_CONFIG.csv create mode 100644 Data/tables/20240207_140433/S_INT_PHASE_CONFIG.csv create mode 100644 Data/tables/20240207_140433/S_INT_TPLAN.csv create mode 100644 Data/tables/20240207_140433/S_SA_CYCLE_PLAN.csv create mode 100644 Data/tables/20240207_140433/S_SA_DPLAN.csv create mode 100644 Data/tables/20240207_140433/S_SA_WPLAN.csv create mode 100644 Data/tables/20240207_140433/S_TOD_HIS.csv create mode 100644 Data/tables/20240207_140459/S_INT_CONFIG.csv create mode 100644 Data/tables/20240207_140459/S_INT_PHASE_CONFIG.csv create mode 100644 Data/tables/20240207_140459/S_INT_TPLAN.csv create mode 100644 Data/tables/20240207_140459/S_SA_CYCLE_PLAN.csv create mode 100644 Data/tables/20240207_140459/S_SA_DPLAN.csv create mode 100644 Data/tables/20240207_140459/S_SA_WPLAN.csv create mode 100644 Data/tables/20240207_140459/S_TOD_HIS.csv create mode 100644 Data/tables/20240207_221605/S_INT_CONFIG.csv create mode 100644 Data/tables/20240207_221605/S_INT_PHASE_CONFIG.csv create mode 100644 Data/tables/20240207_221605/S_INT_TPLAN.csv create mode 100644 Data/tables/20240207_221605/S_SA_CYCLE_PLAN.csv create mode 100644 Data/tables/20240207_221605/S_SA_DPLAN.csv create mode 100644 Data/tables/20240207_221605/S_SA_WPLAN.csv create mode 100644 Data/tables/20240207_221605/S_TOD_HIS.csv create mode 100644 Documents/김선중_신호생성(240207).pptx create mode 100644 Documents/신호생성프로세스검토_데이터정의(240207).pptx create mode 100644 Results/sn_1704417900.add.xml create mode 100644 Results/sn_1704420300.add.xml create mode 100644 Results/sn_1704420600.add.xml create mode 100644 Results/sn_1704422100.add.xml create mode 100644 Results/sn_1704423300.add.xml create mode 100644 Results/sn_1704423600.add.xml create mode 100644 Results/sn_1704423900.add.xml create mode 100644 Results/sn_1704446400.add.xml create mode 100644 Results/sn_1704450300.add.xml create mode 100644 Results/sn_1704452400.add.xml create mode 100644 Results/sn_1704452700.add.xml create mode 100644 Results/sn_1704453000.add.xml create mode 100644 Results/sn_1704453900.add.xml create mode 100644 Results/sn_1704454200.add.xml create mode 100644 Results/sn_1704454500.add.xml create mode 100644 Results/sn_1704454800.add.xml create mode 100644 Results/sn_1704459000.add.xml create mode 100644 Results/sn_1704459600.add.xml create mode 100644 Results/sn_1704459900.add.xml create mode 100644 Results/sn_1704460500.add.xml create mode 100644 Results/sn_1704460800.add.xml create mode 100644 Results/sn_1704461100.add.xml create mode 100644 Results/sn_1704461400.add.xml create mode 100644 Scripts/__pycache__/generate_signals.cpython-38.pyc create mode 100644 Scripts/__pycache__/preprocess_daily.cpython-38.pyc diff --git a/Analysis/0207_unit_test/0207_unit_test_on_generate_signal.ipynb b/Analysis/0207_unit_test/0207_unit_test_on_generate_signal.ipynb new file mode 100644 index 000000000..c9da65b15 --- /dev/null +++ b/Analysis/0207_unit_test/0207_unit_test_on_generate_signal.ipynb @@ -0,0 +1,5771 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 단위테스트 2 : generate_signal.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. 개요" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 입력데이터 목록\n", + "- `sn.net.xml`\n", + "- `history.csv`\n", + "- `plan.csv`\n", + "- `inter_info.csv`\n", + "- `inter_node.csv`\n", + "- `plan_[timestamp].csv`\n", + "- `match1.csv`\n", + "- `match6.csv`\n", + "- `matching.csv`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 중간산출물 (데이터프레임) 목록\n", + "- 신호이력 관련\n", + " - `rhistory`\n", + " - `rhists`\n", + " - `hrhists`\n", + "- 이동류 관련\n", + " - `movement`\n", + " - `movement_updated`\n", + "- 통합테이블 관련\n", + " - `histid`\n", + " - `histids`\n", + " - `sigtable`\n", + " - `Sigtable`\n", + " - `SIGTABLE`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 출력데이터\n", + "- `sn_[timestamp].add.xml`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 단위테스트 개요\n", + "주요 중간산출물 및 출력데이터가 입력데이터의 내용을 잘 반영하는지 살핀다." + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. 데이터를 준비합니다.\n", + "1-1. 네트워크가 로드되었습니다.\n", + "1-2. 테이블들이 로드되었습니다.\n", + "2. 신호이력 테이블을 변환합니다.\n", + "3. 이동류정보 테이블을 변환합니다.\n", + "4. 통합 테이블을 생성합니다.\n", + "5. 신호를 생성합니다.\n", + "6. 이슈사항을 저장합니다.\n" + ] + } + ], + "source": [ + "import sys\n", + "from datetime import datetime\n", + "sys.path.append('../../Scripts')\n", + "from generate_signals import SignalGenerator\n", + "self = SignalGenerator()\n", + "self.present_time = int(datetime(2024, 1, 5, 12, 5).timestamp())\n", + "\n", + "# 1. 데이터 준비\n", + "self.prepare_data()\n", + "# 2. 신호이력 전처리\n", + "self.process_history()\n", + "# 3. 이동류정보 전처리\n", + "self.process_movement()\n", + "# 4. 통합테이블 생성\n", + "self.make_histids()\n", + "# 5. 신호 생성\n", + "self.get_signals()\n", + "# 6. 이슈사항 저장\n", + "self.write_issues()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# # 하나의 교차로에 대해서 단위테스트\n", + "# inter_no = 175\n", + "# # 입력 데이터\n", + "# history = self.history[self.history.inter_no==inter_no]\n", + "# plan = self.plan[self.plan.inter_no==inter_no]\n", + "# inter_info = self.inter_info[self.inter_info.inter_no==inter_no]\n", + "# inter_node = self.inter_node[self.inter_node.inter_no==inter_no]\n", + "# # 주교차로id와 부교차로id\n", + "# the_inter_node = inter_node[inter_node.inter_type=='parent']\n", + "# the_parent_id = the_inter_node.node_id.iloc[0]\n", + "# the_child_ids = list(inter_node[inter_node.inter_type=='child'].node_id)\n", + "# the_node_ids = [the_parent_id] + the_child_ids\n", + "# # 그외 입력 데이터\n", + "# match1 = self.match1[self.match1.inter_no==inter_no]\n", + "# match6 = self.match6[self.match6.node_id.isin(the_node_ids)]\n", + "# matching = self.matching[self.matching.node_id.isin(the_node_ids)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. 단위테스트" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 신호이력 관련 데이터프레임 (`rhistory`, `rhists`, `hrhists`)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.1.1 `rhistory`\n", + "1. 종료유닉스 → 시작유닉스 변환\n", + "2. 현재시점으로부터 2시간 반 동안의 신호이력 로드\n", + "3. 현재시각 + 10분에 한 주기 신호 추가\n", + "4. 존재하지 않는 교차로에 대하여 한 주기 신호 추가" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " inter_no start_unix dura_A1 dura_A2 dura_A3 dura_A4 dura_A5 dura_A6 \\\n", + "0 176 1704414741 37 103 40 0 0 0 \n", + "1 206 1704414810 45 53 26 26 0 0 \n", + "2 202 1704414810 46 114 0 0 0 0 \n", + "3 210 1704414800 43 43 70 24 0 0 \n", + "4 178 1704414820 38 39 43 50 0 0 \n", + "\n", + " dura_A7 dura_A8 dura_B1 dura_B2 dura_B3 dura_B4 dura_B5 dura_B6 \\\n", + "0 0 0 37 103 40 0 0 0 \n", + "1 0 0 45 53 26 26 0 0 \n", + "2 0 0 46 114 0 0 0 0 \n", + "3 0 0 28 58 70 24 0 0 \n", + "4 0 0 38 39 71 22 0 0 \n", + "\n", + " dura_B7 dura_B8 cycle \n", + "0 0 0 180 \n", + "1 0 0 150 \n", + "2 0 0 160 \n", + "3 0 0 180 \n", + "4 0 0 170 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9609\n", + "1704423900 1704424350\n", + "1704423900 1704424350\n" + ] + } + ], + "source": [ + "display(self.history.head())\n", + "display(self.rhistory.head())\n", + "# 1 : 검증완료\n", + "# 2 : 검증완료\n", + "print(self.rhistory.start_unix.max() - self.rhistory.start_unix.min())\n", + "# 3 : 완료\n", + "print(self.present_time, self.rhistory.start_unix.max())\n", + "# 4 : 검증하지 않음. 이전에 검증했음.\n", + "print(self.present_time, self.rhistory.start_unix.max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.1.2 `rhists`\n", + "1. 결측치 처리\n", + "2. 이상치 처리 (삭제, 대체)\n", + "3. 현재시점으로부터 한시간 슬라이싱" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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inter_nostart_unixdura_A1dura_A2dura_A3dura_A4dura_A5dura_A6dura_A7dura_A8...dura_B2dura_B3dura_B4dura_B5dura_B6dura_B7dura_B8cycleUnnamed: 0diff
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25 rows × 21 columns

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" + ], + "text/plain": [ + " inter_no start_unix dura_A1 dura_A2 dura_A3 dura_A4 dura_A5 \\\n", + "35 202 1704420410 46 114 0 0 0 \n", + "36 202 1704420640 66 164 0 0 0 \n", + "37 202 1704420800 46 114 0 0 0 \n", + "38 202 1704420960 46 114 0 0 0 \n", + "39 202 1704421120 46 114 0 0 0 \n", + "40 202 1704421281 46 115 0 0 0 \n", + "41 202 1704421440 46 113 0 0 0 \n", + "42 202 1704421600 46 114 0 0 0 \n", + "43 202 1704421760 46 114 0 0 0 \n", + "44 202 1704421920 46 114 0 0 0 \n", + "45 202 1704422080 46 114 0 0 0 \n", + "46 202 1704422239 46 113 0 0 0 \n", + "47 202 1704422400 46 115 0 0 0 \n", + "48 202 1704422560 46 114 0 0 0 \n", + "49 202 1704422720 46 114 0 0 0 \n", + "50 202 1704422880 46 114 0 0 0 \n", + "51 202 1704423040 46 114 0 0 0 \n", + "52 202 1704423200 46 114 0 0 0 \n", + "53 202 1704423360 46 114 0 0 0 \n", + "54 202 1704423520 46 114 0 0 0 \n", + "55 202 1704423680 46 114 0 0 0 \n", + "56 202 1704423840 46 114 0 0 0 \n", + "57 202 1704424000 46 114 0 0 0 \n", + "58 202 1704424160 46 114 0 0 0 \n", + "59 202 1704424320 46 114 0 0 0 \n", + "\n", + " dura_A6 dura_A7 dura_A8 ... dura_B2 dura_B3 dura_B4 dura_B5 \\\n", + "35 0 0 0 ... 114 0 0 0 \n", + "36 0 0 0 ... 164 0 0 0 \n", + "37 0 0 0 ... 114 0 0 0 \n", + "38 0 0 0 ... 114 0 0 0 \n", + "39 0 0 0 ... 114 0 0 0 \n", + "40 0 0 0 ... 115 0 0 0 \n", + "41 0 0 0 ... 113 0 0 0 \n", + "42 0 0 0 ... 114 0 0 0 \n", + "43 0 0 0 ... 114 0 0 0 \n", + "44 0 0 0 ... 114 0 0 0 \n", + "45 0 0 0 ... 114 0 0 0 \n", + "46 0 0 0 ... 113 0 0 0 \n", + "47 0 0 0 ... 115 0 0 0 \n", + "48 0 0 0 ... 114 0 0 0 \n", + "49 0 0 0 ... 114 0 0 0 \n", + "50 0 0 0 ... 114 0 0 0 \n", + "51 0 0 0 ... 114 0 0 0 \n", + "52 0 0 0 ... 114 0 0 0 \n", + "53 0 0 0 ... 114 0 0 0 \n", + "54 0 0 0 ... 114 0 0 0 \n", + "55 0 0 0 ... 114 0 0 0 \n", + "56 0 0 0 ... 114 0 0 0 \n", + "57 0 0 0 ... 114 0 0 0 \n", + "58 0 0 0 ... 114 0 0 0 \n", + "59 0 0 0 ... 114 0 0 0 \n", + "\n", + " dura_B6 dura_B7 dura_B8 cycle Unnamed: 0 diff \n", + "35 0 0 0 160 NaN NaN \n", + "36 0 0 0 230 NaN 230.0 \n", + "37 0 0 0 160 NaN 160.0 \n", + "38 0 0 0 160 NaN 160.0 \n", + "39 0 0 0 160 NaN 160.0 \n", + "40 0 0 0 161 NaN 161.0 \n", + "41 0 0 0 159 NaN 159.0 \n", + "42 0 0 0 160 NaN 160.0 \n", + "43 0 0 0 160 NaN 160.0 \n", + "44 0 0 0 160 NaN 160.0 \n", + "45 0 0 0 160 NaN 160.0 \n", + "46 0 0 0 159 NaN 159.0 \n", + "47 0 0 0 161 NaN 161.0 \n", + "48 0 0 0 160 NaN 160.0 \n", + "49 0 0 0 160 NaN 160.0 \n", + "50 0 0 0 160 NaN 160.0 \n", + "51 0 0 0 160 NaN 160.0 \n", + "52 0 0 0 160 NaN 160.0 \n", + "53 0 0 0 160 NaN 160.0 \n", + "54 0 0 0 160 NaN 160.0 \n", + "55 0 0 0 160 NaN 160.0 \n", + "56 0 0 0 160 22.0 160.0 \n", + "57 0 0 0 160 22.0 160.0 \n", + "58 0 0 0 160 22.0 160.0 \n", + "59 0 0 0 160 22.0 160.0 \n", + "\n", + "[25 rows x 21 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 2 : 이상치 처리(삭제 후 대체)가 잘 이루어졌음이 확인됨 (1704420410 ~ 1704420475 ~ 1704420640)\n", + "inter_no = 202\n", + "arhistory = self.rhistory.copy()[self.rhistory.inter_no==inter_no]\n", + "arhistory = arhistory.reset_index(drop=True)\n", + "arhistory['diff'] = arhistory['start_unix'].diff()\n", + "display(arhistory)\n", + "arhists = self.rhists.copy()[self.rhists.inter_no==inter_no]\n", + "arhists['diff'] = arhists['start_unix'].diff()\n", + "display(arhists)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4050" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 3 rhists의 시간간격이 약 1시간임이 확인됨\n", + "self.rhists.start_unix.max() - self.rhists.start_unix.min()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.1.2 `hrhists`\n", + "1. 계층화" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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inter_nostart_unixphas_Aphas_Bduration
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..................
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798 rows × 5 columns

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" + ], + "text/plain": [ + " inter_no start_unix phas_A phas_B duration\n", + "0 201.0 1.704420e+09 1 1 33.0\n", + "1 201.0 1.704420e+09 2 2 36.0\n", + "2 201.0 1.704420e+09 3 3 25.0\n", + "3 201.0 1.704420e+09 4 4 58.0\n", + "4 201.0 1.704420e+09 5 5 18.0\n", + ".. ... ... ... ... ...\n", + "793 201.0 1.704424e+09 5 5 13.0\n", + "794 206.0 1.704424e+09 1 1 27.0\n", + "795 206.0 1.704424e+09 2 2 32.0\n", + "796 206.0 1.704424e+09 3 3 16.0\n", + "797 206.0 1.704424e+09 4 4 16.0\n", + "\n", + "[798 rows x 5 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 계층화가 잘 이루어짐\n", + "self.hrhists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 이동류 관련 데이터프레임 (`movement`, `movement_updated`)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.2.1 `movement`\n", + "5초마다 move 조회 후 신호이력에서 start_unix 가져옴\n", + "1. 각 5초마다 move 정보가 잘 반영되었음.\n", + "2. start_unix 정보가 잘 반영되었음" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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inter_nophas_Aphas_Bmove_Amove_B
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01761184
01771184
01782273
02013362
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021011618
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" + ], + "text/plain": [ + " inter_no phas_A phas_B move_A move_B\n", + "0 175 1 1 8 4\n", + "0 176 1 1 8 4\n", + "0 177 1 1 8 4\n", + "0 178 2 2 7 3\n", + "0 201 3 3 6 2\n", + "0 202 2 2 17 18\n", + "0 206 2 2 17 18\n", + "0 210 1 1 6 18" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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inter_nophas_Aphas_Bmove_Amove_B
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" + ], + "text/plain": [ + " inter_no phas_A phas_B move_A move_B\n", + "0 175 4 4 5 2\n", + "0 176 3 3 5 18\n", + "0 177 4 4 5 1\n", + "0 178 3 3 5 2\n", + "0 201 4 4 6 1\n", + "0 202 2 2 17 18\n", + "0 206 2 2 17 18\n", + "0 210 4 4 8 3" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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inter_nophas_Aphas_Bmove_Amove_B
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01771184
01783352
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" + ], + "text/plain": [ + " inter_no phas_A phas_B move_A move_B\n", + "0 175 4 4 5 2\n", + "0 176 1 1 8 4\n", + "0 177 1 1 8 4\n", + "0 178 3 3 5 2\n", + "0 201 4 4 6 1\n", + "0 202 2 2 17 18\n", + "0 206 2 2 17 18\n", + "0 210 1 1 6 18" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(pd.read_csv(f'../../data/tables/move/move_{self.present_time - 600}.csv', index_col=0))\n", + "display(pd.read_csv(f'../../data/tables/move/move_{self.present_time - 5}.csv', index_col=0))\n", + "display(pd.read_csv(f'../../data/tables/move/move_{self.present_time}.csv', index_col=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# os.listdir('../../data/tables/move')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1704423300\n", + "1704423900\n", + "1704420340\n", + "1704423880\n" + ] + } + ], + "source": [ + "# \n", + "print(self.present_time - 600)\n", + "print(self.present_time)\n", + "print(self.movement.start_unix.min())\n", + "print(self.movement.start_unix.max())" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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inter_nophas_Aphas_Bmove_Amove_Bstart_unix
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.....................
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634 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " inter_no phas_A phas_B move_A move_B start_unix\n", + "0 176 1 1 8 4 1704420340\n", + "1 176 2 2 8 3 1704420340\n", + "2 176 3 3 5 18 1704420340\n", + "3 178 1 1 8 4 1704420360\n", + "4 178 2 2 7 3 1704420360\n", + ".. ... ... ... ... ... ...\n", + "629 210 3 3 7 4 1704423861\n", + "630 210 4 4 8 3 1704423861\n", + "631 177 1 1 8 4 1704423870\n", + "632 177 4 4 5 1 1704423870\n", + "633 178 3 3 5 2 1704423880\n", + "\n", + "[634 rows x 6 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.movement" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['1107_intermediate_data', '1107_movements', '1108_intermediate_data', '1110_intermediate_data', '1113_intermediate_data', '1113_INT_NOs', '1113_movements', '1113_SN_limits', '1114_intermediate_data', '1115_intermediate_data', '1115_node2inter_info', '1116_intermediate_data', '1117_intermediate_data', '1120_intermediate_data', '1122_intermediate_data', '1123_intermediate_data', '1129_intermediate_data', '1130_intermediate_data', '1204_intermediate_data']\n", + "['analysis', 'inter2node.json', 'networks', 'parent2child.json', 'tables']\n" + ] + } + ], + "source": [ + "print(os.listdir('../../Intermediates'))\n", + "print(os.listdir('../../Data'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rts", + "language": "python", + "name": "rts" + }, + "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/Analysis/0207_unit_test/histids.csv b/Analysis/0207_unit_test/histids.csv new file mode 100644 index 000000000..72e1d0fff --- /dev/null +++ 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+294,u60,1704461650,4,25,ggggggggr,2024-01-05 22:34:10 diff --git a/Analysis/0207_uturn_code_change/0207_uturn_code_change.ipynb b/Analysis/0207_uturn_code_change/0207_uturn_code_change.ipynb new file mode 100644 index 000000000..615b0654d --- /dev/null +++ b/Analysis/0207_uturn_code_change/0207_uturn_code_change.ipynb @@ -0,0 +1,548 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. 데이터를 로드합니다.\n", + "1-1. 네트워크가 로드되었습니다.\n", + "1-2. 테이블들이 로드되었습니다.\n", + "1-3. 네트워크의 모든 clean state requirement들을 체크했습니다.\n", + "1-4. 테이블들의 무결성 검사를 완료했습니다.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "이동류정보 불러오는 중: 100%|██████████| 17280/17280 [00:13<00:00, 1247.47it/s]\n" + ] + } + ], + "source": [ + "import sys\n", + "from datetime import datetime\n", + "sys.path.append('../../Scripts')\n", + "from preprocess_daily import DailyPreprocessor\n", + "self = DailyPreprocessor()\n", + "\n", + "# 1. 데이터 준비\n", + "self.load_data()\n", + "\n", + "self.make_match1()\n", + "self.make_match2()\n", + "self.make_match3()\n", + "self.make_match4()\n", + "self.make_match5()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "u00 True\n", + "u20 False\n", + "u30 True\n", + "u31 True\n", + "u32 True\n", + "u60 True\n" + ] + } + ], + "source": [ + "self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no']))\n", + "\n", + "child_ids = self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()\n", + "ch2pa = {} # child to parent\n", + "for child_id in child_ids:\n", + " parent_no = self.inter_node[self.inter_node.node_id==child_id].inter_no.iloc[0]\n", + " sub_inter_node = self.inter_node[self.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 self.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 = self.match5.copy()[self.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 == \"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", + " print(child_id, ((cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A)).any())\n", + " # (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함\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", + " if condition == '보행신호시':\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", + " # 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", + "self.coord['inter_no'] = self.coord['parent_id'].map(self.node2inter)\n", + "self.coord = self.coord.rename(columns={'child_id':'node_id'})\n", + "self.coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan\n", + "self.coord['move_no'] = 20\n", + "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']]\n", + "\n", + "# display(coord)\n", + "cmatches = pd.concat(cmatches)\n", + "self.match6 = pd.concat([self.match5, cmatches, self.coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type'])\n", + "# self.match6.to_csv(os.path.join(self.path_intermediates, 'match6.csv'))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.match6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "splits 딕셔너리 다시 만들기" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dura_A1 37\n", + "dura_A2 39\n", + "dura_A3 55\n", + "dura_A4 29\n", + "dura_A5 0\n", + "dura_A6 0\n", + "dura_A7 0\n", + "dura_A8 0\n", + "Name: 0, dtype: int64\n", + "dura_B1 37\n", + "dura_B2 39\n", + "dura_B3 25\n", + "dura_B4 59\n", + "dura_B5 0\n", + "dura_B6 0\n", + "dura_B7 0\n", + "dura_B8 0\n", + "Name: 0, dtype: int64\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'dura_A' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[53], line 11\u001b[0m\n\u001b[0;32m 9\u001b[0m duration_dict \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m# 두 시리즈의 길이가 같다고 가정합니다.\u001b[39;00m\n\u001b[1;32m---> 11\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[43mdura_A\u001b[49m)):\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# A와 B의 현시간이 같은 경우\u001b[39;00m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dura_A[i] \u001b[38;5;241m==\u001b[39m dura_B[i]:\n\u001b[0;32m 14\u001b[0m duration_dict[(i\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m, i\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)] \u001b[38;5;241m=\u001b[39m dura_A[i]\n", + "\u001b[1;31mNameError\u001b[0m: name 'dura_A' is not defined" + ] + } + ], + "source": [ + "import numpy as np\n", + "row = self.plan.iloc[0]\n", + "inter_no = row.inter_no\n", + "start_hour = row.start_hour\n", + "start_minute = row.start_minute\n", + "cycle = row.cycle\n", + "print(row[[f'dura_A{j}' for j in range(1,9)]])\n", + "print(row[[f'dura_B{j}' for j in range(1,9)]])\n", + "\n", + "\n", + "dura_A = row[[f'dura_A{j}' for j in range(1, 9)]]\n", + "dura_B = row[[f'dura_B{j}' for j in range(1, 9)]]\n", + "\n", + "duration_dict = {}\n", + "# 두 시리즈의 길이가 같다고 가정합니다.\n", + "for i in range(len(dura_A)):\n", + " # A와 B의 현시간이 같은 경우\n", + " if dura_A[i] == dura_B[i]:\n", + " duration_dict[(i+1, i+1)] = dura_A[i]\n", + " # A와 B의 현시간이 다른 경우\n", + " else:\n", + " duration_dict[(i+1, i+1)] = min(dura_A[i], dura_B[i])\n", + " duration_dict[(i+1, i+2)] = max(dura_A[i], dura_B[i])\n", + "\n", + "print(duration_dict)\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", + "# print(cums_A)\n", + "# print(cums_B)\n", + "# detailed_cums = []\n", + "# combined_row = np.unique(np.concatenate((cums_A,cums_B)))\n", + "# print(combined_row)\n", + "# detailed_durations = np.concatenate(([combined_row[0]], np.diff(combined_row)))\n", + "# print(detailed_durations)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# split, isplit : A,B 분리 혹은 통합시 사용될 수 있는 딕셔너리 \n", + "self.splits = {} # splits maps (inter_no, start_hour, start_minute) to split \n", + "for i, row in self.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", + " self.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", + " self.splits[(inter_no, start_hour, start_minute)][(phas_A, phas_B)] = k\n", + "self.isplits = {} # the inverse of splits\n", + "for i in self.splits:\n", + " self.isplits[i] = {self.splits[i][k]:k for k in self.splits[i]} # isplit maps k to (phas_A, phas_B)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(175, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (3, 4): 4, (4, 4): 5},\n", + " (175, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (3, 4): 4, (4, 4): 5},\n", + " (175, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (3, 4): 4, (4, 4): 5},\n", + " (175, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (3, 4): 4, (4, 4): 5},\n", + " (176, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3},\n", + " (176, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3},\n", + " (176, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3},\n", + " (176, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3},\n", + " (177, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n", + " (177, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n", + " (177, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n", + " (177, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n", + " (178, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n", + " (178, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 3): 4, (4, 4): 5},\n", + " (178, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 3): 4, (4, 4): 5},\n", + " (178, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 3): 4, (4, 4): 5},\n", + " (201, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4, (5, 5): 5},\n", + " (201, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4, (5, 5): 5},\n", + " (201, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4, (5, 5): 5},\n", + " (201, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4, (5, 5): 5},\n", + " (202, 0, 0): {(1, 1): 1, (2, 2): 2},\n", + " (202, 7, 0): {(1, 1): 1, (2, 2): 2},\n", + " (202, 9, 0): {(1, 1): 1, (2, 2): 2},\n", + " (202, 18, 30): {(1, 1): 1, (2, 2): 2},\n", + " (206, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n", + " (206, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n", + " (206, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n", + " (206, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n", + " (210, 0, 0): {(1, 1): 1, (1, 2): 2, (2, 2): 3, (3, 3): 4, (4, 4): 5},\n", + " (210, 7, 0): {(1, 1): 1, (1, 2): 2, (2, 2): 3, (3, 3): 4, (4, 4): 5},\n", + " (210, 9, 0): {(1, 1): 1, (1, 2): 2, (2, 2): 3, (3, 3): 4, (4, 4): 5},\n", + " (210, 18, 30): {(1, 1): 1, (1, 2): 2, (2, 2): 3, (3, 3): 4, (4, 4): 5}}" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.splits" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rts", + "language": "python", + "name": "rts" + }, + "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/Archives/Scripts/generate_signals_2.py b/Archives/Scripts/generate_signals_2.py new file mode 100644 index 000000000..56e01acc5 --- /dev/null +++ b/Archives/Scripts/generate_signals_2.py @@ -0,0 +1,826 @@ +# (siggen) PS C:\Github\snits_siggen> python .\Scripts\generate_signals.py +import pandas as pd +import numpy as np +import os, sys +import json +import copy +from tqdm import tqdm +import sumolib, traci +from datetime import datetime +import time + +class SignalGenerator(): + def __init__(self): + # 루트폴더 지정 + self.path_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + with open(os.path.join(self.path_root, 'Scripts', 'config.json'), 'r') as config_file: + config = json.load(config_file) + # 주요 폴더 경로 지정 + self.paths = config['paths'] + self.path_data = os.path.join(self.path_root, *self.paths['data']) + self.path_intermediates = os.path.join(self.path_root, *self.paths['intermediates']) + self.path_results = os.path.join(self.path_root, *self.paths['results']) + self.path_tables = os.path.join(self.path_root, *self.paths['tables']) + self.path_networks = os.path.join(self.path_root, *self.paths['networks']) + self.path_scripts = os.path.join(self.path_root, *self.paths['scripts']) + # 이슈사항 목록 + self.issues = [] + + self.midnight = int(datetime(2024, 1, 5, 0, 0, 0).timestamp()) + self.next_day = int(datetime(2024, 1, 6, 0, 0, 0).timestamp()) + self.fsecs = range(self.midnight, self.next_day, 5) # fsecs : unix time by Five SECondS + self.fmins = range(self.midnight, self.next_day, 300) # fmins : unix time by Five MINuteS + + self.present_time = datetime.now().replace(month=1, day=5).timestamp() + self.present_time = max([fmin for fmin in list(self.fmins) if fmin <= self.present_time]) + + self.adder = 600 + + # 1. 데이터 준비 + def prepare_data(self): + print("1. 데이터를 준비합니다.") + self.load_networks() + self.load_tables() + # self.check_networks() + # self.check_tables() + self.prepare_auxiliaries() + + # 1-1. 네트워크 불러오기 + def load_networks(self): + self.net = sumolib.net.readNet(os.path.join(self.path_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' + + # 테이블 불러오기 + self.inter_info = pd.read_csv(os.path.join(self.path_tables, 'inter_info.csv'), dtype=loading_dtype) + self.plan = pd.read_csv(os.path.join(self.path_tables, 'plan.csv'), dtype=loading_dtype) + self.history = pd.read_csv(os.path.join(self.path_tables, 'history.csv'), dtype=loading_dtype) + self.inter_node = pd.read_csv(os.path.join(self.path_tables, 'inter_node.csv'), dtype=loading_dtype) + self.matching = pd.read_csv(os.path.join(self.path_intermediates, 'matching.csv'), dtype=loading_dtype) + self.match1 = pd.read_csv(os.path.join(self.path_intermediates, 'match1.csv'), dtype=loading_dtype) + self.match6 = pd.read_csv(os.path.join(self.path_intermediates, 'match6.csv'), dtype=loading_dtype) + self.match6 = self.match6[['node_id', 'phase_no', 'ring_type', 'inc_edge', 'out_edge']].reset_index(drop=True) + + # 교차로목록 정의 + self.inter_nos = sorted(self.inter_info.inter_no.unique()) + print("1-2. 테이블들이 로드되었습니다.") + + # 1-3. 네트워크 무결성 검사 + 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_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. 테이블 무결성 검사 + def check_tables(self): + self.check_history() + # 교차로정보, 방위각정보, 신호계획에 대해서는 preprocess_daily.py에서 + # 무결성검사를 완료했으므로 여기에서는 따로 검사하지 않음. + # self.check_moves() # 이동류번호에 대한 무결성검사 필요하나 아직 작성하지 않음. (24. 2. 5 화) + print("1-4. 테이블들의 무결성 검사를 완료했습니다.") + + # 1-4-1. 신호이력(history) 검사 + def check_history(self): + # 1-4-1-1. inter_no 검사 + # self.history.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드 + missing_inter_nos = set(self.history.inter_no) - set(self.inter_nos) + if missing_inter_nos: + msg = f"1-4-1-1. history의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}" + self.issues.append(msg) + + # 1-4-1-2. 종료유닉스 검사 + # self.history.loc[0, 'end_unix'] = 38.0 # 에러 발생을 위한 코드 + self.min_unix, self.max_unix = int(datetime(2020, 1, 1).timestamp()), int(datetime(2038, 1, 1).timestamp()) + for _, row in self.history.iterrows(): + unixbool = self.min_unix <= row['end_unix'] <= self.max_unix + if not unixbool: + msg = f"1-4-1-2. 적정 범위를 벗어난 유닉스시각(end_unix)이 존재합니다 : inter_no : {row['inter_no']}" + self.issues.append(msg) + + # 1-4-1-3. 현시시간 검사 + # self.history.loc[0, 'dura_A1'] = -2 # 에러 발생을 위한 코드 + durations = self.history[[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.history[~ valid_indices].inter_no.unique()) + if invalid_inter_nos: + msg = f"1-4-1-3. 음수이거나 200보다 큰 현시시간이 존재합니다. : {invalid_inter_nos}" + + # 1-5. 보조 딕셔너리, 데이터프레임, 리스트 등 만들기 + def prepare_auxiliaries(self): + # inter2node : a dictionary that maps inter_no to the node_id + inter_node_p = self.inter_node[self.inter_node.inter_type=='parent'] + self.inter2node = dict(zip(inter_node_p['inter_no'], inter_node_p['node_id'])) + self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no'])) + + # hours : 정각에 해당하는 시각들 목록 + self.hours = np.array(range(self.midnight - 7200, self.next_day + 1, 3600)) + + # split, isplit : A,B 분리 혹은 통합시 사용될 수 있는 딕셔너리 + self.splits = {} # splits maps (inter_no, start_hour, start_minute) to split + for i, row in self.plan.iterrows(): + inter_no = row.inter_no + start_hour = row.start_hour + start_minute = row.start_minute + cycle = row.cycle + cums_A = row[[f'dura_A{j}' for j in range(1,9)]].cumsum() + cums_B = row[[f'dura_B{j}' for j in range(1,9)]].cumsum() + self.splits[(inter_no, start_hour, start_minute)] = {} # split maps (phas_A, phas_B) to k + k = 0 + for t in range(cycle): + new_phas_A = len(cums_A[cums_A < t]) + 1 + new_phas_B = len(cums_B[cums_B < t]) + 1 + if k == 0 or ((new_phas_A, new_phas_B) != (phas_A, phas_B)): + k += 1 + phas_A = new_phas_A + phas_B = new_phas_B + self.splits[(inter_no, start_hour, start_minute)][(phas_A, phas_B)] = k + self.isplits = {} # the inverse of splits + for i in self.splits: + self.isplits[i] = {self.splits[i][k]:k for k in self.splits[i]} # isplit maps k to (phas_A, phas_B) + + # timetable : 교차로별 프로그램 시작시각 + self.timetable = self.plan[['start_hour', 'start_minute']].drop_duplicates() + self.timetable['start_seconds'] = self.midnight + self.timetable['start_hour'] * 3600 + self.timetable['start_minute'] * 60 + + # A dictionary that maps parent_id to a list of child_ids + self.pa2ch = {'i0':['u00'], 'i1':[], 'i2':['u20'], 'i3':['c30', 'u30', 'u31', 'u32'], 'i6':['u60'], 'i7':[], 'i8':[], 'i9':[]} + self.node_ids = sorted(self.inter_node.node_id.unique()) + self.parent_ids = sorted(self.inter_node[self.inter_node.inter_type=='parent'].node_id.unique()) + self.nodes = [self.net.getNode(node_id) for node_id in self.node_ids] + + # node2num_cycles : A dictionary that maps a node_id to the number of cycles + with open(os.path.join(self.path_intermediates, 'node2num_cycles.json'), 'r') as file: + # json.load() 함수를 사용해 파일 내용을 Python 딕셔너리로 불러옵니다. + self.node2num_cycles = json.load(file) + + # 2. 신호이력 전처리 + def process_history(self): + print("2. 신호이력 테이블을 변환합니다.") + self.make_rhistory() + self.time21 = datetime.now() + self.make_rhists() + self.time22 = datetime.now() + self.make_hrhists() + self.time23 = datetime.now() + + # 2-1. rhistory + def make_rhistory(self): + # 1. 조회시점의 유닉스 타임 이전의 신호이력 수집 + self.rhistory = self.history.copy() # recent history + self.rhistory = self.rhistory[(self.rhistory.end_unix <= self.present_time) & (self.rhistory.end_unix > self.present_time - 9000)] # 두 시간 반 전부터 현재까지의 신호이력을 가져옴. 9000 = 3600 * 2.5 + + # rhistory에 모든 교차로번호가 존재하지 않으면 해당 교차로번호에 대한 신호이력을 추가함 (at 최근 프로그램 시작시각) + whole_inter_nos = sorted(self.history.inter_no.unique()) + recent_inter_nos = sorted(self.rhistory.inter_no.unique()) + if not whole_inter_nos==recent_inter_nos: + for inter_no in set(whole_inter_nos) - set(recent_inter_nos): + program_start, prow = self.load_prow(inter_no, self.present_time - 9000) + cycle = prow.cycle.iloc[0] + row1 = prow.drop(['start_hour', 'start_minute'], axis=1).copy() + row2 = prow.drop(['start_hour', 'start_minute'], axis=1).copy() + # prow에서 필요한 부분을 rhistory에 추가 + row1['end_unix'] = program_start + row2['end_unix'] = program_start + cycle + self.rhistory = pd.concat([self.rhistory, row1, row2]).reset_index(drop=True) + # present_time + adder 의 시각에 한 주기의 신호 추가 + for inter_no in set(whole_inter_nos): + program_start, prow = self.load_prow(inter_no, self.present_time) + cycle = prow.cycle.iloc[0] + row3 = prow.drop(['start_hour', 'start_minute'], axis=1).copy() + # prow에서 필요한 부분을 rhistory에 추가 + row3['end_unix'] = self.present_time + self.adder + self.rhistory = pd.concat([self.rhistory, row3]).reset_index(drop=True) + + # 2. 시작 유닉스 타임컬럼 생성 후 종류 유닉스 타임에서 현시별 현시기간 컬럼의 합을 뺀 값으로 입력 + # - 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 + for i, row in self.rhistory.iterrows(): + inter_no = row.inter_no + end_unix = row.end_unix + elapsed_time = row[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]].sum() // 2 # 현시시간 합 + # 이전 유닉스 존재하지 않음 : 현시시간 합의 차 + start_unix = end_unix - elapsed_time + pre_rows = self.history[:i] # previous rows + if inter_no in pre_rows.inter_no.unique(): # 이전 유닉스 존재 + pre_unix = pre_rows[pre_rows.inter_no == inter_no]['end_unix'].iloc[-1] # previous unix time + # 이전 유닉스 존재, abs < 10 : 이전 유닉스 + if abs(pre_unix - start_unix) < 10: + start_unix = pre_unix + # 이전 유닉스 존재, abs >=10 : 현시시간 합의 차 + else: + pass + self.rhistory.loc[i, 'start_unix'] = start_unix + self.rhistory[self.rhistory.isna()] = 0 + self.rhistory['start_unix'] = self.rhistory['start_unix'].astype(int) + self.rhistory = self.rhistory[['inter_no', 'start_unix'] + [f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)] + ['cycle']] + + def load_prow(self, inter_no, time): + ''' + load planned row + ''' + # 프로그램 시작시각 + program_starts = np.array(self.timetable.start_seconds) + idx = (program_starts <= time).sum() - 1 + program_start = program_starts[idx] + + # 최근 프로그램 시작시각에 대한 신호계획 + start_hour = self.timetable.iloc[idx].start_hour + start_minute = self.timetable.iloc[idx].start_minute + prow = self.plan[(self.plan.inter_no==inter_no) & (self.plan.start_hour==start_hour) & (self.plan.start_minute==start_minute)] # planned row + return program_start, prow + + # 2-2. rhists + def make_rhists(self): + self.rhists = [] + for inter_no in sorted(self.rhistory.inter_no.unique()): + filtered_rhist = self.rhistory[self.rhistory.inter_no == inter_no].drop_duplicates(subset=['start_unix']).reset_index(drop=True) + self.rhist = filtered_rhist + + # D_n 및 S_n 값 정의 + self.rhist['D_n'] = 0 # D_n : 시간차이 + self.rhist['S_n'] = 0 # S_n : 현시시간합 + for n in range(len(self.rhist)): + curr_unix = self.rhist.iloc[n].start_unix # current start_unix + self.rhist.loc[n, ['D_n', 'S_n']] = self.calculate_DS(self.rhist, curr_unix) + + # 이전시각, 현재시각 + prev_unix = self.rhist.loc[0, 'start_unix'] # previous start_unix + curr_unix = self.rhist.loc[1, 'start_unix'] # current start_unix + + # rhist의 마지막 행에 도달할 때까지 반복 + while True: + n = self.rhist[self.rhist.start_unix==curr_unix].index[0] + cycle = self.rhist.loc[n, 'cycle'] + D_n = self.rhist.loc[n, 'D_n'] + S_n = self.rhist.loc[n, 'S_n'] + # 참값인 경우 + if (abs(D_n - S_n) <= 5): + pass + # 참값이 아닌 경우 + else: + # 2-1-1. 결측치 처리 : 인접한 두 start_unix의 차이가 계획된 주기의 두 배보다 크면 결측이 일어났다고 판단, 신호계획의 현시시간으로 "대체" + if curr_unix - prev_unix >= 2 * cycle: + # prev_unix를 계획된 주기만큼 늘려가면서 한 행씩 채워나간다. + # (curr_unix와의 차이가 계획된 주기보다 작거나 같아질 때까지) + while curr_unix - prev_unix > cycle: + prev_unix += cycle + # 신호 계획(prow) 불러오기 + start_seconds = np.array(self.timetable.start_seconds) + idx = (start_seconds <= prev_unix).sum() - 1 + start_hour = self.timetable.iloc[idx].start_hour + start_minute = self.timetable.iloc[idx].start_minute + prow = self.plan.copy()[(self.plan.inter_no==inter_no) & (self.plan.start_hour==start_hour) & (self.plan.start_minute==start_minute)] # planned row + # prow에서 필요한 부분을 rhist에 추가 + prow['start_unix'] = prev_unix + prow = prow.drop(['start_hour', 'start_minute', 'offset'], axis=1) + cycle = prow.iloc[0].cycle + self.rhist = pd.concat([self.rhist, prow]) + self.rhist = self.rhist.sort_values(by='start_unix').reset_index(drop=True) + n += 1 + + # 2-1-2. 이상치 처리 : 비율에 따라 해당 행을 "삭제"(R_n <= 0.5) 또는 "조정"(R_n > 0.5)한다 + R_n = (curr_unix - prev_unix) / cycle # R_n : 비율 + # R_n이 0.5보다 작거나 같으면 해당 행을 삭제 + if R_n <= 0.5: + self.rhist = self.rhist.drop(index=n).reset_index(drop=True) + if n >= self.rhist.index[-1]: + break + # 행삭제에 따른 curr_unix, R_n 재정의 + curr_unix = self.rhist.loc[n, 'start_unix'] + R_n = (curr_unix - prev_unix) / cycle # R_n : 비율 + + # R_n이 0.5보다 크면 해당 행 조정 (비율을 유지한 채로 현시시간 대체) + if R_n > 0.5: + # 신호 계획(prow) 불러오기 + start_seconds = np.array(self.timetable.start_seconds) + idx = (start_seconds <= curr_unix).sum() - 1 + start_hour = self.timetable.iloc[idx].start_hour + start_minute = self.timetable.iloc[idx].start_minute + prow = self.plan[(self.plan.inter_no==inter_no) & (self.plan.start_hour==start_hour) & (self.plan.start_minute==start_minute)] # planned row + # 조정된 현시시간 (prow에 R_n을 곱하고 정수로 바꿈) + adjusted_dur = prow.copy()[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] * R_n + int_parts = adjusted_dur.iloc[0].apply(lambda x: int(x)) + frac_parts = adjusted_dur.iloc[0] - int_parts + difference = round(adjusted_dur.iloc[0].sum()) - int_parts.sum() + for _ in range(difference): # 소수 부분이 가장 큰 상위 'difference'개의 값에 대해 올림 처리 + max_frac_index = frac_parts.idxmax() + int_parts[max_frac_index] += 1 + frac_parts[max_frac_index] = 0 # 이미 처리된 항목은 0으로 설정 + # rhist에 조정된 현시시간을 반영 + self.rhist.loc[n, [f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] = int_parts.values + self.rhist.loc[n, 'cycle'] = int_parts.sum().sum() // 2 + + if n >= self.rhist.index[-1]: + break + prev_unix = curr_unix + curr_unix = self.rhist.loc[n+1, 'start_unix'] + + self.rhists.append(self.rhist) + self.rhists = pd.concat(self.rhists).sort_values(by=['start_unix','inter_no']) + self.rhists = self.rhists[self.rhists.start_unix >= self.present_time - 3600] + self.rhists = self.rhists.drop(columns=['D_n', 'S_n']) + + def calculate_DS(self, rhist, curr_unix): + program_starts = np.array(self.timetable.start_seconds) + idx = (program_starts <= self.present_time).sum() - 1 + program_start = program_starts[idx] + if list(self.hours[self.hours <= curr_unix]): + ghour_lt_curr_unix = self.hours[self.hours <= curr_unix].max() # the greatest hour less than or equal to curr_unix + else: + ghour_lt_curr_unix = program_start + start_unixes = rhist.start_unix.unique() + start_unixes_lt_ghour = np.sort(start_unixes[start_unixes < ghour_lt_curr_unix]) # start unixes less than ghour_lt_curr_unix + # 기준유닉스(base_unix) : curr_unix보다 작은 hour 중에서 가장 큰 값으로부터 다섯 번째로 작은 start_unix + if len(start_unixes_lt_ghour) > 5: + base_unix = start_unixes_lt_ghour[-5] + # start_unixes_lt_ghour의 길이가 5 미만일 경우에는 맨 앞 start_unix로 base_unix를 지정 + else: + base_unix = rhist.start_unix.min() + D_n = curr_unix - base_unix + S_n_durs = rhist[(rhist.start_unix > base_unix) & (rhist.start_unix <= curr_unix)] \ + [[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] + S_n = S_n_durs.values.sum() // 2 + return D_n, S_n + + # 2-2. hrhists + def make_hrhists(self): + # 계층화된 형태로 변환 + self.hrhists = [] # hierarchied recent history + for i, row in self.rhists.iterrows(): + inter_no = row.inter_no + start_unix = row.start_unix + + ind = (self.timetable['start_seconds'] <= row.start_unix).sum() - 1 + start_hour = self.timetable.iloc[ind].start_hour + start_minute = self.timetable.iloc[ind].start_minute + self.isplit = self.isplits[(inter_no, start_hour, start_minute)] + phas_As = [self.isplit[j][0] for j in self.isplit.keys()] + phas_Bs = [self.isplit[j][1] for j in self.isplit.keys()] + durs_A = row[[f'dura_A{j}' for j in range(1,9)]] + durs_B = row[[f'dura_B{j}' for j in range(1,9)]] + durations = [] + for j in range(1, len(self.isplit)+1): + ja = self.isplit[j][0] + jb = self.isplit[j][1] + if ja == jb: + durations.append(min(durs_A[ja-1], durs_B[jb-1])) + else: + durations.append(abs(durs_A[ja-1] - durs_B[ja-1])) + new_rows = pd.DataFrame({'inter_no':[inter_no] * len(durations), 'start_unix':[start_unix] * len(durations), + 'phas_A':phas_As, 'phas_B':phas_Bs, 'duration':durations}) + self.hrhists.append(new_rows) + self.hrhists = pd.concat(self.hrhists) + self.hrhists = self.hrhists.sort_values(by = ['start_unix', 'inter_no', 'phas_A', 'phas_B']).reset_index(drop=True) + + # 3. 이동류정보 전처리 + def process_movement(self): + print("3. 이동류정보 테이블을 변환합니다.") + self.make_movement() + self.update_movement() + + # 3-1. movement + def make_movement(self): + # - 아래 절차를 5초마다 반복 + for fsec in range(self.present_time - 300, self.present_time + 1, 5): # fsec : unix time by Five SECond + # 1. 상태 테이블 조회해서 전체 데이터중 필요데이터(교차로번호, A링 현시번호, A링 이동류번호, B링 현시번호, B링 이동류번호)만 수집 : A + # move = time2move[fsec] + move = pd.read_csv(os.path.join(self.path_tables, 'move', f'move_{fsec}.csv'), index_col=0) + # 2. 이력 테이블 조회해서 교차로별로 유닉스시간 최대인 데이터(교차로변호, 종료유닉스타임)만 수집 : B + recent_histories = [group.iloc[-1:] for _, group in self.history[self.history['end_unix'] < fsec].groupby('inter_no')] # 교차로별로 유닉스시간이 최대인 행들 + if not recent_histories: + rhistory = pd.DataFrame({'inter_no':[], 'end_unix':[]}) # recent history + else: + rhistory = pd.concat(recent_histories) + recent_unix = rhistory[['inter_no', 'end_unix']] + # 3. 상태 테이블 조회정보(A)와 이력 테이블 조회정보(B) 조인(키값 : 교차로번호) : C + move = pd.merge(move, recent_unix, how='left', on='inter_no') + move['end_unix'] = move['end_unix'].fillna(0).astype(int) + move = move.drop_duplicates() + # 4. C데이터 프레임에 신규 컬럼(시작 유닉스타임) 생성 후 종료유닉스 타임 값 입력, 종료 유닉스 타임 컬럼 제거 + move = move.rename(columns = {'end_unix':'start_unix'}) + # 5. 이동류 이력정보 READ + # - CSV 파일로 서버에 저장된 이동류정보를 읽어옴(파일이 없는 경우에는 데이터가 없는 프레임 D 생성) + try: + if isinstance(movement, pd.DataFrame): # movement가 존재할 경우 그걸 그대로 씀. + pass + else: + movement = pd.DataFrame() + except NameError: # movement가 존재하지 않는 경우 생성 + movement = pd.DataFrame() + # 6. 이동류 이력정보 데이터테이블(D)에 C데이터 add + movement = pd.concat([movement, move]) + # 7. D데이터 프레임에서 중복데이터 제거(교차로번호, 시작 유닉스타임, A링 현시번호, B링 현시번호 같은 행은 제거) + movement = movement.drop_duplicates(['inter_no','phas_A','phas_B','start_unix']) + # 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) + movement = movement[movement.start_unix > fsec - 3600] + movement = movement.sort_values(by=['start_unix','inter_no','phas_A','phas_B']).reset_index(drop=True) + self.movement = pd.read_csv(os.path.join(self.path_intermediates, 'movement', f'movement_{self.present_time}.csv'), index_col=0) + + # 3-2. movement_updated + def update_movement(self): + # 중복을 제거하고 (inter_no, start_unix) 쌍을 만듭니다. + hrhists_inter_unix = set(self.hrhists[['inter_no', 'start_unix']].drop_duplicates().itertuples(index=False, name=None)) + movement_inter_unix = set(self.movement[['inter_no', 'start_unix']].drop_duplicates().itertuples(index=False, name=None)) + + # hrhists에는 있지만 movement에는 없는 (inter_no, start_unix) 쌍을 찾습니다. + missing_in_movement = hrhists_inter_unix - movement_inter_unix + + # 새로운 행들을 생성합니다. + new_rows = [] + if missing_in_movement: + for inter_no, start_unix in missing_in_movement: + # match1에서 해당 inter_no의 데이터를 찾습니다. + new_row = self.match1[self.match1['inter_no'] == inter_no].copy() + # start_unix 값을 설정합니다. + new_row['start_unix'] = start_unix + new_rows.append(new_row) + + # 새로운 데이터프레임을 생성하고 기존 movement 데이터프레임과 합칩니다. + new_movement = pd.concat(new_rows, ignore_index=True) + self.movement_updated = pd.concat([self.movement, new_movement], ignore_index=True) + else: + self.movement_updated = self.movement + + # 4. 통합테이블 생성 + def make_histids(self): + print("4. 통합 테이블을 생성합니다.") + self.merge_dfs() + self.time41 = datetime.now() + self.attach_children() + self.time42 = datetime.now() + + # 4-1. histid + def merge_dfs(self): + # movements and durations + movedur = pd.merge(self.hrhists, self.movement_updated, how='inner', on=['inter_no', 'start_unix', 'phas_A', 'phas_B']) + movedur = movedur.sort_values(by=['start_unix', 'inter_no', 'phas_A','phas_B']) + movedur = movedur[['inter_no', 'start_unix', 'phas_A', 'phas_B', 'move_A', 'move_B', 'duration']] + + # matching DataFrame에 대해 multi-index 설정 + self.matching.set_index(['inter_no', 'move_no'], inplace=True) + self.matching.sort_index(inplace=True) + + for row in movedur.itertuples(index=True): + inter_no = row.inter_no + start_unix = row.start_unix + move_A = row.move_A + move_B = row.move_B + + # incoming and outgoing edges A + if move_A in [17, 18]: + inc_edge_A = np.nan + out_edge_A = np.nan + else: + match_A = self.matching.loc[(inter_no, move_A)] + inc_edge_A = match_A.inc_edge.values[0] + out_edge_A = match_A.out_edge.values[0] + movedur.at[row.Index, 'inc_edge_A'] = inc_edge_A + movedur.at[row.Index, 'out_edge_A'] = out_edge_A + + # incoming and outgoing edges B + if move_B in [17, 18]: + inc_edge_B = np.nan + out_edge_B = np.nan + else: + match_B = self.matching.loc[(inter_no, move_B)] + inc_edge_B = match_B.inc_edge.values[0] + out_edge_B = match_B.out_edge.values[0] + movedur.at[row.Index, 'inc_edge_B'] = inc_edge_B + movedur.at[row.Index, 'out_edge_B'] = out_edge_B + + # 이동류 컬럼 제거 + movedur = movedur.drop(['move_A', 'move_B'], axis=1) + + self.histid = movedur.copy() # history with edge ids (incoming and outgoing edge ids) + self.histid['node_id'] = self.histid['inter_no'].map(self.inter2node) + self.histid = self.histid[['inter_no', 'node_id', 'start_unix', 'phas_A', 'phas_B', 'duration', 'inc_edge_A', 'out_edge_A', 'inc_edge_B', 'out_edge_B']] + histid_start = self.present_time - 600 + self.histid = self.histid[self.histid.start_unix > histid_start] + + # 4-2. histids + def attach_children(self): + ''' + 자식교차로에 대한 진입·진출 엣지 정보를 붙여주는 함수 + + input : + (1) histid + - 각 교차로에 대한 (시작유닉스, A현시, B현시)별 현시시간, 진입·진출엣지 + - 부모교차로(주교차로)에 대해서만 값이 지정되어 있음 + (2) match6 + - (현시, 링)별 진입·진출엣지 + - 자식교차로(유턴 및 연동교차로)에 대해서도 값이 지정되어 있음 + (3) parent_ids : 부모교차로 목록 + (4) pa2ch : 각 부모교차로id를 부모교차로가 포함하고 있는 자식교차로들의 id들의 리스트로 대응시키는 딕셔너리 + + output : histids + - 모든(부모 및 자식) 교차로에 대한 시작유닉스 (시작유닉스, A현시, B현시)별 현시시간, 진입·진출엣지 + ''' + new_histids = [] + for parent_id in self.parent_ids: + for child_id in self.pa2ch[parent_id]: + new_histid = self.histid.copy()[self.histid.node_id==parent_id] + new_histid[['inc_edge_A', 'out_edge_A', 'inc_edge_B', 'out_edge_B']] = np.nan + for row in new_histid.itertuples(index=True): + phas_A = row.phas_A + phas_B = row.phas_B + new_match = self.match6[self.match6.node_id==child_id] + Arow = new_match[(new_match.phase_no==phas_A) & (new_match.ring_type=='A')] + if not Arow[['inc_edge', 'out_edge']].isna().all().all(): + inc_edge = Arow.iloc[0].inc_edge + out_edge = Arow.iloc[0].out_edge + new_histid.loc[row.Index, ['inc_edge_A', 'out_edge_A']] = [inc_edge, out_edge] + Brow = new_match[(new_match.phase_no==phas_B) & (new_match.ring_type=='B')] + if not Brow[['inc_edge', 'out_edge']].isna().all().all(): + inc_edge = Brow.iloc[0].inc_edge + out_edge = Brow.iloc[0].out_edge + new_histid.loc[row.Index, ['inc_edge_B', 'out_edge_B']] = [inc_edge, out_edge] + new_histid.loc[row.Index, 'node_id'] = child_id + new_histids.append(new_histid) + new_histids = pd.concat(new_histids) + self.histids = pd.concat([self.histid.copy(), new_histids]) + self.histids = self.histids.sort_values(by=['start_unix', 'node_id', 'phas_A', 'phas_B']).reset_index(drop=True) + + # 5. 신호 생성 + def get_signals(self): + print("5. 신호를 생성합니다.") + self.initialize_states() + self.assign_signals() + self.set_timepoints() + self.assign_red_yellow() + self.make_tl_file() + + # 5-1. 신호초기화 + def initialize_states(self): + ''' + 신호 초기화 + + input : + (1) net : 네트워크 + (2) nodes : 노드 목록 + (3) histids : 모든 교차로에 대한 시작유닉스 (시작유닉스, A현시, B현시)별 현시시간, 진입·진출엣지 + + output : node2init + - 각 노드를 초기화된 신호로 맵핑하는 딕셔너리 + - 초기화된 신호란, 우회전을 g로 나머지는 r로 지정한 신호를 말함. + ''' + self.node2init = {} + for node in self.nodes: + node_id = node.getID() + conns = [(c.getJunctionIndex(), c) for c in node.getConnections()] + conns = [c for c in conns if c[0] >= 0] + conns = sorted(conns, key=lambda x: x[0]) + state = [] + for i, ci in conns: + if ci.getTLLinkIndex() < 0: + continue + are_foes = False + for j, cj in conns: + if ci.getTo() == cj.getTo(): + continue + if node.areFoes(i, j): + are_foes = True + break + state.append('r' if are_foes else 'g') + self.node2init[node_id] = state + + # 어떤 연결과도 상충이 일어나지는 않지만, 신호가 부여되어 있는 경우에는 r을 부여 + for _, row in self.histids.iterrows(): + node_id = row['node_id'] + inc_edge_A = row.inc_edge_A + inc_edge_B = row.inc_edge_B + out_edge_A = row.out_edge_A + out_edge_B = row.out_edge_B + + if pd.isna(inc_edge_A) or pd.isna(out_edge_A): + pass + else: + inc_edge_A = self.net.getEdge(inc_edge_A) + out_edge_A = self.net.getEdge(out_edge_A) + for conn in inc_edge_A.getConnections(out_edge_A): + index = conn.getTLLinkIndex() + if index >= 0: + self.node2init[node_id][index] = 'r' + + if pd.isna(inc_edge_B) or pd.isna(out_edge_B): + pass + else: + inc_edge_B = self.net.getEdge(inc_edge_B) + out_edge_B = self.net.getEdge(out_edge_B) + for conn in inc_edge_B.getConnections(out_edge_B): + index = conn.getTLLinkIndex() + if index >= 0: + self.node2init[node_id][index] = 'r' + + # 5-2. 녹색신호 부여 + def assign_signals(self): + ''' + 진입·진출엣지를 신호문자열로 배정 + + input : + (1) histids : 모든 교차로에 대한 (시작유닉스, A현시, B현시)별 현시시간, 진입·진출엣지 + (2) node2init : 각 노드를 초기화된 신호로 맵핑하는 딕셔너리 + (3) net : 네트워크 + + output : sigtable + - 모든 교차로에 대한 (시작유닉스, A현시, B현시)별 현시시간, 신호문자열 + - 황색 및 적색신호는 아직 반영되지 않았음. + ''' + self.sigtable = self.histids.copy() + self.sigtable['init_state'] = self.sigtable['node_id'].map(self.node2init) + self.sigtable['state'] = self.sigtable['init_state'].map(lambda x:''.join(x)) + for i, row in self.sigtable.iterrows(): + node_id = row.node_id + inc_edge_A = row.inc_edge_A + inc_edge_B = row.inc_edge_B + out_edge_A = row.out_edge_A + out_edge_B = row.out_edge_B + state = copy.deepcopy(self.node2init)[node_id] + if pd.isna(inc_edge_A) or pd.isna(out_edge_A): + pass + else: + inc_edge_A = self.net.getEdge(inc_edge_A) + out_edge_A = self.net.getEdge(out_edge_A) + for conn in inc_edge_A.getConnections(out_edge_A): + index = conn.getTLLinkIndex() + if index >= 0: + state[index] = 'G' + self.sigtable.at[i, 'state'] = ''.join(state) + + if pd.isna(inc_edge_B) or pd.isna(out_edge_B): + pass + else: + inc_edge_B = self.net.getEdge(inc_edge_B) + out_edge_B = self.net.getEdge(out_edge_B) + for conn in inc_edge_B.getConnections(out_edge_B): + index = conn.getTLLinkIndex() + if index >= 0: + state[index] = 'G' + self.sigtable.at[i, 'state'] = ''.join(state) + self.sigtable = self.sigtable.dropna(subset='state') + self.sigtable = self.sigtable.reset_index(drop=True) + self.sigtable['phase_sumo'] = self.sigtable.groupby(['node_id', 'start_unix']).cumcount() + self.sigtable = self.sigtable[['node_id', 'start_unix', 'phase_sumo', 'duration', 'state']] + self.sigtable = self.sigtable.sort_values(by=['start_unix', 'node_id']) + self.sigtable['start_dt'] = self.sigtable['start_unix'].apply(lambda x:datetime.fromtimestamp(x)) + + # 5-3. 신호 파일의 시작 및 종료시각 설정 + def set_timepoints(self): + self.offsets = {} + self.Sigtable = [] + sim_start = self.present_time - 300 + for node_id, group in self.sigtable.groupby('node_id'): + lsbs = group[group['start_unix'] < sim_start]['start_unix'].max() # the last start_unix before sim_start + self.offsets[node_id] = lsbs - sim_start + group = group[group.start_unix >= lsbs] + start_unixes = np.array(group.start_unix) + start_unixes = np.sort(np.unique(start_unixes))[:self.node2num_cycles[node_id]] + group = group[group.start_unix.isin(start_unixes)] + self.Sigtable.append(group) + self.Sigtable = pd.concat(self.Sigtable) + + # 5-4. 적색 및 황색신호 부여 + def assign_red_yellow(self): + ''' + 적색, 황색신호를 반영한 신호문자열 배정 + + input : Sigtable + - 모든 교차로에 대한 (시작유닉스, 세부현시번호)별 현시시간, 신호문자열, 진입·진출엣지 + * 세부현시란 오버랩을 반영한 현시번호를 뜻함. + + output : SIGTABLE + - 모든 교차로에 대한 (시작유닉스, 녹황적세부현시번호)별 현시시간, (황·적색신호가 포함된) 신호문자열 + * 녹황적세부현시번호란 세부현시번호에 r, g, y 옵션까지 포함된 현시번호를 뜻함. + ''' + self.SIGTABLE = [] + for node_id, group in self.Sigtable.groupby('node_id'): + new_rows_list = [] + for i in range(1, len(group)): + prev_row = group.iloc[i-1:i].copy() + next_row = group.iloc[i:i+1].copy() + new_rows = pd.concat([prev_row, prev_row, next_row]).reset_index(drop=True) + new_rows.loc[0, 'phase_sumo'] = str(prev_row.phase_sumo.iloc[0]) + '_g' + new_rows.loc[0, 'duration'] = new_rows.loc[0, 'duration'] - 5 + new_rows.loc[1, 'phase_sumo'] = str(prev_row.phase_sumo.iloc[0]) + '_y' + new_rows.loc[1, 'duration'] = 4 + yellow_state = '' + red_state = '' + for a, b in zip(prev_row.state.iloc[0], next_row.state.iloc[0]): + if a == 'G' and b == 'r': + yellow_state += 'y' + red_state += 'r' + else: + yellow_state += a + red_state += a + new_rows.loc[2, 'phase_sumo'] = str(next_row.phase_sumo.iloc[0]) + '__r' + new_rows.loc[2, 'duration'] = 1 + new_rows.loc[1, 'state'] = yellow_state + new_rows.loc[2, 'state'] = red_state + new_rows_list.append(new_rows) + next_row['phase_sumo'] = str(next_row.phase_sumo.iloc[0]) + '_g' + next_row['duration'] -= 5 + # next_row.loc['duration'] -= 5 + new_rows_list.append(next_row) + new_rows = pd.concat(new_rows_list) + self.SIGTABLE.append(new_rows) + self.SIGTABLE = pd.concat(self.SIGTABLE).sort_values(by=['node_id', 'start_unix', 'phase_sumo']).reset_index(drop=True) + + # 5-5. 신호파일 생성 + def make_tl_file(self): + strings = ['\n'] + for node_id, group in self.SIGTABLE.groupby('node_id'): + strings.append(f' \n') + for i, row in group.iterrows(): + duration = row.duration + state = row.state + strings.append(f' \n') + strings.append(' \n') + strings.append('') + strings = ''.join(strings) + # 저장 + self.path_output = os.path.join(self.path_results, f'sn_{self.present_time}.add.xml') + with open(self.path_output, 'w') as f: + f.write(strings) + + # 6. 이슈사항 저장 + def write_issues(self): + print('6. 이슈사항을 저장합니다.') + path_issues = os.path.join(self.path_results, "issues_generate_signals.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): + self.time0 = datetime.now() + # 1. 데이터 준비 + self.prepare_data() + self.time1 = datetime.now() + # 2. 신호이력 전처리 + self.process_history() + self.time2 = datetime.now() + # 3. 이동류정보 전처리 + self.process_movement() + self.time3 = datetime.now() + # 4. 통합테이블 생성 + self.make_histids() + self.time4 = datetime.now() + # 5. 신호 생성 + self.get_signals() + self.time5 = datetime.now() + # 6. 이슈사항 저장 + self.write_issues() + self.time6 = datetime.now() + + print('(1)', self.time1 - self.time0) + print('(2-1)', self.time21 - self.time1) + print('(2-2)', self.time22 - self.time21) + print('(2-3)', self.time23 - self.time22) + print('(2)', self.time2 - self.time1) + print('(3)', self.time3 - self.time2) + print('(4)', self.time4 - self.time3) + print('(4-1)', self.time41 - self.time3) + print('(4-2)', self.time42 - self.time41) + print('(5)', self.time5 - self.time4) + print('(6)', self.time6 - self.time5) + print('total time :', self.time6 - self.time0) + +if __name__ == '__main__': + self = SignalGenerator() + self.main() + self.path_unit = os.path.join(self.path_root, 'Analysis', '0207_unit_test') + self.hrhists.to_csv(os.path.join(self.path_unit, 'hrhists.csv')) + self.histids.to_csv(os.path.join(self.path_unit, 'histids.csv')) + self.sigtable.to_csv(os.path.join(self.path_unit, 'sigtable.csv')) + self.Sigtable.to_csv(os.path.join(self.path_unit, 'ssigtable.csv')) + # print("elapsed time :", datetime.now() - starting_time) \ No newline at end of file diff --git a/Archives/Scripts/preprocess_daily.ipynb b/Archives/Scripts/preprocess_daily.ipynb index 4b86b04d1..6d55265e6 100644 --- a/Archives/Scripts/preprocess_daily.ipynb +++ b/Archives/Scripts/preprocess_daily.ipynb @@ -211,7 +211,7 @@ } ], "source": [ - "inter_node" + "inter_node[]" ] }, { diff --git a/Data/tables/20240207_140433/S_INT_CONFIG.csv b/Data/tables/20240207_140433/S_INT_CONFIG.csv new file mode 100644 index 000000000..9b6b26fc0 --- /dev/null +++ b/Data/tables/20240207_140433/S_INT_CONFIG.csv @@ -0,0 +1,759 @@ +INT_NO,INT_NAME,SA_NO,INT_LAT,INT_LNG,IS_SMART +1,도촌4거리,1,37.41405,127.14385,1 +2,동원SK삼거리(아시아 주유소),1,37.41282,127.14382, +3,상탑4거리,0,37.40809,127.14296, +4,돌마4거리(상탑3거리),0,37.40683,127.14201,1 +5,상탑초3거리,0,37.40561,127.14473, +6,야탑3치안센터(상탑 P.B),0,37.40549,127.14823, +7,테크노 단일로,0,37.40486,127.15121, +8,분당테크노파크A동,0,37.40499,127.15034, +9,분당테크노파크C동(분당테크노파크),0,37.40545,127.15526, +10,야탑차량등록사업소,0,37.40682,127.1547, +11,야탑보듬이,0,37.40636,127.1522, +12,대원빌라4거리,0,37.40631,127.14852, +13,양촌어린이공원,0,37.40677,127.14667, +14,상탑초4거리,0,37.40711,127.14559, +15,중탑3거리,0,37.41473,127.13905, +16,중탑4거리,0,37.41307,127.13866, +17,야탑3동주민센터(야탑3동사무소),0,37.41064,127.13813, +18,목련4거리,0,37.40952,127.13801, +19,목련3거리,0,37.40777,127.13789, +20,목련마을단일로,0,37.40918,127.13983, +21,성남세관3거리,0,37.40858,127.14167, +22,매화마을주공310동(매화마을주공3단지),0,37.41447,127.14114, +23,매화마을4단지,0,37.41288,127.14243, +24,여수동입구4거리,0,37.41683,127.13394, +25,매화4거리,0,37.4134,127.1339, +26,분당성마르코성당(야탑성당),0,37.41085,127.13388, +27,송림4거리(구송림고4거리),0,37.40944,127.13386, +28,하탑3거리(하탑도서관),0,37.40737,127.13385, +29,도레미유치원,0,37.40971,127.13598, +30,매화마을3거리,0,37.41333,127.13637, +31,야탑소방서,0,37.41635,127.13569, +32,야탑4거리,0,37.41688,127.12879,1 +33,장미4거리,0,37.41384,127.12876, +34,야탑역 단일로,0,37.41214,127.12875, +35,야탑역4거리,0,37.40986,127.12873,1 +36,하탑4거리(하탑지하차도),0,37.40543,127.12868,1 +37,야탑중학교,0,37.41694,127.13121, +38,야탑시그마Ⅲ 3거리,0,37.41383,127.13048, +39,장미마을단일로,0,37.41376,127.1318, +40,돌마초교,0,37.40978,127.13081, +41,야탑 현대APT,0,37.40951,127.13261, +42,807의무경찰대(807전경대),0,37.40631,127.13138, +43,터미널3거리,0,37.41384,127.12608, +44,터미널4거리,0,37.41237,127.12605, +45,분당차여성병원4거리(경찰서4거리),0,37.41137,127.12604, +46,차병원3거리,0,37.40983,127.12603, +47,야탑지구대 사거리,0,37.40988,127.12711, +48,장미동부APT,0,37.41682,127.12715, +49,장미코오롱아파트(야탑초교),0,37.41678,127.12456, +50,사송교4거리,0,37.41385,127.12258, +51,대우정비사업소,0,37.41503,127.11949, +52,재활용센터,0,37.41426,127.12008, +53,사송교3거리,0,37.41309,127.1208, +54,탄천운동장 동문(운동장서문),0,37.41162,127.12176, +55,보건소3거리,0,37.41137,127.12402, +56,탄천운동장 남문,0,37.40872,127.12109, +57,탄천운동장4거리(탑4거리),0,37.40918,127.12302, +58,야탑교4거리,0,37.40957,127.12479, +59,늘사랑어린이집(탑마을 단일로),0,37.40711,127.1236, +60,하탑초교,0,37.40731,127.12543,0 +61,벌말4거리(보뜰4거리),0,37.40546,127.11914,1 +62,탑대우APT입구(장미대우아파트입구),0,37.40547,127.12066, +63,아름마을태영APT입구(탑대우APT),0,37.40546,127.12156, +64,아름4거리,0,37.40544,127.1236, +65,탑골4거리,0,37.40544,127.12545, +66,아름건영아파트,0,37.40369,127.12359, +67,꾸러기유치원,0,37.40369,127.12545, +68,매송초교,0,37.40111,127.12358, +69,이매중3거리,0,37.40098,127.12541, +70,이매풍림5단지,0,37.39784,127.12353, +71,방아다리4거리,0,37.39921,127.12358, +72,물방아4거리,0,37.39919,127.12539, +73,이매소방서,0,37.39918,127.1275,0 +74,물방아3거리,0,37.39919,127.1287,1 +75,성남아트센터(문화예술회관),0,37.39925,127.12925, +76,매송중학교,0,37.3969,127.12356, +77,이매초교,0,37.39678,127.12539, +78,이매교4거리,0,37.39473,127.12351, +79,안말4거리,0,37.39472,127.12537, +80,이매성지아파트,0,37.39472,127.12678,0 +81,이매역4거리(안말3거리),0,37.39471,127.12828,1 +82,안말초 단일로,0,37.39309,127.1251, +83,안말초교3거리,0,37.39233,127.12447, +84,이매코오롱APT,0,37.39146,127.12543, +85,이매동신아파트,0,37.39074,127.1262, +86,이매촌청구,0,37.39073,127.12676, +87,누엣길공원,0,37.39008,127.12533,0 +88,매송4거리,0,37.39143,127.12022,1 +89,LG분당에클라트(양현4거리),0,37.39011,127.12244, +90,분당제생병원3거리,0,37.38822,127.12076, +91,마사회3거리,0,37.38731,127.11993, +92,양현4거리(이매촌한신4R),0,37.38896,127.12377, +93,분당제생병원4거리,0,37.38731,127.12172, +94,롯데마트4거리,0,37.38659,127.12072, +95,한신아파트3거리(서현산업은행),0,37.38782,127.12507, +96,오일뱅크4거리,0,37.38736,127.12439, +97,분당우체국,0,37.38686,127.12371, +98,우리은행4거리,0,37.38629,127.12289, +99,하나은행4거리,0,37.38555,127.12186, +100,나우병원4거리,0,37.38446,127.12038, +101,서현농협3거리,0,37.38355,127.12139,0 +102,서현교4거리,0,37.38677,127.11658, +103,분당소방서3거리(서현소방서3거리),0,37.38539,127.11812, +104,분당구청4거리,0,37.38399,127.11973, +105,분당구청 단일로,0,37.38309,127.12072, +106,서현4거리,0,37.38222,127.12171, +107,이매4거리,0,37.38621,127.12685,1 +108,수내4거리,0,37.38402,127.11402,1 +109,수내교4거리(조선내화4거리),0,37.38174,127.11658,1 +110,경동빌딩(분당구청 단일로),0,37.38083,127.11758,0 +111,분당4거리,0,37.37998,127.11857,1 +112,서현먹자골목 입구,0,37.38386,127.13056, +113,서현도서관3거리(먹자골SK주유소),0,37.38372,127.13337, +114,남해수산3거리,0,37.38334,127.13591, +115,새마을4거리,0,37.38313,127.13834, +116,서현포스파크,0,37.38315,127.13968, +117,은수사3거리,0,37.38327,127.1433, +118,새마을연수원사거리(수도병원입구),0,37.38376,127.15048, +119,안골입구4거리,0,37.38285,127.1322, +120,서현시범단지우성APT,0,37.38178,127.13072, +121,올림픽스포츠센터,0,37.38038,127.12873, +122,서현시범단지한양APT,0,37.37947,127.12753, +123,서현3거리,0,37.3784,127.12595, +124,서현중학교 단일로,0,37.38128,127.13396, +125,분당어린이공원,0,37.37856,127.13123,0 +126,서당4거리,0,37.37942,127.13604,1 +127,돌마지하차도,0,37.37744,127.13323,0 +128,효자촌4거리,0,37.37539,127.1304,1 +129,당촌초교,0,37.37361,127.13238,0 +130,서당3거리(서당동입구),0,37.37629,127.13959,1 +131,동남프라자,0,37.37399,127.1364, +132,서당삼환APT,0,37.3731,127.13517,0 +133,장안4거리,0,37.37223,127.13395,1 +134,장안동3거리,0,37.37156,127.13301, +135,장안동입구4거리,0,37.37455,127.14238, +136,장안교3거리(장안동 중앙유치원),0,37.37287,127.14158, +137,장안동건영113동,0,37.37214,127.14073, +138,대진고 후문,0,37.37014,127.13793, +139,대진고4거리,0,37.3695,127.13703, +140,장안초등학교,0,37.37166,127.14392,0 +141,장안중3거리,0,37.37213,127.1472,1 +142,장안타운건영107동,0,37.37096,127.14467, +143,장안타운123동,0,37.37039,127.14388, +144,태현공원4거리,0,37.36941,127.14249, +145,장안로삼거리(신동아빌라),0,37.36777,127.14023, +146,장안건영빌라 단일로,0,37.371,127.14761,0 +147,요한성당3거리,0,37.36886,127.14863,1 +148,지역난방공사,0,37.36759,127.14583, +149,지역난방 삼거리,0,37.36794,127.14639, +150,새롬교회(광림교회),0,37.36639,127.14208, +151,현대오피스빌딩(APUS 3거리),0,37.38017,127.11438, +152,휴맥스빌리지(롯데백화점),0,37.37856,127.11217,0 +153,초림3거리,0,37.37539,127.12181,1 +154,수내동 우리은행,0,37.37404,127.11996, +155,롯데파크타운,0,37.37351,127.1192, +156,내정4거리,0,37.37165,127.11659, +157,내정어린이공원,0,37.37217,127.12208,0 +158,샛별4거리,0,37.37198,127.12565,1 +159,수내지하차도,0,37.37019,127.12313,0 +160,푸른마을4거리,0,37.3685,127.12083,1 +161,정자한일아파트,0,37.36778,127.12169, +162,수내중 단일로,0,37.37046,127.12731, +163,샛별3거리,0,37.36914,127.12879, +164,한방병원 단일로,0,37.36781,127.12787, +165,한방병원4거리,0,37.3671,127.12727, +166,수내파출소(수내 P.B),0,37.36637,127.12627, +167,푸른마을삼거리(연정교회3거리),0,37.36515,127.12458, +168,한솔청구아파트,0,37.36438,127.12367, +169,불정3거리,0,37.36347,127.12316, +170,정든마을우성616동(우성116동),0,37.36235,127.12311, +171,정자동아2차,0,37.36101,127.1231, +172,분당중앙고 삼거리,0,37.36013,127.12301, +173,분당중앙고,0,37.35944,127.12231, +174,정자동아아파트,0,37.35923,127.12034, +175,한솔삼거리(한솔마을3거리),0,37.36857,127.11478, +176,느티마을4단지,0,37.36724,127.1148, +177,한솔6단지(정자동사무소),0,37.36619,127.1148, +178,신기4거리,0,37.36346,127.1148, +179,한솔마을5단지(한솔마을단일로),0,37.36348,127.11725, +180,정든마을4거리,0,37.36348,127.11906, +181,불정4거리,0,37.35921,127.11908,1 +182,탄천초교,0,37.3617,127.11482, +183,계원삼거리(한국통신4거리),0,37.35995,127.11483, +184,잡월드 사거리,0,37.3784,127.10897,1 +185,백현교4거리,0,37.37719,127.11034, +186,삼성로얄팰리스(판테온정문),0,37.37633,127.11132, +187,정자4거리,0,37.37545,127.11227,1 +188,백궁교(판테온후문),0,37.37525,127.10981, +189,백궁4거리,0,37.37368,127.10981, +190,백현환기소3거리,0,37.37245,127.10422,1 +191,늘푸른고등학교 교차로(백궁주유소4),0,37.37244,127.10593, +192,백궁3거리,0,37.37243,127.10828,1 +193,상록마을아이파크분당1(백궁현대 ),0,37.37095,127.10592, +194,아이파크분당3단지,0,37.36939,127.10593, +195,마테오성당(백현중학교),0,37.37181,127.10983, +196,백현중학교 삼거리,0,37.37047,127.10982, +197,백현초등학교,0,37.37193,127.11223, +198,백현어린이공원,0,37.37132,127.11183, +199,젤존타워Ⅰ(백궁우체국4거리),0,37.36853,127.10593, +200,궁내4거리,0,37.36852,127.10808,1 +201,느티마을4거리,0,37.36854,127.10984,0 +202,느티마을 안촌유치원,0,37.36855,127.11241, +203,아름방송4거리,0,37.36666,127.10594, +204,정자역 단일로,0,37.36614,127.1081, +205,분당경찰서4거리(벤체타운4거리),0,37.36563,127.10595, +206,상록마을 321동,0,37.36483,127.10981, +207,정자지하차도3거리(정자환기소3거리),0,37.36347,127.10461,1 +208,정자1동주민센터(연강병원4거리),0,37.36345,127.10596,0 +209,금곡교4거리,0,37.36344,127.10811,1 +210,상록4거리(상록마을입구4거리),0,37.36347,127.10982,0 +211,상록마을단일로,0,37.36346,127.11237, +212,상록마을302동 단일로,0,37.36347,127.11306, +213,정자대림아크로텔 단일로,0,37.36232,127.10709, +214,금곡주유소3거리,0,37.3599,127.10424, +215,정자일로1사거리(청솔공업사 4거리),0,37.35991,127.10599, +216,불정교4거리,0,37.3599,127.10812,1 +217,불정교3거리(주택전시관4거리),0,37.35994,127.10986,0 +218,순복음교회앞,0,37.35995,127.11176, +219,늘푸른초등학교,0,37.35788,127.106, +220,정자일로2사거리(보성APT 4거리),0,37.35664,127.10601,0 +221,임광보성아파트(금곡보성아파트),0,37.35665,127.1072, +222,금곡4거리,0,37.35666,127.10849, +223,분당경영고(분당 정보산업고),0,37.35662,127.11017, +224,청솔주공5단지,0,37.35565,127.11268, +225,청솔초교,0,37.35451,127.11472, +226,한국가스공사,0,37.35468,127.1184, +227,돌마교4거리,0,37.3532,127.11711, +228,우방우성102동,0,37.35253,127.11838, +229,파랑새공원단일로,0,37.35495,127.106,0 +230,청솔유천화인아파트 정문(한전기공),0,37.35433,127.106,0 +231,정자일로3사거리(YMCA4거리),0,37.35342,127.10601, +232,청솔마을4거리,0,37.35344,127.10888, +233,청솔종합사회복지관(청솔마을 단일로),0,37.35333,127.11091, +234,이솔유치원(청솔마을 8-9단지),0,37.3527,127.11227, +235,코오롱 트리폴리스,0,37.35138,127.10627, +236,금곡교차로(금곡I/C 3거리),0,37.35007,127.10432,1 +237,머내공원4거리(아울렛4거리),0,37.35002,127.10638, +238,미금역4거리,0,37.35004,127.1089,1 +239,까치마을신원아파트308동(골드프라자),0,37.35003,127.11202, +240,까치마을4거리,0,37.35102,127.11523, +241,까치마을 단일로,0,37.34928,127.11608, +242,꿈터유치원,0,37.34757,127.11202, +243,미금4거리,0,37.34584,127.10891, +244,하얀마을6단지,0,37.34602,127.11345, +245,하얀마을4거리,0,37.34652,127.11608, +246,안디옥감리교회,0,37.34651,127.11836, +247,하얀마을3거리,0,37.34648,127.12008, +248,오리초교 단일로,0,37.34472,127.1161, +249,동막교삼거리,0,37.34359,127.10891, +250,시그마3거리,0,37.34265,127.10451, +251,시그마Ⅱ 4거리,0,37.34265,127.10591, +252,동막교4거리,0,37.34262,127.10891,1 +253,LH오리사옥 정문3거리,0,37.34249,127.11001,0 +254,구미교4거리,0,37.34219,127.11609, +255,구미그랜드빌라,0,37.34352,127.11909, +256,성남우편집중국3거리,0,37.34073,127.10895, +257,LH오리사옥 후문(주공후문3거리),0,37.34072,127.10963, +258,LH스포츠센터(주택문화관),0,37.34072,127.11029, +259,오리공원3거리(구미동 YMCA),0,37.34074,127.11169, +260,까르프3거리,0,37.33968,127.10448, +261,NH주유소4거리(하나로후문4거리),0,37.3397,127.10594, +262,오리홈플러스4거리(까르프4거리),0,37.33969,127.10739, +263,오리역3거리,0,37.33971,127.10895, +264,벽산3거리,0,37.33842,127.10472, +265,하나로마트 정문,0,37.33733,127.10673, +266,농수산물센터4거리(벽산사거리),0,37.33599,127.10895,1 +267,온세통신3거리,0,37.33589,127.11174, +268,무지개 LG아파트,0,37.33644,127.11366, +269,오리3거리,0,37.3381,127.10895,1 +270,성우스타우스(오리먹자골입구),0,37.33809,127.11029, +271,오리1교 4거리(오리교 4거리),0,37.33811,127.11175, +272,오리2교 4거리(오리교 4거리),0,37.33811,127.11311, +273,미금교3거리,0,37.35053,127.12005, +274,서울대병원 단일로,0,37.35063,127.12208, +275,서울대병원 입구,0,37.35019,127.12331, +276,장애인고용공단,0,37.3484,127.12391, +277,서울시니어스타워(서울대병원 주차장),0,37.34732,127.12413, +278,구미중학교(구미중 단일로),0,37.34501,127.12462,0 +279,구미4거리,0,37.34343,127.12601, +280,구미제일아파트,0,37.34272,127.12476,0 +281,구미라이프APT,0,37.34156,127.12347, +282,구미동사무소,0,37.33974,127.12167, +283,구미파출소3거리(구미 P.B),0,37.33909,127.12073, +284,구미초교3거리,0,37.33817,127.11905, +285,구미초정문,0,37.33753,127.11905, +286,구미초교 단일로,0,37.33811,127.1176, +287,구미공원4거리,0,37.34041,127.11612, +288,구미주공4단지,0,37.33923,127.11615, +289,무지개4거리,0,37.33813,127.11614,1 +290,구미대림APT,0,37.33659,127.11673, +291,구미동대림APT4거리,0,37.33576,127.11771, +292,무지개12단지(주공 두산아파트),0,37.33496,127.11816, +293,미금로3거리,0,37.33406,127.11809, +294,대림아파트 후문,0,37.33455,127.11598, +295,불곡초교 단일로,0,37.34037,127.11866,0 +296,금곡I/C분당진입,0,37.35421,127.10072,0 +297,운중교차로 사거리,0,37.39116,127.06308, +298,상상운 삼거리,0,37.39144,127.06527, +299,운중초교 사거리,0,37.39144,127.06801, +300,중산운 사거리,0,37.39173,127.07583, +301,운중동 주민센타,0,37.3916,127.07834, +302,산운 사거리,0,37.39138,127.08157, +303,하산운 사거리,0,37.39056,127.08547,1 +304,판교 도서관 사거리,0,37.38949,127.09017, +305,도서관 삼거리,0,37.38873,127.0939, +306,원마을 8단지 사거리(점멸),0,37.38686,127.09744, +307,광암사거리,0,37.38826,127.09753, +308,낙생농협 4거리,0,37.38963,127.09763, +309,원마을 9단지 사거리,0,37.39073,127.09772, +310,꿈꾸는교회 삼거리,0,37.39273,127.09827, +311,원마을 10단지 3거리,0,37.3934,127.09824, +312,낙생초 삼거리,0,37.39472,127.09817, +313,불꽃교회 삼거리,0,37.39637,127.098, +314,낙원중 삼거리,0,37.39846,127.09672, +315,원마을 11단지 삼거리,0,37.39917,127.0953, +316,성내미 사거리,0,37.3999,127.09474,1 +317,원마을 12단지 사거리(점멸),0,37.4006,127.09417, +318,원마을 13단지 삼거리(소등),0,37.40271,127.0925, +319,영운정사 입구 삼거리,0,37.3989,127.09277, +320,판교로 2사거리,0,37.38881,127.08485, +321,하산운 삼거리,0,37.38703,127.08421, +322,산운길3 삼거리,0,37.39319,127.07594, +323,산운마을 10단지 삼거리,0,37.39073,127.07574, +324,판교원2 사거리,0,37.3893,127.07557, +325,두밀 사거리(국은교),0,37.38793,127.07504, +326,산운마을 13단지 1,0,37.3869,127.07461, +327,산운마을 13단지 2,0,37.38568,127.07424, +328,산운마을 14단지,0,37.3842,127.07427, +329,연구원 사거리(점멸),0,37.39221,127.06352, +330,월든힐스 2단지(점멸),0,37.3928,127.06516, +331,월든힐스 3단지(점멸),0,37.39263,127.06711, +332,운중초교 정문 삼거리,0,37.39221,127.0681, +333,산운마을 4단지 단일로(점멸),0,37.39074,127.06788, +334,운중초교 후문 삼거리,0,37.39299,127.06917, +335,서판교ic 사거리,0,37.38837,127.07002,1 +336,원마을 3단지 북문(점멸),0,37.3886,127.08579, +337,원마을 3단지 서문(점멸),0,37.38769,127.08689, +338,판교중 사거리(점멸),0,37.38672,127.08653, +339,판교 도서관앞(점멸),0,37.39142,127.09085, +340,도서관 주택가 사거리(점멸),0,37.39056,127.09054, +341,판교어린이집 3거리(점멸),0,37.38635,127.08803, +342,원마을 5단지 사거리,0,37.38776,127.08956, +343,판교초교 삼거리,0,37.38636,127.08909, +344,원마을 7단지 단일로(점멸),0,37.3863,127.09315, +345,낙원중 입구(점멸),0,37.39764,127.0954,0 +346,산운초 단일로,0,37.389,127.07848, +347,신미주 1단지 4거리,0,37.40675,127.11545, +348,삼평동사무소,0,37.40583,127.11546, +349,송현초 단일로,0,37.40525,127.11554, +350,송현초 4거리(사송사거리),0,37.40432,127.11617, +351,산운교회 4거리,0,37.40266,127.11685, +352,봇들마을 6단지,0,37.40137,127.11686, +353,삼평중 삼거리,0,37.39985,127.11683,1 +354,구숯내교삼거리,0,37.39834,127.11682, +355,보평초 4거리,0,37.39622,127.11682, +806,대원 사거리,30,37.43346,127.1597, +808,청소년수련관 사거리,30,37.43054,127.15405, +809,아튼빌정문 사거리,30,37.42996,127.1517, +810,아튼빌후문 삼거리,30,37.42935,127.14925, +356,보평초정문 단일로,0,37.39589,127.11843, +357,보평중.고 사거리,0,37.39333,127.11682, +358,동판교로사거리(세계로사거리),0,37.39189,127.11682, +359,백현마을 3.4단지 입구,0,37.39057,127.11681, +360,화랑초교 단일로,0,37.38885,127.11634, +361,재넘어 삼거리,0,37.38819,127.11538, +362,백현동 주택단지 사거리,0,37.38795,127.11442, +363,봇들 사거리,0,37.40323,127.11291,1 +364,삼평 사거리(소방서),0,37.39985,127.11296, +365,봇들교 사거리,0,37.39832,127.11296, +366,판교역 사거리,0,37.39622,127.11294,1 +367,광장로사거리(백현마을 1단지),0,37.39376,127.11294, +368,느티나무사거리(광장로사거리),0,37.39174,127.11294,1 +369,백현 사거리(신백현초),0,37.38794,127.11293, +370,판교테크노13사거리(점멸),0,37.40397,127.10968, +371,판교테크노14사거리(테크윈),0,37.40323,127.10969, +372,SK캐미컬 사거리,0,37.40243,127.10967, +373,안철수연구소 사거리,0,37.40094,127.10967, +374,판교테크노3사거리(안철수),0,37.39984,127.10967,1 +375,동안교 삼거리,0,37.39775,127.10968, +376,동안 사거리,0,37.39623,127.10968, +377,환승주차장 삼거리,0,37.39375,127.10968, +378,낙생지하차도 삼거리,0,37.38968,127.1089, +379,낙생대공원 삼거리,0,37.38795,127.10843, +380,휴먼시아8단지 삼거리,0,37.38733,127.10842, +381,흰돌교회 단일로 1,0,37.38693,127.10842, +382,흰돌교회 단일로 2(점멸),0,37.38612,127.10841, +383,휴먼시아9단지 사거리,0,37.38525,127.1084, +384,백현야구장 삼거리(점멸),0,37.38375,127.11074, +385,동안삼거리(한신교회),0,37.39833,127.11405, +386,롯데마트사거리(봇들마을8단지),0,37.39621,127.11404, +387,안철수연구소 삼거리(점멸),0,37.40095,127.11176, +388,판교역 1번출구 단일로,0,37.39622,127.11127, +389,판교역 3번출구 단일로,0,37.39373,127.11118, +390,백현동사무소,0,37.38793,127.11111, +391,신백현초 정문 단일로(점멸),0,37.38735,127.11132, +392,만도연구소(점멸),0,37.40517,127.10033, +393,포스코삼거리,0,37.40403,127.10221, +394,포스코단일로,0,37.40341,127.10253, +395,넥슨코리아(점멸),0,37.40237,127.10255, +396,NS홈쇼핑(점멸),0,37.40093,127.10255, +397,생태학습관 삼거리(점멸),0,37.39986,127.10372, +398,중앙일보(점멸),0,37.39985,127.10256, +399,우림사거리(점멸),0,37.40373,127.09902, +400,모다아울렛(점멸),0,37.40171,127.10018, +401,마이다스(점멸),0,37.40086,127.10079, +402,송암시스템(점멸),0,37.39984,127.10123, +403,판교테크노중앙사거리,0,37.40322,127.10495,1 +404,판교테크노2사거리,0,37.39984,127.10647, +405,화랑공원 삼거리,0,37.39625,127.10693, +406,판교I/C 사거리,0,37.39424,127.10562, +407,낙생고 삼거리,0,37.3884,127.10158,1 +408,백현고가 4거리,0,37.3816,127.10228,1 +409,성남외고 삼거리,0,37.37823,127.10208, +410,은혜와진리교회 삼거리,0,37.37498,127.10176, +411,궁안동 입구 3거리,0,37.37053,127.10174, +412,삼다가 삼거리,0,37.36956,127.1017, +413,장수촌 단일로,0,37.36663,127.10159, +414,궁내T/G 사거리,0,37.36373,127.1016, +415,보바스기념병원 사거리,0,37.35754,127.10175, +416,로얄냉장 사거리,0,37.35345,127.1017, +417,동원동 삼거리,0,37.34472,127.1015, +418,금곡IC단일로,0,37.34221,127.10257, +419,금곡IC분당진입,0,37.35007,127.10298, +420,정신문화연구원,0,37.39141,127.05731, +421,송유관공사 삼거리,0,37.38044,127.04807, +422,이우학교 삼거리,0,37.35203,127.08585, +423,동막I/C 밑,0,37.35334,127.08143, +424,대장I/C 출구,0,37.36175,127.0678, +425,대장I/C 입구,0,37.35944,127.06821, +426,엔씨소프트단일,0,37.39984,127.10846, +427,스타트업캠퍼스 삼거리(점멸),0,37.40396,127.10648, +428,수질복원센터 삼거리,0,37.39789,127.10157, +429,판교주민센터 뒤(점멸),0,37.38962,127.09975, +430,낙생초교 후문(점멸),0,37.39473,127.10027, +431,낙생초교 정문(점멸),0,37.39473,127.09916, +432,성내미마을 삼거리(점멸),0,37.40161,127.09336, +433,지역난방공사 뒤(점멸),0,37.39504,127.10195, +434,운중교 삼거리(점멸),0,37.39018,127.06781, +435,서판교로 66번길,0,37.39272,127.09952, +436,이배재 사거리,0,37.43166,127.1791,1 +437,반포산업 사거리,0,37.43113,127.1742, +438,코멕스 삼거리,0,37.43153,127.17216, +439,공단본부 사거리,0,37.43218,127.16762, +440,대원지구대 삼거리,0,37.43265,127.16595,1 +441,한일레미콘 단일로,0,37.4329,127.16404, +442,대원 사거리,0,37.43342,127.15993,1 +443,자이아파트 삼거리,0,37.43238,127.15772, +444,청소년수련관 사거리,0,37.43056,127.15404,1 +445,아튼빌정문 사거리,0,37.42997,127.15166, +446,아튼빌후문 삼거리,0,37.42935,127.14916, +447,방송고 단일로,0,37.42887,127.14718, +448,검단초교 사거리,0,37.42829,127.14474, +449,하대원파출소 사거리,0,37.42826,127.14182,1 +450,성남동현대아파트 삼거리,0,37.4291,127.14003, +451,중원구청 사거리,0,37.42972,127.13715,1 +452,신광교회 단일로,0,37.42913,127.13533, +453,월드카센타 앞,0,37.42892,127.13326, +454,천주교성당 앞,0,37.4291,127.13151, +455,금광중 후문,0,37.44749,127.17104, +456,하원초교 삼거리,0,37.44719,127.16961, +457,신구대 사거리,0,37.44671,127.16731, +458,금광2동사무소 단일로,0,37.44611,127.1645, +459,금광1동사무소 사거리,0,37.44527,127.16184, +460,단대쇼핑뒤 단일로,0,37.44462,127.16, +461,해오름 사거리,0,37.44397,127.15791, +462,중앙동사무소 삼거리,0,37.44307,127.15574, +463,중앙동 사거리,0,37.44178,127.15285, +464,성남공판장 단일로,0,37.44064,127.15033, +465,제일초교 단일로,0,37.43958,127.14788, +466,성호시장 사거리,0,37.43845,127.14535, +467,성일여중고 사거리,0,37.43666,127.14201, +468,성일여고 삼거리,0,37.43602,127.14089, +469,성호지구대 단일로,0,37.4354,127.13976, +470,운동장후문 삼거리,0,37.43462,127.13811, +471,운동장 사거리,0,37.434,127.13504,0 +472,삼영전자 앞,0,37.44397,127.15491, +473,세이브존 단일로,0,37.4432,127.15333, +474,우리은행 사거리,0,37.44263,127.15218,1 +475,성남우체국,0,37.4418,127.1501, +476,신흥 사거리,0,37.43962,127.14455, +477,수진역 사거리,0,37.43762,127.14097,1 +478,모란 사거리,0,37.43358,127.12892,1 +479,둔전교회 앞,0,37.43342,127.12747, +480,고속화도로 출구,0,37.43248,127.11988, +481,탄천 삼거리,0,37.43242,127.11894, +482,동양공업사 사거리,0,37.4339,127.17895, +483,보통골 동성교통앞,0,37.43533,127.17884, +484,근로복지관 삼거리,0,37.43794,127.17861,1 +485,궁전아파트 삼거리,0,37.44013,127.17895, +486,궁전아파트 사거리,0,37.44134,127.17911, +487,파리크라상 사거리,0,37.43504,127.17031, +488,구공단파출소 오거리,0,37.43776,127.17374,1 +489,순흥물산 삼거리,0,37.43983,127.17611, +490,자동차검사소 삼거리,0,37.44189,127.17765, +491,중원경찰서 사거리,0,37.44232,127.16868, +492,금광1파출소 사거리,0,37.44514,127.16826, +493,신구대서문 삼거리,0,37.44873,127.16661, +494,금광중 단일로,0,37.44616,127.17079, +495,금광 삼거리,0,37.44793,127.17307, +496,래미안 삼거리,0,37.4502,127.17436, +497,금상초교 단일로,0,37.44253,127.16612, +498,금광주유소 단일로,0,37.4418,127.16417, +499,학년쇼핑 단일로,0,37.44036,127.1625, +500,단남아파트 단일로,0,37.44215,127.15962, +501,해오름주차장 사거리,0,37.44117,127.15994, +502,상대원고개 단일로,0,37.4398,127.15937, +503,상대원시장 삼거리,0,37.43639,127.15814, +504,상대원고개 농협단일로,0,37.43542,127.15812, +505,대원터널 앞,0,37.43257,127.16034, +506,도촌동입구 삼거리,0,37.41725,127.14993, +507,도촌동입구 사거리,0,37.41547,127.14781, +508,신지교회 단일로,0,37.43703,127.1463, +509,성일고 삼거리,0,37.43563,127.14727, +510,성남서고 삼거리,0,37.43471,127.1479, +511,하대원현대아파트 삼거리,0,37.43243,127.1507, +512,대하초교 단일로,0,37.43112,127.1512, +513,은행동순환로밑 삼거리,0,37.45313,127.17252, +514,중부초교 사거리,0,37.45086,127.16588, +515,은행동현대아파트 삼거리,0,37.45029,127.16312, +516,중앙병원 단일로,0,37.45362,127.16194, +517,해성주유소 사거리,0,37.42827,127.13787, +518,여수지하차도위 사거리,0,37.42154,127.12883,1 +519,모란시장 사거리,0,37.42981,127.1294,1 +520,모란 삼거리,0,37.43281,127.12903, +521,모란견인소 삼거리,0,37.43013,127.12016, +522,탄천로변 삼거리,0,37.43038,127.11889, +523,공단주유소 오거리,0,37.43461,127.17396, +524,우림라이온스밸리 사거리,0,37.43354,127.17112, +525,에이스경로당 사거리,0,37.44128,127.17143, +526,에스콰이어교회 앞,0,37.43963,127.17243, +527,중원보건소 삼거리,0,37.44173,127.17027, +528,갈현 삼거리,0,37.42247,127.17061, +529,중앙초교 앞,0,37.43529,127.14318, +530,여울목 삼거리,0,37.42021,127.15724, +531,도촌동 1단지앞,0,37.41485,127.14834, +532,도촌남로 사거리,0,37.41422,127.15018, +533,도촌프라자 단일로,0,37.41443,127.15194, +534,도촌중입구 사거리,0,37.41448,127.15508, +535,도촌초교 단일로,0,37.414,127.15724,0 +536,도촌초교 삼거리,0,37.41361,127.15836, +537,도촌초교 정문,0,37.41417,127.15877, +538,도촌 수산횟집 앞,0,37.41575,127.15064, +539,도촌북로 사거리,0,37.41672,127.15094, +540,도촌우체국 삼거리,0,37.41709,127.15306, +541,도촌파출소 사거리,0,37.41737,127.15531, +542,도촌종합복지관삼거리,0,37.41518,127.15948, +543,901동앞 삼거리,0,37.41253,127.1634, +544,영생사업소 입구,0,37.4198,127.18303, +545,추모원 주차장입구,0,37.42085,127.1827, +546,장례식장 주차장입구,0,37.42182,127.18233, +547,마지로 신호등 1,0,37.42831,127.13139, +548,마지로 신호등 2,0,37.42817,127.13242, +549,마지로 신호등 3,0,37.4282,127.1337, +550,마지로 신호등 4,0,37.42832,127.13591, +551,보육시설 삼거리,0,37.44251,127.15655, +552,힐스테이 삼거리,0,37.43575,127.14989, +553,시청광장 삼거리,0,37.41879,127.12715, +554,시청정문 사거리,0,37.41879,127.12569, +555,시청공원 삼거리,0,37.41759,127.12456, +556,센트럴타운 사거리,0,37.41878,127.13063, +557,여수초교 삼거리,0,37.41877,127.13241, +558,여수공원 삼거리,0,37.41877,127.13394, +559,연꽃마을4단지 사거리,0,37.41866,127.13547, +560,옥토교회 사거리,0,37.4179,127.13735, +561,공영주차장 단일로,0,37.45737,127.16553, +562,은행2동주민센타 삼거리,0,37.45675,127.16663, +563,이원빌라 사거리,0,37.45613,127.16775, +564,두산아파트 사거리,0,37.45455,127.16387, +565,상원초교 입구,0,37.45477,127.16258, +566,모란 LH 1단지 정문,0,37.43075,127.12607, +567,한신병원앞,0,37.4332,127.131,0 +568,모란시장 삼거리,0,37.42996,127.12798, +569,모란시장 단일로,0,37.42983,127.12709, +570,모란주차장사거리,0,37.42969,127.12613, +571,모란공원 단일로,0,37.42959,127.12452, +572,도촌중 단일로,0,37.41672,127.15526, +573,청진동 수산횟집앞,0,37.43441,127.15874, +574,지식산업센타 사거리,0,37.44031,127.17424, +575,금광지구 8번게이트,0,37.44289,127.16712, +576,오복슈퍼앞,0,37.45014,127.16613, +577,상대원초교 정문,0,37.44278,127.18114, +578,도촌스위첸앞,0,37.41454,127.1539, +579,도촌9단지 정문,0,37.41194,127.16294, +580,한양수자인,0,37.44769,127.17192, +581,스타타워 사거리,0,37.43436,127.17666, +582,슈페리어 단일로,0,37.43333,127.17897, +583,상대원 터널,0,37.42791,127.17951, +584,상대원 터널 우회전전용,0,37.42811,127.17991, +585,호반아파트 후문,0,37.42895,127.09436, +586,호반아파트 정문,0,37.42999,127.09621, +587,고등마을,0,37.43036,127.09896, +588,고등지구대,0,37.43022,127.10068, +589,청구아파트,0,37.44327,127.15089, +590,두산아파트,0,37.44398,127.14961,0 +591,한신아파트 후문,0,37.445,127.15082, +592,희망대사거리,0,37.44482,127.14799,0 +593,희망대초교,0,37.44662,127.14941, +594,구)신흥시장길,0,37.44559,127.14657, +595,태평제일교회,0,37.44781,127.1439, +596,신흥성당,0,37.44871,127.14306, +597,신흥초교,0,37.44954,127.14355, +598,성남여중,0,37.44937,127.14247, +599,가일빌딩(나나헤어),0,37.44446,127.1287, +600,태평초교,0,37.44535,127.13168, +601,태평3동 행정복지센터,0,37.4456,127.13256, +602,통보2차 아파트,0,37.44664,127.13612, +603,영락교회,0,37.44699,127.13723, +604,금빛초교 정문,0,37.4475,127.13897, +605,금빛초교 후문,0,37.44798,127.14062, +606,논골도서관,0,37.45625,127.16019, +607,양지아파트,0,37.45698,127.15943, +608,단대제1공영주차장,0,37.45872,127.15853, +609,문원중학교,0,37.46111,127.15798, +610,성보경영고,0,37.4624,127.15797, +611,푸르지오 입구,0,37.45104,127.15885, +612,푸르지오 정문,0,37.45134,127.15761, +613,진로아파트,0,37.45159,127.15681, +614,단대초교,0,37.45295,127.15661, +615,단대지구대,0,37.45252,127.15812, +616,구)목넴기,0,37.40916,127.09803, +617,남산마을,0,37.40883,127.09725, +618,구)금토슈퍼,0,37.40868,127.09621, +619,창조벨리,0,37.40863,127.09557, +620,판교임시IC,0,37.40856,127.09491, +621,금토1통,0,37.41722,127.08102, +622,금토동 삼거리,0,37.41092,127.09947, +623,창조벨리 삼거리,0,37.41221,127.09973, +624,세종연구소 삼거리,0,37.41631,127.1016, +625,시흥사거리,0,37.42232,127.10205, +626,고등소방서,0,37.42354,127.10215, +627,구) 고등주민센터,0,37.42446,127.1022, +628,사송동 차고지,0,37.41539,127.11476, +629,수자원공사입구,0,37.41451,127.11349, +630,사송교차로,0,37.41172,127.11403, +631,단대오거리,0,37.44507,127.15681,1 +632,구)단대동주민센터,0,37.44816,127.15835, +633,금광시장,0,37.45086,127.15952, +634,법원,0,37.452,127.16004, +635,중앙병원,0,37.45413,127.16098,1 +636,상원초교,0,37.4552,127.1618, +637,은행지구대,0,37.45707,127.16339, +638,은행시장,0,37.45804,127.16423, +639,을지대 정문,0,37.45986,127.16577, +640,을지대 후문,0,37.46148,127.16751, +641,양지지구대,0,37.46266,127.16916, +642,남한산성 유원지,0,37.46355,127.17029, +643,모란고개,0,37.43654,127.12838, +644,태평역,0,37.43974,127.12775,1 +645,수정경찰서,0,37.44181,127.12739, +646,태평고개,0,37.44395,127.12701, +647,구)성남병원,0,37.44794,127.12673,1 +648,가천대,0,37.45057,127.12662, +649,복정초소,0,37.45352,127.12652, +650,복정지구대,0,37.45628,127.12629, +651,동서울대,0,37.45976,127.12585,1 +652,복정초교,0,37.46185,127.12944, +653,국제학교,0,37.46545,127.12629,1 +654,삼정그린뷰아파트,0,37.43999,127.12145, +655,삼부아파트 후문,0,37.43952,127.12436, +656,삼부아파트,0,37.4395,127.12589, +657,곽생로,0,37.44049,127.13028, +658,중앙시장,0,37.44093,127.13181, +659,한국관,0,37.44144,127.13346, +660,탄리사거리,0,37.44221,127.13613, +661,구)시청,0,37.44309,127.13906, +662,의료원 장례식장,0,37.44385,127.13869, +663,의료원 정문,0,37.44407,127.1385, +664,외환은행,0,37.44356,127.14065, +665,숯골사거리,0,37.44409,127.14248,1 +666,성남초교(단),0,37.44494,127.14405, +667,성남초교(4),0,37.44636,127.14541,1 +668,구)청직이약국,0,37.44811,127.14531, +669,신흥2동 주민센터,0,37.4491,127.14584, +670,수정구청,0,37.44962,127.14624, +671,포레스티아 서문,0,37.45063,127.14702, +672,포레스티아 중문,0,37.45227,127.14934,1 +673,포레스티아 동문 삼거리,0,37.45406,127.14971, +674,산성역,0,37.45642,127.15006,1 +675,폴리텍,0,37.45869,127.15196, +676,변전소,0,37.46225,127.15521, +677,청산골,0,37.4409,127.14395, +678,구)시청고개,0,37.44208,127.14342, +679,왕남초교 주차장 삼거리,0,37.42777,127.09895, +680,왕남초교 정문,0,37.42786,127.09827, +681,여수대교 사거리,0,37.42205,127.11668,1 +682,시흥동주민센터 삼거리,0,37.42241,127.10856, +683,농협물류창고 삼거리,0,37.42238,127.10527, +684,수성교,0,37.43512,127.13449, +685,수진소방서,0,37.43649,127.13384, +686,Yes 할인마트,0,37.43769,127.1333, +687,공주보일러,0,37.43896,127.1327, +688,태평로약국,0,37.44238,127.13113, +689,서광모터스,0,37.44308,127.1308, +690,하나님의교회,0,37.44411,127.13033, +691,한전사거리,0,37.44483,127.12997, +692,태평중학교,0,37.4462,127.12936, +693,태평3동 공영주차장,0,37.44682,127.12906, +694,LH 기업성장센터 사거리,0,37.41081,127.09487, +695,디어스빌딩 앞 삼거리,0,37.41256,127.09887, +696,경기 기업성장센터 삼거리,0,37.41209,127.09561, +697,판교 기업성장센터 사거리,0,37.41134,127.09495, +698,풍경채 입구,0,37.42564,127.10041, +699,토끼굴,0,37.4261,127.09714, +700,아이비빌딩,0,37.42713,127.09501, +701,정혜사,0,37.42803,127.09346, +702,고등IC,0,37.42956,127.09079, +703,적푸리마을,0,37.43361,127.08478, +704,신구대식물원,0,37.43214,127.08167, +705,청계산주유소,0,37.43071,127.07334, +706,청계산장,0,37.43052,127.07174, +707,탄천교차로,0,37.41867,127.11755, +708,선사교회,0,37.42024,127.11703, +709,둔전교,0,37.42736,127.11654, +710,비닐하우스,0,37.4302,127.1202, +711,탄천주차장 정문,0,37.4422,127.11968, +712,탄천주차장 후문(탄천한우),0,37.44346,127.11971, +713,탄천대교 사거리,0,37.45775,127.11952, +714,경원진지,0,37.45186,127.12105, +715,능골공원,0,37.4491,127.12044, +716,수질복원센터 후문,0,37.4589,127.12104, +717,수질복원센터 정문,0,37.46024,127.12446, +718,수진동행정복지센터,0,37.43869,127.13921, +719,성체유치원,0,37.43916,127.13776, +720,은서유치원,0,37.44357,127.1355, +721,순온천사우나,0,37.44439,127.13511, +722,태평오거리,0,37.4461,127.1343, +723,쌍용아파트,0,37.44797,127.12775, +724,동부센트레빌,0,37.44804,127.12852, +725,성남서초,0,37.44834,127.12956, +726,두산위브,0,37.44966,127.13401, +727,위례부영아파트,0,37.46057,127.14543, +728,위례소방서,0,37.46161,127.14359, +729,미도아파트,0,37.44647,127.15542, +730,세무서,0,37.44686,127.15503, +731,청소년수련원,0,37.44948,127.15321, +732,혜은학교,0,37.44995,127.15261, +733,현대자동차공업사,0,37.45039,127.152, +734,북초교,0,37.45086,127.15132, +735,양지근린공원 임시 단일로,0,37.4629,127.16197,0 +736,풍경채 정문 사거리,0,37.42739,127.1007, +737,왕남초교 삼거리,0,37.42816,127.10089, +738,5171부대 삼거리,0,37.42055,127.10217, +739,구)고등동 삼거리,0,37.42564,127.10233, +740,구)고등사거리,0,37.42807,127.10193, +741,대왕주유소 삼거리,0,37.42995,127.10205, +742,등자리마을 삼거리,0,37.43498,127.10356, +743,한성대 삼거리,0,37.43258,127.10311, +744,제2정문 삼거리,0,37.43848,127.10371, +745,우정아파트 삼거리,0,37.44464,127.10373, +746,공군아파트 삼거리,0,37.44611,127.10401, +747,서울공항 정문 사거리,0,37.44851,127.10497, +748,효성고 단일로,0,37.44952,127.10542, +749,오야실마을 삼거리,0,37.45491,127.10686, +750,미군부대 삼거리,0,37.45789,127.10736, +751,신촌동 삼거리,0,37.45994,127.10763, +752,성남서중,0,37.44775,127.1542, +753,램프구간 하부,0,37.45344,127.17281, +754,창곡사거리,0,37.46316,127.14072,1 diff --git a/Data/tables/20240207_140433/S_INT_PHASE_CONFIG.csv b/Data/tables/20240207_140433/S_INT_PHASE_CONFIG.csv new file mode 100644 index 000000000..a79927e5d --- /dev/null +++ b/Data/tables/20240207_140433/S_INT_PHASE_CONFIG.csv @@ -0,0 +1,3909 @@ +INT_NO,INT_PHASE_NO,INT_PLAN_CLSS,INT_RING,INT_FLOW_NO,INT_MIN_SPLIT,INT_MAX_SPLIT,INT_YELLOW,INT_MAIN_PHASE,INT_LANE,INT_PHASE_LAT1,INT_PHASE_LNG1,INT_PHASE_LAT2,INT_PHASE_LNG2,INT_PHASE_LAT3,INT_PHASE_LNG3 +204,1,0,0,8,0,0,0,0,0,37.36581,127.10822,37.36614,127.10822,37.36626,127.10822 +204,1,0,1,4,0,0,0,0,0,37.36646,127.10797,37.36614,127.10797,37.36602,127.10797 +204,2,0,0,17,0,0,0,0,0,19.69448,117.9926,19.69448,117.9926,19.69448,117.9926 +205,1,0,0,7,0,0,0,0,0,37.36595,127.10607,37.36563,127.10608,37.36563,127.10622 +205,1,0,1,4,0,0,0,0,0,37.36595,127.10582,37.36563,127.10583,37.36551,127.10583 +205,2,0,1,3,0,0,0,0,0,37.3653,127.10583,37.36563,127.10583,37.36563,127.10568 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+60,하탑초교,0,37.40731,127.12543,0 +61,벌말4거리(보뜰4거리),0,37.40546,127.11914,1 +62,탑대우APT입구(장미대우아파트입구),0,37.40547,127.12066, +63,아름마을태영APT입구(탑대우APT),0,37.40546,127.12156, +64,아름4거리,0,37.40544,127.1236, +65,탑골4거리,0,37.40544,127.12545, +66,아름건영아파트,0,37.40369,127.12359, +67,꾸러기유치원,0,37.40369,127.12545, +68,매송초교,0,37.40111,127.12358, +69,이매중3거리,0,37.40098,127.12541, +70,이매풍림5단지,0,37.39784,127.12353, +71,방아다리4거리,0,37.39921,127.12358, +72,물방아4거리,0,37.39919,127.12539, +73,이매소방서,0,37.39918,127.1275,0 +74,물방아3거리,0,37.39919,127.1287,1 +75,성남아트센터(문화예술회관),0,37.39925,127.12925, +76,매송중학교,0,37.3969,127.12356, +77,이매초교,0,37.39678,127.12539, +78,이매교4거리,0,37.39473,127.12351, +79,안말4거리,0,37.39472,127.12537, +80,이매성지아파트,0,37.39472,127.12678,0 +81,이매역4거리(안말3거리),0,37.39471,127.12828,1 +82,안말초 단일로,0,37.39309,127.1251, +83,안말초교3거리,0,37.39233,127.12447, +84,이매코오롱APT,0,37.39146,127.12543, +85,이매동신아파트,0,37.39074,127.1262, +86,이매촌청구,0,37.39073,127.12676, +87,누엣길공원,0,37.39008,127.12533,0 +88,매송4거리,0,37.39143,127.12022,1 +89,LG분당에클라트(양현4거리),0,37.39011,127.12244, +90,분당제생병원3거리,0,37.38822,127.12076, +91,마사회3거리,0,37.38731,127.11993, +92,양현4거리(이매촌한신4R),0,37.38896,127.12377, +93,분당제생병원4거리,0,37.38731,127.12172, +94,롯데마트4거리,0,37.38659,127.12072, +95,한신아파트3거리(서현산업은행),0,37.38782,127.12507, +96,오일뱅크4거리,0,37.38736,127.12439, +97,분당우체국,0,37.38686,127.12371, +98,우리은행4거리,0,37.38629,127.12289, +99,하나은행4거리,0,37.38555,127.12186, +100,나우병원4거리,0,37.38446,127.12038, +101,서현농협3거리,0,37.38355,127.12139,0 +102,서현교4거리,0,37.38677,127.11658, +103,분당소방서3거리(서현소방서3거리),0,37.38539,127.11812, +104,분당구청4거리,0,37.38399,127.11973, +105,분당구청 단일로,0,37.38309,127.12072, +106,서현4거리,0,37.38222,127.12171, +107,이매4거리,0,37.38621,127.12685,1 +108,수내4거리,0,37.38402,127.11402,1 +109,수내교4거리(조선내화4거리),0,37.38174,127.11658,1 +110,경동빌딩(분당구청 단일로),0,37.38083,127.11758,0 +111,분당4거리,0,37.37998,127.11857,1 +112,서현먹자골목 입구,0,37.38386,127.13056, +113,서현도서관3거리(먹자골SK주유소),0,37.38372,127.13337, +114,남해수산3거리,0,37.38334,127.13591, +115,새마을4거리,0,37.38313,127.13834, +116,서현포스파크,0,37.38315,127.13968, +117,은수사3거리,0,37.38327,127.1433, +118,새마을연수원사거리(수도병원입구),0,37.38376,127.15048, +119,안골입구4거리,0,37.38285,127.1322, +120,서현시범단지우성APT,0,37.38178,127.13072, +121,올림픽스포츠센터,0,37.38038,127.12873, +122,서현시범단지한양APT,0,37.37947,127.12753, +123,서현3거리,0,37.3784,127.12595, +124,서현중학교 단일로,0,37.38128,127.13396, +125,분당어린이공원,0,37.37856,127.13123,0 +126,서당4거리,0,37.37942,127.13604,1 +127,돌마지하차도,0,37.37744,127.13323,0 +128,효자촌4거리,0,37.37539,127.1304,1 +129,당촌초교,0,37.37361,127.13238,0 +130,서당3거리(서당동입구),0,37.37629,127.13959,1 +131,동남프라자,0,37.37399,127.1364, +132,서당삼환APT,0,37.3731,127.13517,0 +133,장안4거리,0,37.37223,127.13395,1 +134,장안동3거리,0,37.37156,127.13301, +135,장안동입구4거리,0,37.37455,127.14238, +136,장안교3거리(장안동 중앙유치원),0,37.37287,127.14158, +137,장안동건영113동,0,37.37214,127.14073, +138,대진고 후문,0,37.37014,127.13793, +139,대진고4거리,0,37.3695,127.13703, +140,장안초등학교,0,37.37166,127.14392,0 +141,장안중3거리,0,37.37213,127.1472,1 +142,장안타운건영107동,0,37.37096,127.14467, +143,장안타운123동,0,37.37039,127.14388, +144,태현공원4거리,0,37.36941,127.14249, +145,장안로삼거리(신동아빌라),0,37.36777,127.14023, +146,장안건영빌라 단일로,0,37.371,127.14761,0 +147,요한성당3거리,0,37.36886,127.14863,1 +148,지역난방공사,0,37.36759,127.14583, +149,지역난방 삼거리,0,37.36794,127.14639, +150,새롬교회(광림교회),0,37.36639,127.14208, +151,현대오피스빌딩(APUS 3거리),0,37.38017,127.11438, +152,휴맥스빌리지(롯데백화점),0,37.37856,127.11217,0 +153,초림3거리,0,37.37539,127.12181,1 +154,수내동 우리은행,0,37.37404,127.11996, +155,롯데파크타운,0,37.37351,127.1192, +156,내정4거리,0,37.37165,127.11659, +157,내정어린이공원,0,37.37217,127.12208,0 +158,샛별4거리,0,37.37198,127.12565,1 +159,수내지하차도,0,37.37019,127.12313,0 +160,푸른마을4거리,0,37.3685,127.12083,1 +161,정자한일아파트,0,37.36778,127.12169, +162,수내중 단일로,0,37.37046,127.12731, +163,샛별3거리,0,37.36914,127.12879, +164,한방병원 단일로,0,37.36781,127.12787, +165,한방병원4거리,0,37.3671,127.12727, +166,수내파출소(수내 P.B),0,37.36637,127.12627, +167,푸른마을삼거리(연정교회3거리),0,37.36515,127.12458, +168,한솔청구아파트,0,37.36438,127.12367, +169,불정3거리,0,37.36347,127.12316, +170,정든마을우성616동(우성116동),0,37.36235,127.12311, +171,정자동아2차,0,37.36101,127.1231, +172,분당중앙고 삼거리,0,37.36013,127.12301, +173,분당중앙고,0,37.35944,127.12231, +174,정자동아아파트,0,37.35923,127.12034, +175,한솔삼거리(한솔마을3거리),0,37.36857,127.11478, +176,느티마을4단지,0,37.36724,127.1148, +177,한솔6단지(정자동사무소),0,37.36619,127.1148, +178,신기4거리,0,37.36346,127.1148, +179,한솔마을5단지(한솔마을단일로),0,37.36348,127.11725, +180,정든마을4거리,0,37.36348,127.11906, +181,불정4거리,0,37.35921,127.11908,1 +182,탄천초교,0,37.3617,127.11482, +183,계원삼거리(한국통신4거리),0,37.35995,127.11483, +184,잡월드 사거리,0,37.3784,127.10897,1 +185,백현교4거리,0,37.37719,127.11034, +186,삼성로얄팰리스(판테온정문),0,37.37633,127.11132, +187,정자4거리,0,37.37545,127.11227,1 +188,백궁교(판테온후문),0,37.37525,127.10981, +189,백궁4거리,0,37.37368,127.10981, +190,백현환기소3거리,0,37.37245,127.10422,1 +191,늘푸른고등학교 교차로(백궁주유소4),0,37.37244,127.10593, +192,백궁3거리,0,37.37243,127.10828,1 +193,상록마을아이파크분당1(백궁현대 ),0,37.37095,127.10592, +194,아이파크분당3단지,0,37.36939,127.10593, +195,마테오성당(백현중학교),0,37.37181,127.10983, +196,백현중학교 삼거리,0,37.37047,127.10982, +197,백현초등학교,0,37.37193,127.11223, +198,백현어린이공원,0,37.37132,127.11183, +199,젤존타워Ⅰ(백궁우체국4거리),0,37.36853,127.10593, +200,궁내4거리,0,37.36852,127.10808,1 +201,느티마을4거리,0,37.36854,127.10984,0 +202,느티마을 안촌유치원,0,37.36855,127.11241, +203,아름방송4거리,0,37.36666,127.10594, +204,정자역 단일로,0,37.36614,127.1081, +205,분당경찰서4거리(벤체타운4거리),0,37.36563,127.10595, +206,상록마을 321동,0,37.36483,127.10981, +207,정자지하차도3거리(정자환기소3거리),0,37.36347,127.10461,1 +208,정자1동주민센터(연강병원4거리),0,37.36345,127.10596,0 +209,금곡교4거리,0,37.36344,127.10811,1 +210,상록4거리(상록마을입구4거리),0,37.36347,127.10982,0 +211,상록마을단일로,0,37.36346,127.11237, +212,상록마을302동 단일로,0,37.36347,127.11306, +213,정자대림아크로텔 단일로,0,37.36232,127.10709, +214,금곡주유소3거리,0,37.3599,127.10424, +215,정자일로1사거리(청솔공업사 4거리),0,37.35991,127.10599, +216,불정교4거리,0,37.3599,127.10812,1 +217,불정교3거리(주택전시관4거리),0,37.35994,127.10986,0 +218,순복음교회앞,0,37.35995,127.11176, +219,늘푸른초등학교,0,37.35788,127.106, +220,정자일로2사거리(보성APT 4거리),0,37.35664,127.10601,0 +221,임광보성아파트(금곡보성아파트),0,37.35665,127.1072, +222,금곡4거리,0,37.35666,127.10849, +223,분당경영고(분당 정보산업고),0,37.35662,127.11017, +224,청솔주공5단지,0,37.35565,127.11268, +225,청솔초교,0,37.35451,127.11472, +226,한국가스공사,0,37.35468,127.1184, +227,돌마교4거리,0,37.3532,127.11711, +228,우방우성102동,0,37.35253,127.11838, +229,파랑새공원단일로,0,37.35495,127.106,0 +230,청솔유천화인아파트 정문(한전기공),0,37.35433,127.106,0 +231,정자일로3사거리(YMCA4거리),0,37.35342,127.10601, +232,청솔마을4거리,0,37.35344,127.10888, +233,청솔종합사회복지관(청솔마을 단일로),0,37.35333,127.11091, +234,이솔유치원(청솔마을 8-9단지),0,37.3527,127.11227, +235,코오롱 트리폴리스,0,37.35138,127.10627, +236,금곡교차로(금곡I/C 3거리),0,37.35007,127.10432,1 +237,머내공원4거리(아울렛4거리),0,37.35002,127.10638, +238,미금역4거리,0,37.35004,127.1089,1 +239,까치마을신원아파트308동(골드프라자),0,37.35003,127.11202, +240,까치마을4거리,0,37.35102,127.11523, +241,까치마을 단일로,0,37.34928,127.11608, +242,꿈터유치원,0,37.34757,127.11202, +243,미금4거리,0,37.34584,127.10891, +244,하얀마을6단지,0,37.34602,127.11345, +245,하얀마을4거리,0,37.34652,127.11608, +246,안디옥감리교회,0,37.34651,127.11836, +247,하얀마을3거리,0,37.34648,127.12008, +248,오리초교 단일로,0,37.34472,127.1161, +249,동막교삼거리,0,37.34359,127.10891, +250,시그마3거리,0,37.34265,127.10451, +251,시그마Ⅱ 4거리,0,37.34265,127.10591, +252,동막교4거리,0,37.34262,127.10891,1 +253,LH오리사옥 정문3거리,0,37.34249,127.11001,0 +254,구미교4거리,0,37.34219,127.11609, +255,구미그랜드빌라,0,37.34352,127.11909, +256,성남우편집중국3거리,0,37.34073,127.10895, +257,LH오리사옥 후문(주공후문3거리),0,37.34072,127.10963, +258,LH스포츠센터(주택문화관),0,37.34072,127.11029, +259,오리공원3거리(구미동 YMCA),0,37.34074,127.11169, +260,까르프3거리,0,37.33968,127.10448, +261,NH주유소4거리(하나로후문4거리),0,37.3397,127.10594, +262,오리홈플러스4거리(까르프4거리),0,37.33969,127.10739, +263,오리역3거리,0,37.33971,127.10895, +264,벽산3거리,0,37.33842,127.10472, +265,하나로마트 정문,0,37.33733,127.10673, +266,농수산물센터4거리(벽산사거리),0,37.33599,127.10895,1 +267,온세통신3거리,0,37.33589,127.11174, +268,무지개 LG아파트,0,37.33644,127.11366, +269,오리3거리,0,37.3381,127.10895,1 +270,성우스타우스(오리먹자골입구),0,37.33809,127.11029, +271,오리1교 4거리(오리교 4거리),0,37.33811,127.11175, +272,오리2교 4거리(오리교 4거리),0,37.33811,127.11311, +273,미금교3거리,0,37.35053,127.12005, +274,서울대병원 단일로,0,37.35063,127.12208, +275,서울대병원 입구,0,37.35019,127.12331, +276,장애인고용공단,0,37.3484,127.12391, +277,서울시니어스타워(서울대병원 주차장),0,37.34732,127.12413, +278,구미중학교(구미중 단일로),0,37.34501,127.12462,0 +279,구미4거리,0,37.34343,127.12601, +280,구미제일아파트,0,37.34272,127.12476,0 +281,구미라이프APT,0,37.34156,127.12347, +282,구미동사무소,0,37.33974,127.12167, +283,구미파출소3거리(구미 P.B),0,37.33909,127.12073, +284,구미초교3거리,0,37.33817,127.11905, +285,구미초정문,0,37.33753,127.11905, +286,구미초교 단일로,0,37.33811,127.1176, +287,구미공원4거리,0,37.34041,127.11612, +288,구미주공4단지,0,37.33923,127.11615, +289,무지개4거리,0,37.33813,127.11614,1 +290,구미대림APT,0,37.33659,127.11673, +291,구미동대림APT4거리,0,37.33576,127.11771, +292,무지개12단지(주공 두산아파트),0,37.33496,127.11816, +293,미금로3거리,0,37.33406,127.11809, +294,대림아파트 후문,0,37.33455,127.11598, +295,불곡초교 단일로,0,37.34037,127.11866,0 +296,금곡I/C분당진입,0,37.35421,127.10072,0 +297,운중교차로 사거리,0,37.39116,127.06308, +298,상상운 삼거리,0,37.39144,127.06527, +299,운중초교 사거리,0,37.39144,127.06801, +300,중산운 사거리,0,37.39173,127.07583, +301,운중동 주민센타,0,37.3916,127.07834, +302,산운 사거리,0,37.39138,127.08157, +303,하산운 사거리,0,37.39056,127.08547,1 +304,판교 도서관 사거리,0,37.38949,127.09017, +305,도서관 삼거리,0,37.38873,127.0939, +306,원마을 8단지 사거리(점멸),0,37.38686,127.09744, +307,광암사거리,0,37.38826,127.09753, +308,낙생농협 4거리,0,37.38963,127.09763, +309,원마을 9단지 사거리,0,37.39073,127.09772, +310,꿈꾸는교회 삼거리,0,37.39273,127.09827, +311,원마을 10단지 3거리,0,37.3934,127.09824, +312,낙생초 삼거리,0,37.39472,127.09817, +313,불꽃교회 삼거리,0,37.39637,127.098, +314,낙원중 삼거리,0,37.39846,127.09672, +315,원마을 11단지 삼거리,0,37.39917,127.0953, +316,성내미 사거리,0,37.3999,127.09474,1 +317,원마을 12단지 사거리(점멸),0,37.4006,127.09417, +318,원마을 13단지 삼거리(소등),0,37.40271,127.0925, +319,영운정사 입구 삼거리,0,37.3989,127.09277, +320,판교로 2사거리,0,37.38881,127.08485, +321,하산운 삼거리,0,37.38703,127.08421, +322,산운길3 삼거리,0,37.39319,127.07594, +323,산운마을 10단지 삼거리,0,37.39073,127.07574, +324,판교원2 사거리,0,37.3893,127.07557, +325,두밀 사거리(국은교),0,37.38793,127.07504, +326,산운마을 13단지 1,0,37.3869,127.07461, +327,산운마을 13단지 2,0,37.38568,127.07424, +328,산운마을 14단지,0,37.3842,127.07427, +329,연구원 사거리(점멸),0,37.39221,127.06352, +330,월든힐스 2단지(점멸),0,37.3928,127.06516, +331,월든힐스 3단지(점멸),0,37.39263,127.06711, +332,운중초교 정문 삼거리,0,37.39221,127.0681, +333,산운마을 4단지 단일로(점멸),0,37.39074,127.06788, +334,운중초교 후문 삼거리,0,37.39299,127.06917, +335,서판교ic 사거리,0,37.38837,127.07002,1 +336,원마을 3단지 북문(점멸),0,37.3886,127.08579, +337,원마을 3단지 서문(점멸),0,37.38769,127.08689, +338,판교중 사거리(점멸),0,37.38672,127.08653, +339,판교 도서관앞(점멸),0,37.39142,127.09085, +340,도서관 주택가 사거리(점멸),0,37.39056,127.09054, +341,판교어린이집 3거리(점멸),0,37.38635,127.08803, +342,원마을 5단지 사거리,0,37.38776,127.08956, +343,판교초교 삼거리,0,37.38636,127.08909, +344,원마을 7단지 단일로(점멸),0,37.3863,127.09315, +345,낙원중 입구(점멸),0,37.39764,127.0954,0 +346,산운초 단일로,0,37.389,127.07848, +347,신미주 1단지 4거리,0,37.40675,127.11545, +348,삼평동사무소,0,37.40583,127.11546, +349,송현초 단일로,0,37.40525,127.11554, +350,송현초 4거리(사송사거리),0,37.40432,127.11617, +351,산운교회 4거리,0,37.40266,127.11685, +352,봇들마을 6단지,0,37.40137,127.11686, +353,삼평중 삼거리,0,37.39985,127.11683,1 +354,구숯내교삼거리,0,37.39834,127.11682, +355,보평초 4거리,0,37.39622,127.11682, +806,대원 사거리,30,37.43346,127.1597, +808,청소년수련관 사거리,30,37.43054,127.15405, +809,아튼빌정문 사거리,30,37.42996,127.1517, +810,아튼빌후문 삼거리,30,37.42935,127.14925, +356,보평초정문 단일로,0,37.39589,127.11843, +357,보평중.고 사거리,0,37.39333,127.11682, +358,동판교로사거리(세계로사거리),0,37.39189,127.11682, +359,백현마을 3.4단지 입구,0,37.39057,127.11681, +360,화랑초교 단일로,0,37.38885,127.11634, +361,재넘어 삼거리,0,37.38819,127.11538, +362,백현동 주택단지 사거리,0,37.38795,127.11442, +363,봇들 사거리,0,37.40323,127.11291,1 +364,삼평 사거리(소방서),0,37.39985,127.11296, +365,봇들교 사거리,0,37.39832,127.11296, +366,판교역 사거리,0,37.39622,127.11294,1 +367,광장로사거리(백현마을 1단지),0,37.39376,127.11294, +368,느티나무사거리(광장로사거리),0,37.39174,127.11294,1 +369,백현 사거리(신백현초),0,37.38794,127.11293, +370,판교테크노13사거리(점멸),0,37.40397,127.10968, +371,판교테크노14사거리(테크윈),0,37.40323,127.10969, +372,SK캐미컬 사거리,0,37.40243,127.10967, +373,안철수연구소 사거리,0,37.40094,127.10967, +374,판교테크노3사거리(안철수),0,37.39984,127.10967,1 +375,동안교 삼거리,0,37.39775,127.10968, +376,동안 사거리,0,37.39623,127.10968, +377,환승주차장 삼거리,0,37.39375,127.10968, +378,낙생지하차도 삼거리,0,37.38968,127.1089, +379,낙생대공원 삼거리,0,37.38795,127.10843, +380,휴먼시아8단지 삼거리,0,37.38733,127.10842, +381,흰돌교회 단일로 1,0,37.38693,127.10842, +382,흰돌교회 단일로 2(점멸),0,37.38612,127.10841, +383,휴먼시아9단지 사거리,0,37.38525,127.1084, +384,백현야구장 삼거리(점멸),0,37.38375,127.11074, +385,동안삼거리(한신교회),0,37.39833,127.11405, +386,롯데마트사거리(봇들마을8단지),0,37.39621,127.11404, +387,안철수연구소 삼거리(점멸),0,37.40095,127.11176, +388,판교역 1번출구 단일로,0,37.39622,127.11127, +389,판교역 3번출구 단일로,0,37.39373,127.11118, +390,백현동사무소,0,37.38793,127.11111, +391,신백현초 정문 단일로(점멸),0,37.38735,127.11132, +392,만도연구소(점멸),0,37.40517,127.10033, +393,포스코삼거리,0,37.40403,127.10221, +394,포스코단일로,0,37.40341,127.10253, +395,넥슨코리아(점멸),0,37.40237,127.10255, +396,NS홈쇼핑(점멸),0,37.40093,127.10255, +397,생태학습관 삼거리(점멸),0,37.39986,127.10372, +398,중앙일보(점멸),0,37.39985,127.10256, +399,우림사거리(점멸),0,37.40373,127.09902, +400,모다아울렛(점멸),0,37.40171,127.10018, +401,마이다스(점멸),0,37.40086,127.10079, +402,송암시스템(점멸),0,37.39984,127.10123, +403,판교테크노중앙사거리,0,37.40322,127.10495,1 +404,판교테크노2사거리,0,37.39984,127.10647, +405,화랑공원 삼거리,0,37.39625,127.10693, +406,판교I/C 사거리,0,37.39424,127.10562, +407,낙생고 삼거리,0,37.3884,127.10158,1 +408,백현고가 4거리,0,37.3816,127.10228,1 +409,성남외고 삼거리,0,37.37823,127.10208, +410,은혜와진리교회 삼거리,0,37.37498,127.10176, +411,궁안동 입구 3거리,0,37.37053,127.10174, +412,삼다가 삼거리,0,37.36956,127.1017, +413,장수촌 단일로,0,37.36663,127.10159, +414,궁내T/G 사거리,0,37.36373,127.1016, +415,보바스기념병원 사거리,0,37.35754,127.10175, +416,로얄냉장 사거리,0,37.35345,127.1017, +417,동원동 삼거리,0,37.34472,127.1015, +418,금곡IC단일로,0,37.34221,127.10257, +419,금곡IC분당진입,0,37.35007,127.10298, +420,정신문화연구원,0,37.39141,127.05731, +421,송유관공사 삼거리,0,37.38044,127.04807, +422,이우학교 삼거리,0,37.35203,127.08585, +423,동막I/C 밑,0,37.35334,127.08143, +424,대장I/C 출구,0,37.36175,127.0678, +425,대장I/C 입구,0,37.35944,127.06821, +426,엔씨소프트단일,0,37.39984,127.10846, +427,스타트업캠퍼스 삼거리(점멸),0,37.40396,127.10648, +428,수질복원센터 삼거리,0,37.39789,127.10157, +429,판교주민센터 뒤(점멸),0,37.38962,127.09975, +430,낙생초교 후문(점멸),0,37.39473,127.10027, +431,낙생초교 정문(점멸),0,37.39473,127.09916, +432,성내미마을 삼거리(점멸),0,37.40161,127.09336, +433,지역난방공사 뒤(점멸),0,37.39504,127.10195, +434,운중교 삼거리(점멸),0,37.39018,127.06781, +435,서판교로 66번길,0,37.39272,127.09952, +436,이배재 사거리,0,37.43166,127.1791,1 +437,반포산업 사거리,0,37.43113,127.1742, +438,코멕스 삼거리,0,37.43153,127.17216, +439,공단본부 사거리,0,37.43218,127.16762, +440,대원지구대 삼거리,0,37.43265,127.16595,1 +441,한일레미콘 단일로,0,37.4329,127.16404, +442,대원 사거리,0,37.43342,127.15993,1 +443,자이아파트 삼거리,0,37.43238,127.15772, +444,청소년수련관 사거리,0,37.43056,127.15404,1 +445,아튼빌정문 사거리,0,37.42997,127.15166, +446,아튼빌후문 삼거리,0,37.42935,127.14916, +447,방송고 단일로,0,37.42887,127.14718, +448,검단초교 사거리,0,37.42829,127.14474, +449,하대원파출소 사거리,0,37.42826,127.14182,1 +450,성남동현대아파트 삼거리,0,37.4291,127.14003, +451,중원구청 사거리,0,37.42972,127.13715,1 +452,신광교회 단일로,0,37.42913,127.13533, +453,월드카센타 앞,0,37.42892,127.13326, +454,천주교성당 앞,0,37.4291,127.13151, +455,금광중 후문,0,37.44749,127.17104, +456,하원초교 삼거리,0,37.44719,127.16961, +457,신구대 사거리,0,37.44671,127.16731, +458,금광2동사무소 단일로,0,37.44611,127.1645, +459,금광1동사무소 사거리,0,37.44527,127.16184, +460,단대쇼핑뒤 단일로,0,37.44462,127.16, +461,해오름 사거리,0,37.44397,127.15791, +462,중앙동사무소 삼거리,0,37.44307,127.15574, +463,중앙동 사거리,0,37.44178,127.15285, +464,성남공판장 단일로,0,37.44064,127.15033, +465,제일초교 단일로,0,37.43958,127.14788, +466,성호시장 사거리,0,37.43845,127.14535, +467,성일여중고 사거리,0,37.43666,127.14201, +468,성일여고 삼거리,0,37.43602,127.14089, +469,성호지구대 단일로,0,37.4354,127.13976, +470,운동장후문 삼거리,0,37.43462,127.13811, +471,운동장 사거리,0,37.434,127.13504,0 +472,삼영전자 앞,0,37.44397,127.15491, +473,세이브존 단일로,0,37.4432,127.15333, +474,우리은행 사거리,0,37.44263,127.15218,1 +475,성남우체국,0,37.4418,127.1501, +476,신흥 사거리,0,37.43962,127.14455, +477,수진역 사거리,0,37.43762,127.14097,1 +478,모란 사거리,0,37.43358,127.12892,1 +479,둔전교회 앞,0,37.43342,127.12747, +480,고속화도로 출구,0,37.43248,127.11988, +481,탄천 삼거리,0,37.43242,127.11894, +482,동양공업사 사거리,0,37.4339,127.17895, +483,보통골 동성교통앞,0,37.43533,127.17884, +484,근로복지관 삼거리,0,37.43794,127.17861,1 +485,궁전아파트 삼거리,0,37.44013,127.17895, +486,궁전아파트 사거리,0,37.44134,127.17911, +487,파리크라상 사거리,0,37.43504,127.17031, +488,구공단파출소 오거리,0,37.43776,127.17374,1 +489,순흥물산 삼거리,0,37.43983,127.17611, +490,자동차검사소 삼거리,0,37.44189,127.17765, +491,중원경찰서 사거리,0,37.44232,127.16868, +492,금광1파출소 사거리,0,37.44514,127.16826, +493,신구대서문 삼거리,0,37.44873,127.16661, +494,금광중 단일로,0,37.44616,127.17079, +495,금광 삼거리,0,37.44793,127.17307, +496,래미안 삼거리,0,37.4502,127.17436, +497,금상초교 단일로,0,37.44253,127.16612, +498,금광주유소 단일로,0,37.4418,127.16417, +499,학년쇼핑 단일로,0,37.44036,127.1625, +500,단남아파트 단일로,0,37.44215,127.15962, +501,해오름주차장 사거리,0,37.44117,127.15994, +502,상대원고개 단일로,0,37.4398,127.15937, +503,상대원시장 삼거리,0,37.43639,127.15814, +504,상대원고개 농협단일로,0,37.43542,127.15812, +505,대원터널 앞,0,37.43257,127.16034, +506,도촌동입구 삼거리,0,37.41725,127.14993, +507,도촌동입구 사거리,0,37.41547,127.14781, +508,신지교회 단일로,0,37.43703,127.1463, +509,성일고 삼거리,0,37.43563,127.14727, +510,성남서고 삼거리,0,37.43471,127.1479, +511,하대원현대아파트 삼거리,0,37.43243,127.1507, +512,대하초교 단일로,0,37.43112,127.1512, +513,은행동순환로밑 삼거리,0,37.45313,127.17252, +514,중부초교 사거리,0,37.45086,127.16588, +515,은행동현대아파트 삼거리,0,37.45029,127.16312, +516,중앙병원 단일로,0,37.45362,127.16194, +517,해성주유소 사거리,0,37.42827,127.13787, +518,여수지하차도위 사거리,0,37.42154,127.12883,1 +519,모란시장 사거리,0,37.42981,127.1294,1 +520,모란 삼거리,0,37.43281,127.12903, +521,모란견인소 삼거리,0,37.43013,127.12016, +522,탄천로변 삼거리,0,37.43038,127.11889, +523,공단주유소 오거리,0,37.43461,127.17396, +524,우림라이온스밸리 사거리,0,37.43354,127.17112, +525,에이스경로당 사거리,0,37.44128,127.17143, +526,에스콰이어교회 앞,0,37.43963,127.17243, +527,중원보건소 삼거리,0,37.44173,127.17027, +528,갈현 삼거리,0,37.42247,127.17061, +529,중앙초교 앞,0,37.43529,127.14318, +530,여울목 삼거리,0,37.42021,127.15724, +531,도촌동 1단지앞,0,37.41485,127.14834, +532,도촌남로 사거리,0,37.41422,127.15018, +533,도촌프라자 단일로,0,37.41443,127.15194, +534,도촌중입구 사거리,0,37.41448,127.15508, +535,도촌초교 단일로,0,37.414,127.15724,0 +536,도촌초교 삼거리,0,37.41361,127.15836, +537,도촌초교 정문,0,37.41417,127.15877, +538,도촌 수산횟집 앞,0,37.41575,127.15064, +539,도촌북로 사거리,0,37.41672,127.15094, +540,도촌우체국 삼거리,0,37.41709,127.15306, +541,도촌파출소 사거리,0,37.41737,127.15531, +542,도촌종합복지관삼거리,0,37.41518,127.15948, +543,901동앞 삼거리,0,37.41253,127.1634, +544,영생사업소 입구,0,37.4198,127.18303, +545,추모원 주차장입구,0,37.42085,127.1827, +546,장례식장 주차장입구,0,37.42182,127.18233, +547,마지로 신호등 1,0,37.42831,127.13139, +548,마지로 신호등 2,0,37.42817,127.13242, +549,마지로 신호등 3,0,37.4282,127.1337, +550,마지로 신호등 4,0,37.42832,127.13591, +551,보육시설 삼거리,0,37.44251,127.15655, +552,힐스테이 삼거리,0,37.43575,127.14989, +553,시청광장 삼거리,0,37.41879,127.12715, +554,시청정문 사거리,0,37.41879,127.12569, +555,시청공원 삼거리,0,37.41759,127.12456, +556,센트럴타운 사거리,0,37.41878,127.13063, +557,여수초교 삼거리,0,37.41877,127.13241, +558,여수공원 삼거리,0,37.41877,127.13394, +559,연꽃마을4단지 사거리,0,37.41866,127.13547, +560,옥토교회 사거리,0,37.4179,127.13735, +561,공영주차장 단일로,0,37.45737,127.16553, +562,은행2동주민센타 삼거리,0,37.45675,127.16663, +563,이원빌라 사거리,0,37.45613,127.16775, +564,두산아파트 사거리,0,37.45455,127.16387, +565,상원초교 입구,0,37.45477,127.16258, +566,모란 LH 1단지 정문,0,37.43075,127.12607, +567,한신병원앞,0,37.4332,127.131,0 +568,모란시장 삼거리,0,37.42996,127.12798, +569,모란시장 단일로,0,37.42983,127.12709, +570,모란주차장사거리,0,37.42969,127.12613, +571,모란공원 단일로,0,37.42959,127.12452, +572,도촌중 단일로,0,37.41672,127.15526, +573,청진동 수산횟집앞,0,37.43441,127.15874, +574,지식산업센타 사거리,0,37.44031,127.17424, +575,금광지구 8번게이트,0,37.44289,127.16712, +576,오복슈퍼앞,0,37.45014,127.16613, +577,상대원초교 정문,0,37.44278,127.18114, +578,도촌스위첸앞,0,37.41454,127.1539, +579,도촌9단지 정문,0,37.41194,127.16294, +580,한양수자인,0,37.44769,127.17192, +581,스타타워 사거리,0,37.43436,127.17666, +582,슈페리어 단일로,0,37.43333,127.17897, +583,상대원 터널,0,37.42791,127.17951, +584,상대원 터널 우회전전용,0,37.42811,127.17991, +585,호반아파트 후문,0,37.42895,127.09436, +586,호반아파트 정문,0,37.42999,127.09621, +587,고등마을,0,37.43036,127.09896, +588,고등지구대,0,37.43022,127.10068, +589,청구아파트,0,37.44327,127.15089, +590,두산아파트,0,37.44398,127.14961,0 +591,한신아파트 후문,0,37.445,127.15082, +592,희망대사거리,0,37.44482,127.14799,0 +593,희망대초교,0,37.44662,127.14941, +594,구)신흥시장길,0,37.44559,127.14657, +595,태평제일교회,0,37.44781,127.1439, +596,신흥성당,0,37.44871,127.14306, +597,신흥초교,0,37.44954,127.14355, +598,성남여중,0,37.44937,127.14247, +599,가일빌딩(나나헤어),0,37.44446,127.1287, +600,태평초교,0,37.44535,127.13168, +601,태평3동 행정복지센터,0,37.4456,127.13256, +602,통보2차 아파트,0,37.44664,127.13612, +603,영락교회,0,37.44699,127.13723, +604,금빛초교 정문,0,37.4475,127.13897, +605,금빛초교 후문,0,37.44798,127.14062, +606,논골도서관,0,37.45625,127.16019, +607,양지아파트,0,37.45698,127.15943, +608,단대제1공영주차장,0,37.45872,127.15853, +609,문원중학교,0,37.46111,127.15798, +610,성보경영고,0,37.4624,127.15797, +611,푸르지오 입구,0,37.45104,127.15885, +612,푸르지오 정문,0,37.45134,127.15761, +613,진로아파트,0,37.45159,127.15681, +614,단대초교,0,37.45295,127.15661, +615,단대지구대,0,37.45252,127.15812, +616,구)목넴기,0,37.40916,127.09803, +617,남산마을,0,37.40883,127.09725, +618,구)금토슈퍼,0,37.40868,127.09621, +619,창조벨리,0,37.40863,127.09557, +620,판교임시IC,0,37.40856,127.09491, +621,금토1통,0,37.41722,127.08102, +622,금토동 삼거리,0,37.41092,127.09947, +623,창조벨리 삼거리,0,37.41221,127.09973, +624,세종연구소 삼거리,0,37.41631,127.1016, +625,시흥사거리,0,37.42232,127.10205, +626,고등소방서,0,37.42354,127.10215, +627,구) 고등주민센터,0,37.42446,127.1022, +628,사송동 차고지,0,37.41539,127.11476, +629,수자원공사입구,0,37.41451,127.11349, +630,사송교차로,0,37.41172,127.11403, +631,단대오거리,0,37.44507,127.15681,1 +632,구)단대동주민센터,0,37.44816,127.15835, +633,금광시장,0,37.45086,127.15952, +634,법원,0,37.452,127.16004, +635,중앙병원,0,37.45413,127.16098,1 +636,상원초교,0,37.4552,127.1618, +637,은행지구대,0,37.45707,127.16339, +638,은행시장,0,37.45804,127.16423, +639,을지대 정문,0,37.45986,127.16577, +640,을지대 후문,0,37.46148,127.16751, +641,양지지구대,0,37.46266,127.16916, +642,남한산성 유원지,0,37.46355,127.17029, +643,모란고개,0,37.43654,127.12838, +644,태평역,0,37.43974,127.12775,1 +645,수정경찰서,0,37.44181,127.12739, +646,태평고개,0,37.44395,127.12701, +647,구)성남병원,0,37.44794,127.12673,1 +648,가천대,0,37.45057,127.12662, +649,복정초소,0,37.45352,127.12652, +650,복정지구대,0,37.45628,127.12629, +651,동서울대,0,37.45976,127.12585,1 +652,복정초교,0,37.46185,127.12944, +653,국제학교,0,37.46545,127.12629,1 +654,삼정그린뷰아파트,0,37.43999,127.12145, +655,삼부아파트 후문,0,37.43952,127.12436, +656,삼부아파트,0,37.4395,127.12589, +657,곽생로,0,37.44049,127.13028, +658,중앙시장,0,37.44093,127.13181, +659,한국관,0,37.44144,127.13346, +660,탄리사거리,0,37.44221,127.13613, +661,구)시청,0,37.44309,127.13906, +662,의료원 장례식장,0,37.44385,127.13869, +663,의료원 정문,0,37.44407,127.1385, +664,외환은행,0,37.44356,127.14065, +665,숯골사거리,0,37.44409,127.14248,1 +666,성남초교(단),0,37.44494,127.14405, +667,성남초교(4),0,37.44636,127.14541,1 +668,구)청직이약국,0,37.44811,127.14531, +669,신흥2동 주민센터,0,37.4491,127.14584, +670,수정구청,0,37.44962,127.14624, +671,포레스티아 서문,0,37.45063,127.14702, +672,포레스티아 중문,0,37.45227,127.14934,1 +673,포레스티아 동문 삼거리,0,37.45406,127.14971, +674,산성역,0,37.45642,127.15006,1 +675,폴리텍,0,37.45869,127.15196, +676,변전소,0,37.46225,127.15521, +677,청산골,0,37.4409,127.14395, +678,구)시청고개,0,37.44208,127.14342, +679,왕남초교 주차장 삼거리,0,37.42777,127.09895, +680,왕남초교 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08:06:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:05:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:04:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:03:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:02:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:01:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:00:00,0,0,0,0,0,0,,,0,0 diff --git a/Data/tables/20240207_221605/S_INT_CONFIG.csv b/Data/tables/20240207_221605/S_INT_CONFIG.csv new file mode 100644 index 000000000..9b6b26fc0 --- /dev/null +++ b/Data/tables/20240207_221605/S_INT_CONFIG.csv @@ -0,0 +1,759 @@ +INT_NO,INT_NAME,SA_NO,INT_LAT,INT_LNG,IS_SMART +1,도촌4거리,1,37.41405,127.14385,1 +2,동원SK삼거리(아시아 주유소),1,37.41282,127.14382, +3,상탑4거리,0,37.40809,127.14296, +4,돌마4거리(상탑3거리),0,37.40683,127.14201,1 +5,상탑초3거리,0,37.40561,127.14473, +6,야탑3치안센터(상탑 P.B),0,37.40549,127.14823, +7,테크노 단일로,0,37.40486,127.15121, +8,분당테크노파크A동,0,37.40499,127.15034, +9,분당테크노파크C동(분당테크노파크),0,37.40545,127.15526, +10,야탑차량등록사업소,0,37.40682,127.1547, +11,야탑보듬이,0,37.40636,127.1522, +12,대원빌라4거리,0,37.40631,127.14852, 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+96,오일뱅크4거리,0,37.38736,127.12439, +97,분당우체국,0,37.38686,127.12371, +98,우리은행4거리,0,37.38629,127.12289, +99,하나은행4거리,0,37.38555,127.12186, +100,나우병원4거리,0,37.38446,127.12038, +101,서현농협3거리,0,37.38355,127.12139,0 +102,서현교4거리,0,37.38677,127.11658, +103,분당소방서3거리(서현소방서3거리),0,37.38539,127.11812, +104,분당구청4거리,0,37.38399,127.11973, +105,분당구청 단일로,0,37.38309,127.12072, +106,서현4거리,0,37.38222,127.12171, +107,이매4거리,0,37.38621,127.12685,1 +108,수내4거리,0,37.38402,127.11402,1 +109,수내교4거리(조선내화4거리),0,37.38174,127.11658,1 +110,경동빌딩(분당구청 단일로),0,37.38083,127.11758,0 +111,분당4거리,0,37.37998,127.11857,1 +112,서현먹자골목 입구,0,37.38386,127.13056, +113,서현도서관3거리(먹자골SK주유소),0,37.38372,127.13337, +114,남해수산3거리,0,37.38334,127.13591, +115,새마을4거리,0,37.38313,127.13834, +116,서현포스파크,0,37.38315,127.13968, +117,은수사3거리,0,37.38327,127.1433, +118,새마을연수원사거리(수도병원입구),0,37.38376,127.15048, +119,안골입구4거리,0,37.38285,127.1322, +120,서현시범단지우성APT,0,37.38178,127.13072, +121,올림픽스포츠센터,0,37.38038,127.12873, +122,서현시범단지한양APT,0,37.37947,127.12753, +123,서현3거리,0,37.3784,127.12595, +124,서현중학교 단일로,0,37.38128,127.13396, +125,분당어린이공원,0,37.37856,127.13123,0 +126,서당4거리,0,37.37942,127.13604,1 +127,돌마지하차도,0,37.37744,127.13323,0 +128,효자촌4거리,0,37.37539,127.1304,1 +129,당촌초교,0,37.37361,127.13238,0 +130,서당3거리(서당동입구),0,37.37629,127.13959,1 +131,동남프라자,0,37.37399,127.1364, +132,서당삼환APT,0,37.3731,127.13517,0 +133,장안4거리,0,37.37223,127.13395,1 +134,장안동3거리,0,37.37156,127.13301, +135,장안동입구4거리,0,37.37455,127.14238, +136,장안교3거리(장안동 중앙유치원),0,37.37287,127.14158, +137,장안동건영113동,0,37.37214,127.14073, +138,대진고 후문,0,37.37014,127.13793, +139,대진고4거리,0,37.3695,127.13703, +140,장안초등학교,0,37.37166,127.14392,0 +141,장안중3거리,0,37.37213,127.1472,1 +142,장안타운건영107동,0,37.37096,127.14467, +143,장안타운123동,0,37.37039,127.14388, +144,태현공원4거리,0,37.36941,127.14249, +145,장안로삼거리(신동아빌라),0,37.36777,127.14023, +146,장안건영빌라 단일로,0,37.371,127.14761,0 +147,요한성당3거리,0,37.36886,127.14863,1 +148,지역난방공사,0,37.36759,127.14583, +149,지역난방 삼거리,0,37.36794,127.14639, +150,새롬교회(광림교회),0,37.36639,127.14208, +151,현대오피스빌딩(APUS 3거리),0,37.38017,127.11438, +152,휴맥스빌리지(롯데백화점),0,37.37856,127.11217,0 +153,초림3거리,0,37.37539,127.12181,1 +154,수내동 우리은행,0,37.37404,127.11996, +155,롯데파크타운,0,37.37351,127.1192, +156,내정4거리,0,37.37165,127.11659, +157,내정어린이공원,0,37.37217,127.12208,0 +158,샛별4거리,0,37.37198,127.12565,1 +159,수내지하차도,0,37.37019,127.12313,0 +160,푸른마을4거리,0,37.3685,127.12083,1 +161,정자한일아파트,0,37.36778,127.12169, +162,수내중 단일로,0,37.37046,127.12731, +163,샛별3거리,0,37.36914,127.12879, +164,한방병원 단일로,0,37.36781,127.12787, +165,한방병원4거리,0,37.3671,127.12727, +166,수내파출소(수내 P.B),0,37.36637,127.12627, +167,푸른마을삼거리(연정교회3거리),0,37.36515,127.12458, +168,한솔청구아파트,0,37.36438,127.12367, +169,불정3거리,0,37.36347,127.12316, +170,정든마을우성616동(우성116동),0,37.36235,127.12311, +171,정자동아2차,0,37.36101,127.1231, +172,분당중앙고 삼거리,0,37.36013,127.12301, +173,분당중앙고,0,37.35944,127.12231, +174,정자동아아파트,0,37.35923,127.12034, +175,한솔삼거리(한솔마을3거리),0,37.36857,127.11478, +176,느티마을4단지,0,37.36724,127.1148, +177,한솔6단지(정자동사무소),0,37.36619,127.1148, +178,신기4거리,0,37.36346,127.1148, +179,한솔마을5단지(한솔마을단일로),0,37.36348,127.11725, +180,정든마을4거리,0,37.36348,127.11906, +181,불정4거리,0,37.35921,127.11908,1 +182,탄천초교,0,37.3617,127.11482, +183,계원삼거리(한국통신4거리),0,37.35995,127.11483, +184,잡월드 사거리,0,37.3784,127.10897,1 +185,백현교4거리,0,37.37719,127.11034, +186,삼성로얄팰리스(판테온정문),0,37.37633,127.11132, +187,정자4거리,0,37.37545,127.11227,1 +188,백궁교(판테온후문),0,37.37525,127.10981, +189,백궁4거리,0,37.37368,127.10981, +190,백현환기소3거리,0,37.37245,127.10422,1 +191,늘푸른고등학교 교차로(백궁주유소4),0,37.37244,127.10593, +192,백궁3거리,0,37.37243,127.10828,1 +193,상록마을아이파크분당1(백궁현대 ),0,37.37095,127.10592, +194,아이파크분당3단지,0,37.36939,127.10593, +195,마테오성당(백현중학교),0,37.37181,127.10983, +196,백현중학교 삼거리,0,37.37047,127.10982, +197,백현초등학교,0,37.37193,127.11223, +198,백현어린이공원,0,37.37132,127.11183, +199,젤존타워Ⅰ(백궁우체국4거리),0,37.36853,127.10593, +200,궁내4거리,0,37.36852,127.10808,1 +201,느티마을4거리,0,37.36854,127.10984,0 +202,느티마을 안촌유치원,0,37.36855,127.11241, +203,아름방송4거리,0,37.36666,127.10594, +204,정자역 단일로,0,37.36614,127.1081, +205,분당경찰서4거리(벤체타운4거리),0,37.36563,127.10595, +206,상록마을 321동,0,37.36483,127.10981, +207,정자지하차도3거리(정자환기소3거리),0,37.36347,127.10461,1 +208,정자1동주민센터(연강병원4거리),0,37.36345,127.10596,0 +209,금곡교4거리,0,37.36344,127.10811,1 +210,상록4거리(상록마을입구4거리),0,37.36347,127.10982,0 +211,상록마을단일로,0,37.36346,127.11237, +212,상록마을302동 단일로,0,37.36347,127.11306, +213,정자대림아크로텔 단일로,0,37.36232,127.10709, +214,금곡주유소3거리,0,37.3599,127.10424, +215,정자일로1사거리(청솔공업사 4거리),0,37.35991,127.10599, +216,불정교4거리,0,37.3599,127.10812,1 +217,불정교3거리(주택전시관4거리),0,37.35994,127.10986,0 +218,순복음교회앞,0,37.35995,127.11176, +219,늘푸른초등학교,0,37.35788,127.106, +220,정자일로2사거리(보성APT 4거리),0,37.35664,127.10601,0 +221,임광보성아파트(금곡보성아파트),0,37.35665,127.1072, +222,금곡4거리,0,37.35666,127.10849, +223,분당경영고(분당 정보산업고),0,37.35662,127.11017, +224,청솔주공5단지,0,37.35565,127.11268, +225,청솔초교,0,37.35451,127.11472, +226,한국가스공사,0,37.35468,127.1184, +227,돌마교4거리,0,37.3532,127.11711, +228,우방우성102동,0,37.35253,127.11838, +229,파랑새공원단일로,0,37.35495,127.106,0 +230,청솔유천화인아파트 정문(한전기공),0,37.35433,127.106,0 +231,정자일로3사거리(YMCA4거리),0,37.35342,127.10601, +232,청솔마을4거리,0,37.35344,127.10888, +233,청솔종합사회복지관(청솔마을 단일로),0,37.35333,127.11091, +234,이솔유치원(청솔마을 8-9단지),0,37.3527,127.11227, +235,코오롱 트리폴리스,0,37.35138,127.10627, +236,금곡교차로(금곡I/C 3거리),0,37.35007,127.10432,1 +237,머내공원4거리(아울렛4거리),0,37.35002,127.10638, +238,미금역4거리,0,37.35004,127.1089,1 +239,까치마을신원아파트308동(골드프라자),0,37.35003,127.11202, +240,까치마을4거리,0,37.35102,127.11523, +241,까치마을 단일로,0,37.34928,127.11608, +242,꿈터유치원,0,37.34757,127.11202, +243,미금4거리,0,37.34584,127.10891, +244,하얀마을6단지,0,37.34602,127.11345, +245,하얀마을4거리,0,37.34652,127.11608, +246,안디옥감리교회,0,37.34651,127.11836, +247,하얀마을3거리,0,37.34648,127.12008, +248,오리초교 단일로,0,37.34472,127.1161, +249,동막교삼거리,0,37.34359,127.10891, +250,시그마3거리,0,37.34265,127.10451, +251,시그마Ⅱ 4거리,0,37.34265,127.10591, +252,동막교4거리,0,37.34262,127.10891,1 +253,LH오리사옥 정문3거리,0,37.34249,127.11001,0 +254,구미교4거리,0,37.34219,127.11609, +255,구미그랜드빌라,0,37.34352,127.11909, +256,성남우편집중국3거리,0,37.34073,127.10895, +257,LH오리사옥 후문(주공후문3거리),0,37.34072,127.10963, +258,LH스포츠센터(주택문화관),0,37.34072,127.11029, +259,오리공원3거리(구미동 YMCA),0,37.34074,127.11169, +260,까르프3거리,0,37.33968,127.10448, +261,NH주유소4거리(하나로후문4거리),0,37.3397,127.10594, +262,오리홈플러스4거리(까르프4거리),0,37.33969,127.10739, +263,오리역3거리,0,37.33971,127.10895, +264,벽산3거리,0,37.33842,127.10472, +265,하나로마트 정문,0,37.33733,127.10673, +266,농수산물센터4거리(벽산사거리),0,37.33599,127.10895,1 +267,온세통신3거리,0,37.33589,127.11174, +268,무지개 LG아파트,0,37.33644,127.11366, +269,오리3거리,0,37.3381,127.10895,1 +270,성우스타우스(오리먹자골입구),0,37.33809,127.11029, +271,오리1교 4거리(오리교 4거리),0,37.33811,127.11175, +272,오리2교 4거리(오리교 4거리),0,37.33811,127.11311, +273,미금교3거리,0,37.35053,127.12005, +274,서울대병원 단일로,0,37.35063,127.12208, +275,서울대병원 입구,0,37.35019,127.12331, +276,장애인고용공단,0,37.3484,127.12391, +277,서울시니어스타워(서울대병원 주차장),0,37.34732,127.12413, +278,구미중학교(구미중 단일로),0,37.34501,127.12462,0 +279,구미4거리,0,37.34343,127.12601, +280,구미제일아파트,0,37.34272,127.12476,0 +281,구미라이프APT,0,37.34156,127.12347, +282,구미동사무소,0,37.33974,127.12167, +283,구미파출소3거리(구미 P.B),0,37.33909,127.12073, +284,구미초교3거리,0,37.33817,127.11905, +285,구미초정문,0,37.33753,127.11905, +286,구미초교 단일로,0,37.33811,127.1176, +287,구미공원4거리,0,37.34041,127.11612, +288,구미주공4단지,0,37.33923,127.11615, +289,무지개4거리,0,37.33813,127.11614,1 +290,구미대림APT,0,37.33659,127.11673, +291,구미동대림APT4거리,0,37.33576,127.11771, +292,무지개12단지(주공 두산아파트),0,37.33496,127.11816, +293,미금로3거리,0,37.33406,127.11809, +294,대림아파트 후문,0,37.33455,127.11598, +295,불곡초교 단일로,0,37.34037,127.11866,0 +296,금곡I/C분당진입,0,37.35421,127.10072,0 +297,운중교차로 사거리,0,37.39116,127.06308, +298,상상운 삼거리,0,37.39144,127.06527, +299,운중초교 사거리,0,37.39144,127.06801, +300,중산운 사거리,0,37.39173,127.07583, +301,운중동 주민센타,0,37.3916,127.07834, +302,산운 사거리,0,37.39138,127.08157, +303,하산운 사거리,0,37.39056,127.08547,1 +304,판교 도서관 사거리,0,37.38949,127.09017, +305,도서관 삼거리,0,37.38873,127.0939, +306,원마을 8단지 사거리(점멸),0,37.38686,127.09744, +307,광암사거리,0,37.38826,127.09753, +308,낙생농협 4거리,0,37.38963,127.09763, +309,원마을 9단지 사거리,0,37.39073,127.09772, +310,꿈꾸는교회 삼거리,0,37.39273,127.09827, +311,원마을 10단지 3거리,0,37.3934,127.09824, +312,낙생초 삼거리,0,37.39472,127.09817, +313,불꽃교회 삼거리,0,37.39637,127.098, +314,낙원중 삼거리,0,37.39846,127.09672, +315,원마을 11단지 삼거리,0,37.39917,127.0953, +316,성내미 사거리,0,37.3999,127.09474,1 +317,원마을 12단지 사거리(점멸),0,37.4006,127.09417, +318,원마을 13단지 삼거리(소등),0,37.40271,127.0925, +319,영운정사 입구 삼거리,0,37.3989,127.09277, +320,판교로 2사거리,0,37.38881,127.08485, +321,하산운 삼거리,0,37.38703,127.08421, +322,산운길3 삼거리,0,37.39319,127.07594, +323,산운마을 10단지 삼거리,0,37.39073,127.07574, +324,판교원2 사거리,0,37.3893,127.07557, +325,두밀 사거리(국은교),0,37.38793,127.07504, +326,산운마을 13단지 1,0,37.3869,127.07461, +327,산운마을 13단지 2,0,37.38568,127.07424, +328,산운마을 14단지,0,37.3842,127.07427, +329,연구원 사거리(점멸),0,37.39221,127.06352, +330,월든힐스 2단지(점멸),0,37.3928,127.06516, +331,월든힐스 3단지(점멸),0,37.39263,127.06711, +332,운중초교 정문 삼거리,0,37.39221,127.0681, +333,산운마을 4단지 단일로(점멸),0,37.39074,127.06788, +334,운중초교 후문 삼거리,0,37.39299,127.06917, +335,서판교ic 사거리,0,37.38837,127.07002,1 +336,원마을 3단지 북문(점멸),0,37.3886,127.08579, +337,원마을 3단지 서문(점멸),0,37.38769,127.08689, +338,판교중 사거리(점멸),0,37.38672,127.08653, +339,판교 도서관앞(점멸),0,37.39142,127.09085, +340,도서관 주택가 사거리(점멸),0,37.39056,127.09054, +341,판교어린이집 3거리(점멸),0,37.38635,127.08803, +342,원마을 5단지 사거리,0,37.38776,127.08956, +343,판교초교 삼거리,0,37.38636,127.08909, +344,원마을 7단지 단일로(점멸),0,37.3863,127.09315, +345,낙원중 입구(점멸),0,37.39764,127.0954,0 +346,산운초 단일로,0,37.389,127.07848, +347,신미주 1단지 4거리,0,37.40675,127.11545, +348,삼평동사무소,0,37.40583,127.11546, +349,송현초 단일로,0,37.40525,127.11554, +350,송현초 4거리(사송사거리),0,37.40432,127.11617, +351,산운교회 4거리,0,37.40266,127.11685, +352,봇들마을 6단지,0,37.40137,127.11686, +353,삼평중 삼거리,0,37.39985,127.11683,1 +354,구숯내교삼거리,0,37.39834,127.11682, +355,보평초 4거리,0,37.39622,127.11682, +806,대원 사거리,30,37.43346,127.1597, +808,청소년수련관 사거리,30,37.43054,127.15405, +809,아튼빌정문 사거리,30,37.42996,127.1517, +810,아튼빌후문 삼거리,30,37.42935,127.14925, +356,보평초정문 단일로,0,37.39589,127.11843, +357,보평중.고 사거리,0,37.39333,127.11682, +358,동판교로사거리(세계로사거리),0,37.39189,127.11682, +359,백현마을 3.4단지 입구,0,37.39057,127.11681, +360,화랑초교 단일로,0,37.38885,127.11634, +361,재넘어 삼거리,0,37.38819,127.11538, +362,백현동 주택단지 사거리,0,37.38795,127.11442, +363,봇들 사거리,0,37.40323,127.11291,1 +364,삼평 사거리(소방서),0,37.39985,127.11296, +365,봇들교 사거리,0,37.39832,127.11296, +366,판교역 사거리,0,37.39622,127.11294,1 +367,광장로사거리(백현마을 1단지),0,37.39376,127.11294, +368,느티나무사거리(광장로사거리),0,37.39174,127.11294,1 +369,백현 사거리(신백현초),0,37.38794,127.11293, +370,판교테크노13사거리(점멸),0,37.40397,127.10968, +371,판교테크노14사거리(테크윈),0,37.40323,127.10969, +372,SK캐미컬 사거리,0,37.40243,127.10967, +373,안철수연구소 사거리,0,37.40094,127.10967, +374,판교테크노3사거리(안철수),0,37.39984,127.10967,1 +375,동안교 삼거리,0,37.39775,127.10968, +376,동안 사거리,0,37.39623,127.10968, +377,환승주차장 삼거리,0,37.39375,127.10968, +378,낙생지하차도 삼거리,0,37.38968,127.1089, +379,낙생대공원 삼거리,0,37.38795,127.10843, +380,휴먼시아8단지 삼거리,0,37.38733,127.10842, +381,흰돌교회 단일로 1,0,37.38693,127.10842, +382,흰돌교회 단일로 2(점멸),0,37.38612,127.10841, +383,휴먼시아9단지 사거리,0,37.38525,127.1084, +384,백현야구장 삼거리(점멸),0,37.38375,127.11074, +385,동안삼거리(한신교회),0,37.39833,127.11405, +386,롯데마트사거리(봇들마을8단지),0,37.39621,127.11404, +387,안철수연구소 삼거리(점멸),0,37.40095,127.11176, +388,판교역 1번출구 단일로,0,37.39622,127.11127, +389,판교역 3번출구 단일로,0,37.39373,127.11118, +390,백현동사무소,0,37.38793,127.11111, +391,신백현초 정문 단일로(점멸),0,37.38735,127.11132, +392,만도연구소(점멸),0,37.40517,127.10033, +393,포스코삼거리,0,37.40403,127.10221, +394,포스코단일로,0,37.40341,127.10253, +395,넥슨코리아(점멸),0,37.40237,127.10255, +396,NS홈쇼핑(점멸),0,37.40093,127.10255, +397,생태학습관 삼거리(점멸),0,37.39986,127.10372, +398,중앙일보(점멸),0,37.39985,127.10256, +399,우림사거리(점멸),0,37.40373,127.09902, +400,모다아울렛(점멸),0,37.40171,127.10018, +401,마이다스(점멸),0,37.40086,127.10079, +402,송암시스템(점멸),0,37.39984,127.10123, +403,판교테크노중앙사거리,0,37.40322,127.10495,1 +404,판교테크노2사거리,0,37.39984,127.10647, +405,화랑공원 삼거리,0,37.39625,127.10693, +406,판교I/C 사거리,0,37.39424,127.10562, +407,낙생고 삼거리,0,37.3884,127.10158,1 +408,백현고가 4거리,0,37.3816,127.10228,1 +409,성남외고 삼거리,0,37.37823,127.10208, +410,은혜와진리교회 삼거리,0,37.37498,127.10176, +411,궁안동 입구 3거리,0,37.37053,127.10174, +412,삼다가 삼거리,0,37.36956,127.1017, +413,장수촌 단일로,0,37.36663,127.10159, +414,궁내T/G 사거리,0,37.36373,127.1016, +415,보바스기념병원 사거리,0,37.35754,127.10175, +416,로얄냉장 사거리,0,37.35345,127.1017, +417,동원동 삼거리,0,37.34472,127.1015, +418,금곡IC단일로,0,37.34221,127.10257, +419,금곡IC분당진입,0,37.35007,127.10298, +420,정신문화연구원,0,37.39141,127.05731, +421,송유관공사 삼거리,0,37.38044,127.04807, +422,이우학교 삼거리,0,37.35203,127.08585, +423,동막I/C 밑,0,37.35334,127.08143, +424,대장I/C 출구,0,37.36175,127.0678, +425,대장I/C 입구,0,37.35944,127.06821, +426,엔씨소프트단일,0,37.39984,127.10846, +427,스타트업캠퍼스 삼거리(점멸),0,37.40396,127.10648, +428,수질복원센터 삼거리,0,37.39789,127.10157, +429,판교주민센터 뒤(점멸),0,37.38962,127.09975, +430,낙생초교 후문(점멸),0,37.39473,127.10027, +431,낙생초교 정문(점멸),0,37.39473,127.09916, +432,성내미마을 삼거리(점멸),0,37.40161,127.09336, +433,지역난방공사 뒤(점멸),0,37.39504,127.10195, +434,운중교 삼거리(점멸),0,37.39018,127.06781, +435,서판교로 66번길,0,37.39272,127.09952, +436,이배재 사거리,0,37.43166,127.1791,1 +437,반포산업 사거리,0,37.43113,127.1742, +438,코멕스 삼거리,0,37.43153,127.17216, +439,공단본부 사거리,0,37.43218,127.16762, +440,대원지구대 삼거리,0,37.43265,127.16595,1 +441,한일레미콘 단일로,0,37.4329,127.16404, +442,대원 사거리,0,37.43342,127.15993,1 +443,자이아파트 삼거리,0,37.43238,127.15772, +444,청소년수련관 사거리,0,37.43056,127.15404,1 +445,아튼빌정문 사거리,0,37.42997,127.15166, +446,아튼빌후문 삼거리,0,37.42935,127.14916, +447,방송고 단일로,0,37.42887,127.14718, +448,검단초교 사거리,0,37.42829,127.14474, +449,하대원파출소 사거리,0,37.42826,127.14182,1 +450,성남동현대아파트 삼거리,0,37.4291,127.14003, +451,중원구청 사거리,0,37.42972,127.13715,1 +452,신광교회 단일로,0,37.42913,127.13533, +453,월드카센타 앞,0,37.42892,127.13326, +454,천주교성당 앞,0,37.4291,127.13151, +455,금광중 후문,0,37.44749,127.17104, +456,하원초교 삼거리,0,37.44719,127.16961, +457,신구대 사거리,0,37.44671,127.16731, +458,금광2동사무소 단일로,0,37.44611,127.1645, +459,금광1동사무소 사거리,0,37.44527,127.16184, +460,단대쇼핑뒤 단일로,0,37.44462,127.16, +461,해오름 사거리,0,37.44397,127.15791, +462,중앙동사무소 삼거리,0,37.44307,127.15574, +463,중앙동 사거리,0,37.44178,127.15285, +464,성남공판장 단일로,0,37.44064,127.15033, +465,제일초교 단일로,0,37.43958,127.14788, +466,성호시장 사거리,0,37.43845,127.14535, +467,성일여중고 사거리,0,37.43666,127.14201, +468,성일여고 삼거리,0,37.43602,127.14089, +469,성호지구대 단일로,0,37.4354,127.13976, +470,운동장후문 삼거리,0,37.43462,127.13811, +471,운동장 사거리,0,37.434,127.13504,0 +472,삼영전자 앞,0,37.44397,127.15491, +473,세이브존 단일로,0,37.4432,127.15333, +474,우리은행 사거리,0,37.44263,127.15218,1 +475,성남우체국,0,37.4418,127.1501, +476,신흥 사거리,0,37.43962,127.14455, +477,수진역 사거리,0,37.43762,127.14097,1 +478,모란 사거리,0,37.43358,127.12892,1 +479,둔전교회 앞,0,37.43342,127.12747, +480,고속화도로 출구,0,37.43248,127.11988, +481,탄천 삼거리,0,37.43242,127.11894, +482,동양공업사 사거리,0,37.4339,127.17895, +483,보통골 동성교통앞,0,37.43533,127.17884, +484,근로복지관 삼거리,0,37.43794,127.17861,1 +485,궁전아파트 삼거리,0,37.44013,127.17895, +486,궁전아파트 사거리,0,37.44134,127.17911, +487,파리크라상 사거리,0,37.43504,127.17031, +488,구공단파출소 오거리,0,37.43776,127.17374,1 +489,순흥물산 삼거리,0,37.43983,127.17611, +490,자동차검사소 삼거리,0,37.44189,127.17765, +491,중원경찰서 사거리,0,37.44232,127.16868, +492,금광1파출소 사거리,0,37.44514,127.16826, +493,신구대서문 삼거리,0,37.44873,127.16661, +494,금광중 단일로,0,37.44616,127.17079, +495,금광 삼거리,0,37.44793,127.17307, +496,래미안 삼거리,0,37.4502,127.17436, +497,금상초교 단일로,0,37.44253,127.16612, +498,금광주유소 단일로,0,37.4418,127.16417, +499,학년쇼핑 단일로,0,37.44036,127.1625, +500,단남아파트 단일로,0,37.44215,127.15962, +501,해오름주차장 사거리,0,37.44117,127.15994, +502,상대원고개 단일로,0,37.4398,127.15937, +503,상대원시장 삼거리,0,37.43639,127.15814, +504,상대원고개 농협단일로,0,37.43542,127.15812, +505,대원터널 앞,0,37.43257,127.16034, +506,도촌동입구 삼거리,0,37.41725,127.14993, +507,도촌동입구 사거리,0,37.41547,127.14781, +508,신지교회 단일로,0,37.43703,127.1463, +509,성일고 삼거리,0,37.43563,127.14727, +510,성남서고 삼거리,0,37.43471,127.1479, +511,하대원현대아파트 삼거리,0,37.43243,127.1507, +512,대하초교 단일로,0,37.43112,127.1512, +513,은행동순환로밑 삼거리,0,37.45313,127.17252, +514,중부초교 사거리,0,37.45086,127.16588, +515,은행동현대아파트 삼거리,0,37.45029,127.16312, +516,중앙병원 단일로,0,37.45362,127.16194, +517,해성주유소 사거리,0,37.42827,127.13787, +518,여수지하차도위 사거리,0,37.42154,127.12883,1 +519,모란시장 사거리,0,37.42981,127.1294,1 +520,모란 삼거리,0,37.43281,127.12903, +521,모란견인소 삼거리,0,37.43013,127.12016, +522,탄천로변 삼거리,0,37.43038,127.11889, +523,공단주유소 오거리,0,37.43461,127.17396, +524,우림라이온스밸리 사거리,0,37.43354,127.17112, +525,에이스경로당 사거리,0,37.44128,127.17143, +526,에스콰이어교회 앞,0,37.43963,127.17243, +527,중원보건소 삼거리,0,37.44173,127.17027, +528,갈현 삼거리,0,37.42247,127.17061, +529,중앙초교 앞,0,37.43529,127.14318, +530,여울목 삼거리,0,37.42021,127.15724, +531,도촌동 1단지앞,0,37.41485,127.14834, +532,도촌남로 사거리,0,37.41422,127.15018, +533,도촌프라자 단일로,0,37.41443,127.15194, +534,도촌중입구 사거리,0,37.41448,127.15508, +535,도촌초교 단일로,0,37.414,127.15724,0 +536,도촌초교 삼거리,0,37.41361,127.15836, +537,도촌초교 정문,0,37.41417,127.15877, +538,도촌 수산횟집 앞,0,37.41575,127.15064, +539,도촌북로 사거리,0,37.41672,127.15094, +540,도촌우체국 삼거리,0,37.41709,127.15306, +541,도촌파출소 사거리,0,37.41737,127.15531, +542,도촌종합복지관삼거리,0,37.41518,127.15948, +543,901동앞 삼거리,0,37.41253,127.1634, +544,영생사업소 입구,0,37.4198,127.18303, +545,추모원 주차장입구,0,37.42085,127.1827, +546,장례식장 주차장입구,0,37.42182,127.18233, +547,마지로 신호등 1,0,37.42831,127.13139, +548,마지로 신호등 2,0,37.42817,127.13242, +549,마지로 신호등 3,0,37.4282,127.1337, +550,마지로 신호등 4,0,37.42832,127.13591, +551,보육시설 삼거리,0,37.44251,127.15655, +552,힐스테이 삼거리,0,37.43575,127.14989, +553,시청광장 삼거리,0,37.41879,127.12715, +554,시청정문 사거리,0,37.41879,127.12569, +555,시청공원 삼거리,0,37.41759,127.12456, +556,센트럴타운 사거리,0,37.41878,127.13063, +557,여수초교 삼거리,0,37.41877,127.13241, +558,여수공원 삼거리,0,37.41877,127.13394, +559,연꽃마을4단지 사거리,0,37.41866,127.13547, +560,옥토교회 사거리,0,37.4179,127.13735, +561,공영주차장 단일로,0,37.45737,127.16553, +562,은행2동주민센타 삼거리,0,37.45675,127.16663, +563,이원빌라 사거리,0,37.45613,127.16775, +564,두산아파트 사거리,0,37.45455,127.16387, +565,상원초교 입구,0,37.45477,127.16258, +566,모란 LH 1단지 정문,0,37.43075,127.12607, +567,한신병원앞,0,37.4332,127.131,0 +568,모란시장 삼거리,0,37.42996,127.12798, +569,모란시장 단일로,0,37.42983,127.12709, +570,모란주차장사거리,0,37.42969,127.12613, +571,모란공원 단일로,0,37.42959,127.12452, +572,도촌중 단일로,0,37.41672,127.15526, +573,청진동 수산횟집앞,0,37.43441,127.15874, +574,지식산업센타 사거리,0,37.44031,127.17424, +575,금광지구 8번게이트,0,37.44289,127.16712, +576,오복슈퍼앞,0,37.45014,127.16613, +577,상대원초교 정문,0,37.44278,127.18114, +578,도촌스위첸앞,0,37.41454,127.1539, +579,도촌9단지 정문,0,37.41194,127.16294, +580,한양수자인,0,37.44769,127.17192, +581,스타타워 사거리,0,37.43436,127.17666, +582,슈페리어 단일로,0,37.43333,127.17897, +583,상대원 터널,0,37.42791,127.17951, +584,상대원 터널 우회전전용,0,37.42811,127.17991, +585,호반아파트 후문,0,37.42895,127.09436, +586,호반아파트 정문,0,37.42999,127.09621, +587,고등마을,0,37.43036,127.09896, +588,고등지구대,0,37.43022,127.10068, +589,청구아파트,0,37.44327,127.15089, +590,두산아파트,0,37.44398,127.14961,0 +591,한신아파트 후문,0,37.445,127.15082, +592,희망대사거리,0,37.44482,127.14799,0 +593,희망대초교,0,37.44662,127.14941, +594,구)신흥시장길,0,37.44559,127.14657, +595,태평제일교회,0,37.44781,127.1439, +596,신흥성당,0,37.44871,127.14306, +597,신흥초교,0,37.44954,127.14355, +598,성남여중,0,37.44937,127.14247, +599,가일빌딩(나나헤어),0,37.44446,127.1287, +600,태평초교,0,37.44535,127.13168, +601,태평3동 행정복지센터,0,37.4456,127.13256, +602,통보2차 아파트,0,37.44664,127.13612, +603,영락교회,0,37.44699,127.13723, +604,금빛초교 정문,0,37.4475,127.13897, +605,금빛초교 후문,0,37.44798,127.14062, +606,논골도서관,0,37.45625,127.16019, +607,양지아파트,0,37.45698,127.15943, +608,단대제1공영주차장,0,37.45872,127.15853, +609,문원중학교,0,37.46111,127.15798, +610,성보경영고,0,37.4624,127.15797, +611,푸르지오 입구,0,37.45104,127.15885, +612,푸르지오 정문,0,37.45134,127.15761, +613,진로아파트,0,37.45159,127.15681, +614,단대초교,0,37.45295,127.15661, +615,단대지구대,0,37.45252,127.15812, +616,구)목넴기,0,37.40916,127.09803, +617,남산마을,0,37.40883,127.09725, +618,구)금토슈퍼,0,37.40868,127.09621, +619,창조벨리,0,37.40863,127.09557, +620,판교임시IC,0,37.40856,127.09491, +621,금토1통,0,37.41722,127.08102, +622,금토동 삼거리,0,37.41092,127.09947, +623,창조벨리 삼거리,0,37.41221,127.09973, +624,세종연구소 삼거리,0,37.41631,127.1016, +625,시흥사거리,0,37.42232,127.10205, +626,고등소방서,0,37.42354,127.10215, +627,구) 고등주민센터,0,37.42446,127.1022, +628,사송동 차고지,0,37.41539,127.11476, +629,수자원공사입구,0,37.41451,127.11349, +630,사송교차로,0,37.41172,127.11403, +631,단대오거리,0,37.44507,127.15681,1 +632,구)단대동주민센터,0,37.44816,127.15835, +633,금광시장,0,37.45086,127.15952, +634,법원,0,37.452,127.16004, +635,중앙병원,0,37.45413,127.16098,1 +636,상원초교,0,37.4552,127.1618, +637,은행지구대,0,37.45707,127.16339, +638,은행시장,0,37.45804,127.16423, +639,을지대 정문,0,37.45986,127.16577, +640,을지대 후문,0,37.46148,127.16751, +641,양지지구대,0,37.46266,127.16916, +642,남한산성 유원지,0,37.46355,127.17029, +643,모란고개,0,37.43654,127.12838, +644,태평역,0,37.43974,127.12775,1 +645,수정경찰서,0,37.44181,127.12739, +646,태평고개,0,37.44395,127.12701, +647,구)성남병원,0,37.44794,127.12673,1 +648,가천대,0,37.45057,127.12662, +649,복정초소,0,37.45352,127.12652, +650,복정지구대,0,37.45628,127.12629, +651,동서울대,0,37.45976,127.12585,1 +652,복정초교,0,37.46185,127.12944, +653,국제학교,0,37.46545,127.12629,1 +654,삼정그린뷰아파트,0,37.43999,127.12145, +655,삼부아파트 후문,0,37.43952,127.12436, +656,삼부아파트,0,37.4395,127.12589, +657,곽생로,0,37.44049,127.13028, +658,중앙시장,0,37.44093,127.13181, +659,한국관,0,37.44144,127.13346, +660,탄리사거리,0,37.44221,127.13613, +661,구)시청,0,37.44309,127.13906, +662,의료원 장례식장,0,37.44385,127.13869, +663,의료원 정문,0,37.44407,127.1385, +664,외환은행,0,37.44356,127.14065, +665,숯골사거리,0,37.44409,127.14248,1 +666,성남초교(단),0,37.44494,127.14405, +667,성남초교(4),0,37.44636,127.14541,1 +668,구)청직이약국,0,37.44811,127.14531, +669,신흥2동 주민센터,0,37.4491,127.14584, +670,수정구청,0,37.44962,127.14624, +671,포레스티아 서문,0,37.45063,127.14702, +672,포레스티아 중문,0,37.45227,127.14934,1 +673,포레스티아 동문 삼거리,0,37.45406,127.14971, +674,산성역,0,37.45642,127.15006,1 +675,폴리텍,0,37.45869,127.15196, +676,변전소,0,37.46225,127.15521, +677,청산골,0,37.4409,127.14395, +678,구)시청고개,0,37.44208,127.14342, +679,왕남초교 주차장 삼거리,0,37.42777,127.09895, +680,왕남초교 정문,0,37.42786,127.09827, +681,여수대교 사거리,0,37.42205,127.11668,1 +682,시흥동주민센터 삼거리,0,37.42241,127.10856, +683,농협물류창고 삼거리,0,37.42238,127.10527, +684,수성교,0,37.43512,127.13449, +685,수진소방서,0,37.43649,127.13384, +686,Yes 할인마트,0,37.43769,127.1333, +687,공주보일러,0,37.43896,127.1327, +688,태평로약국,0,37.44238,127.13113, +689,서광모터스,0,37.44308,127.1308, +690,하나님의교회,0,37.44411,127.13033, +691,한전사거리,0,37.44483,127.12997, +692,태평중학교,0,37.4462,127.12936, +693,태평3동 공영주차장,0,37.44682,127.12906, +694,LH 기업성장센터 사거리,0,37.41081,127.09487, +695,디어스빌딩 앞 삼거리,0,37.41256,127.09887, +696,경기 기업성장센터 삼거리,0,37.41209,127.09561, +697,판교 기업성장센터 사거리,0,37.41134,127.09495, +698,풍경채 입구,0,37.42564,127.10041, +699,토끼굴,0,37.4261,127.09714, +700,아이비빌딩,0,37.42713,127.09501, +701,정혜사,0,37.42803,127.09346, +702,고등IC,0,37.42956,127.09079, +703,적푸리마을,0,37.43361,127.08478, +704,신구대식물원,0,37.43214,127.08167, +705,청계산주유소,0,37.43071,127.07334, +706,청계산장,0,37.43052,127.07174, +707,탄천교차로,0,37.41867,127.11755, +708,선사교회,0,37.42024,127.11703, +709,둔전교,0,37.42736,127.11654, +710,비닐하우스,0,37.4302,127.1202, +711,탄천주차장 정문,0,37.4422,127.11968, +712,탄천주차장 후문(탄천한우),0,37.44346,127.11971, +713,탄천대교 사거리,0,37.45775,127.11952, +714,경원진지,0,37.45186,127.12105, +715,능골공원,0,37.4491,127.12044, +716,수질복원센터 후문,0,37.4589,127.12104, +717,수질복원센터 정문,0,37.46024,127.12446, +718,수진동행정복지센터,0,37.43869,127.13921, +719,성체유치원,0,37.43916,127.13776, +720,은서유치원,0,37.44357,127.1355, +721,순온천사우나,0,37.44439,127.13511, +722,태평오거리,0,37.4461,127.1343, +723,쌍용아파트,0,37.44797,127.12775, +724,동부센트레빌,0,37.44804,127.12852, +725,성남서초,0,37.44834,127.12956, +726,두산위브,0,37.44966,127.13401, +727,위례부영아파트,0,37.46057,127.14543, +728,위례소방서,0,37.46161,127.14359, +729,미도아파트,0,37.44647,127.15542, +730,세무서,0,37.44686,127.15503, +731,청소년수련원,0,37.44948,127.15321, +732,혜은학교,0,37.44995,127.15261, +733,현대자동차공업사,0,37.45039,127.152, +734,북초교,0,37.45086,127.15132, +735,양지근린공원 임시 단일로,0,37.4629,127.16197,0 +736,풍경채 정문 사거리,0,37.42739,127.1007, +737,왕남초교 삼거리,0,37.42816,127.10089, +738,5171부대 삼거리,0,37.42055,127.10217, +739,구)고등동 삼거리,0,37.42564,127.10233, +740,구)고등사거리,0,37.42807,127.10193, +741,대왕주유소 삼거리,0,37.42995,127.10205, +742,등자리마을 삼거리,0,37.43498,127.10356, +743,한성대 삼거리,0,37.43258,127.10311, +744,제2정문 삼거리,0,37.43848,127.10371, +745,우정아파트 삼거리,0,37.44464,127.10373, +746,공군아파트 삼거리,0,37.44611,127.10401, +747,서울공항 정문 사거리,0,37.44851,127.10497, +748,효성고 단일로,0,37.44952,127.10542, +749,오야실마을 삼거리,0,37.45491,127.10686, +750,미군부대 삼거리,0,37.45789,127.10736, +751,신촌동 삼거리,0,37.45994,127.10763, +752,성남서중,0,37.44775,127.1542, +753,램프구간 하부,0,37.45344,127.17281, +754,창곡사거리,0,37.46316,127.14072,1 diff --git a/Data/tables/20240207_221605/S_INT_PHASE_CONFIG.csv b/Data/tables/20240207_221605/S_INT_PHASE_CONFIG.csv new file mode 100644 index 000000000..a79927e5d --- /dev/null +++ b/Data/tables/20240207_221605/S_INT_PHASE_CONFIG.csv @@ -0,0 +1,3909 @@ +INT_NO,INT_PHASE_NO,INT_PLAN_CLSS,INT_RING,INT_FLOW_NO,INT_MIN_SPLIT,INT_MAX_SPLIT,INT_YELLOW,INT_MAIN_PHASE,INT_LANE,INT_PHASE_LAT1,INT_PHASE_LNG1,INT_PHASE_LAT2,INT_PHASE_LNG2,INT_PHASE_LAT3,INT_PHASE_LNG3 +204,1,0,0,8,0,0,0,0,0,37.36581,127.10822,37.36614,127.10822,37.36626,127.10822 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08:04:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:03:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:02:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:01:00,0,0,0,0,0,0,,,0,0 +754,2023-10-18 08:00:00,0,0,0,0,0,0,,,0,0 diff --git a/Data/tables/view_tables.ipynb b/Data/tables/view_tables.ipynb index 3c8a611a9..89f4c71c5 100644 --- a/Data/tables/view_tables.ipynb +++ b/Data/tables/view_tables.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -43,9 +43,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "move_1704405400.csv\n" + ] + } + ], "source": [ "loading_dtype = {\n", " 'inter_no':'int', 'start_hour':'int', 'start_minute':'int', 'cycle':'int','offset':'int',\n", @@ -57,12 +65,13 @@ "for alph in ['A', 'B']:\n", " for j in range(1,9):\n", " loading_dtype[f'angle_{alph}{j}'] = 'str'\n", - " loading_dtype[f'dura_{alph}{j}'] = 'int'\n" + " loading_dtype[f'dura_{alph}{j}'] = 'int'\n", + "print(os.listdir('move')[5000])" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -86,229 +95,328 @@ " \n", " \n", " \n", - " Unnamed: 0\n", " inter_no\n", - " angle_A1\n", - " angle_A2\n", - " angle_A3\n", - " angle_A4\n", - " angle_A5\n", - " angle_A6\n", - " angle_A7\n", - " angle_A8\n", - " angle_B1\n", - " angle_B2\n", - " angle_B3\n", - " angle_B4\n", - " angle_B5\n", - " angle_B6\n", - " angle_B7\n", - " angle_B8\n", + " end_unix\n", + " dura_A1\n", + " dura_A2\n", + " dura_A3\n", + " dura_A4\n", + " dura_A5\n", + " dura_A6\n", + " dura_A7\n", + " dura_A8\n", + " dura_B1\n", + " dura_B2\n", + " dura_B3\n", + " dura_B4\n", + " dura_B5\n", + " dura_B6\n", + " dura_B7\n", + " dura_B8\n", + " cycle\n", + " offset\n", " \n", " \n", " \n", " \n", " 0\n", + " 206\n", + " 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move_noinc_dirout_dir
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"29 0 0 0 0 170 131 \n", - "30 0 0 0 0 180 137 \n", - "31 0 0 0 0 190 143 \n", - "\n", - "[32 rows x 22 columns]" + " move_no inc_dir out_dir\n", + "0 1 동 남\n", + "1 2 서 동\n", + "2 3 남 서\n", + "3 4 북 남\n", + "4 5 서 북\n", + "5 6 동 서\n", + "6 7 북 동\n", + "7 8 남 북\n", + "8 9 북동 남동\n", + "9 10 남서 북동\n", + "10 11 남동 남서\n", + "11 12 북서 남동\n", + "12 13 남서 북서\n", + "13 14 북동 남서\n", + "14 15 북서 북동\n", + "15 16 남동 북서\n", + "16 17 NaN NaN\n", + "17 18 NaN NaN\n", + "18 21 NaN NaN" ] }, "metadata": {}, @@ -2295,23 +2360,169 @@ } ], "source": [ - "angle = pd.read_csv('angle.csv', dtype=loading_dtype)\n", + "history = pd.read_csv('history.csv', dtype=loading_dtype, index_col=0)\n", + "plan = pd.read_csv('plan.csv', dtype=loading_dtype, index_col=0)\n", + "\n", + "angle = pd.read_csv('angle.csv', dtype=loading_dtype, index_col=0)\n", + "move = pd.read_csv(os.path.join('move', 'move_1704405400.csv'), dtype=loading_dtype, index_col=0)\n", + "\n", "child_coord = pd.read_csv('child_coord.csv', dtype=loading_dtype)\n", "child_uturn = pd.read_csv('child_uturn.csv', dtype=loading_dtype)\n", - "history = pd.read_csv('history.csv', dtype=loading_dtype)\n", - "inter_info = pd.read_csv('inter_info.csv', dtype=loading_dtype)\n", - "inter_node = pd.read_csv('inter_node.csv', dtype=loading_dtype)\n", + "inter_info = pd.read_csv('inter_info.csv', dtype=loading_dtype, index_col=0)\n", + "inter_node = pd.read_csv('inter_node.csv', dtype=loading_dtype, index_col=0)\n", "nema = pd.read_csv('nema.csv', dtype=loading_dtype, encoding='cp949')\n", - "plan = pd.read_csv('plan.csv', dtype=loading_dtype)\n", - "display(angle)\n", - "display(child_coord)\n", - "display(child_uturn)\n", + "\n", "display(history)\n", + "display(plan)\n", + "\n", + "display(angle)\n", + "display(move)\n", + "\n", "display(inter_info)\n", "display(inter_node)\n", - "display(nema)\n", - "display(plan)" + "display(child_uturn)\n", + "display(child_coord)\n", + "display(nema)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " a b c d\n", + "0 2 11 pass vector\n", + "1 4 22 pass matrix\n", + "2 6 33 continue tensor\n", + "3 2 11 pass vector\n", + "4 4 22 continue matrix\n", + "5 6 33 pass tensor" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "dtype: bool\n", + "False\n", + "0 True\n", + "1 False\n", + "2 False\n", + "3 True\n", + "4 False\n", + "5 False\n", + "dtype: bool\n", + "True\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.DataFrame({'a':[2,4,6]*2, 'b':[11, 22, 33]*2, 'c':['pass']*2 + ['continue', 'pass']*2, 'd':['vector', 'matrix', 'tensor']*2})\n", + "display(df)\n", + "print(((df.a == 2) & (df.c == 'continue')))\n", + "print(((df.a == 2) & (df.c == 'continue')).any())\n", + "print(((df.a == 2) & (df.c == 'pass')))\n", + "print(((df.a == 2) & (df.c == 'pass')).any())" ] + }, + { + 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zV26;gyWt~O#Iv%<@KrGWsz(xKan(t?$A&ss>qiJSi($WeAAS~N7)L7)7Kf_QYD{b# zB3h{V;}|$PdC)&qb!b95x}_~z7~nA4LfFWXBl}DfuM{`1d>`4hA_y{vEo2T+cxYKQ z|Iwwafk$Ep#?w5*B*>?I2JvwwyM@j;99Rw&O4$?-_mX4a%>RbK!WgFlGK6f(Pi;xP zeD+A9Ex3%G#$;$eY`R+sW$mQ^W7lK|yX;Wd;JI+NPXMXlu`4T(Sd%Z}Bxjmn32S>W z$R2-@Ht{8haNbx?lG3hiE9thtCMlt}?s3EWb;!*g8hFjhj z-L;Skl-TC4(~!<>2q_#i{AB{{69tx}PEVI(SOjr89C==sqtN0&0R}M8!#I!y$F40G z-Fx?Va{%J&xLHDq38u&=e38KpRO~5b$nJk2M+7A&DmNi#oF_{4^SJ{+!f|S=Aja-; zf%lUi^Q+bGrR0|Uii!P{ZN|`8^Emo`$A)_#NPQAXI$FB&DcqZT0~&80r%K1Q{rZIy zIDbe)#OHp>oZW08s2?N%Pc8TgkbUpWSe#QjnpCIcw9QXL$ifs_Sk6piPntO4%00S? z=Q7}UCRJ{;i~V^Jk+MKmpD5&?RNd?|6DK3zy3s$){^oqoegoE0Z(ojrk$wBYEFIpHAmSI_v1{q*I_%q$9;U zLf=btj?y_s=VdyyNHmYr;f!r^{HVJ0vO?10O-E*SPKlfvt=r&W79L0@ld self.present_time - 9000)] # 두 시간 반 전부터 현재까지의 신호이력을 가져옴. 9000 = 3600 * 2.5 + self.rhistory = self.rhistory[(self.rhistory.end_unix <= self.present_time) & (self.rhistory.end_unix > self.present_time - self.substractor)] # 두 시간 반 전부터 현재까지의 신호이력을 가져옴. 9000 = 3600 * 2.5 # rhistory에 모든 교차로번호가 존재하지 않으면 해당 교차로번호에 대한 신호이력을 추가함 (at 최근 프로그램 시작시각) whole_inter_nos = sorted(self.history.inter_no.unique()) recent_inter_nos = sorted(self.rhistory.inter_no.unique()) if not whole_inter_nos==recent_inter_nos: for inter_no in set(whole_inter_nos) - set(recent_inter_nos): - program_start, prow = self.load_prow(inter_no, self.present_time - 9000) + program_start, prow = self.load_prow(inter_no, self.present_time - self.substractor) cycle = prow.cycle.iloc[0] row1 = prow.drop(['start_hour', 'start_minute'], axis=1).copy() row2 = prow.drop(['start_hour', 'start_minute'], axis=1).copy() @@ -261,8 +267,8 @@ class SignalGenerator(): def make_rhists(self): self.rhists = [] for inter_no in sorted(self.rhistory.inter_no.unique()): - self.rhist = self.rhistory.copy()[self.rhistory.inter_no==inter_no] - self.rhist = self.rhist.drop_duplicates(subset=['start_unix']).reset_index(drop=True) + filtered_rhist = self.rhistory[self.rhistory.inter_no == inter_no].drop_duplicates(subset=['start_unix']).reset_index(drop=True) + self.rhist = filtered_rhist # D_n 및 S_n 값 정의 self.rhist['D_n'] = 0 # D_n : 시간차이 @@ -343,35 +349,28 @@ class SignalGenerator(): prev_unix = curr_unix curr_unix = self.rhist.loc[n+1, 'start_unix'] - # # 생략해도 무방할 코드 - # self.rhist = self.rhist.reset_index(drop=True) - # self.rhist = self.rhist.sort_values(by=['start_unix']) - - # # D_n 및 S_n 값 재정의 - # for n in range(len(self.rhist)): - # curr_unix = self.rhist.iloc[n].start_unix # current start_unix - # self.rhist.loc[n, ['D_n', 'S_n']] = self.calculate_DS(self.rhist, curr_unix) self.rhists.append(self.rhist) self.rhists = pd.concat(self.rhists).sort_values(by=['start_unix','inter_no']) - self.rhists = self.rhists[self.rhists.start_unix >= self.present_time - 3600] + self.rhists = self.rhists[self.rhists.start_unix >= self.present_time - 1200] self.rhists = self.rhists.drop(columns=['D_n', 'S_n']) def calculate_DS(self, rhist, curr_unix): program_starts = np.array(self.timetable.start_seconds) idx = (program_starts <= self.present_time).sum() - 1 program_start = program_starts[idx] - if list(self.hours[self.hours <= curr_unix]): - ghour_lt_curr_unix = self.hours[self.hours <= curr_unix].max() # the greatest hour less than or equal to curr_unix - else: - ghour_lt_curr_unix = program_start - start_unixes = rhist.start_unix.unique() - start_unixes_lt_ghour = np.sort(start_unixes[start_unixes < ghour_lt_curr_unix]) # start unixes less than ghour_lt_curr_unix - # 기준유닉스(base_unix) : curr_unix보다 작은 hour 중에서 가장 큰 값으로부터 다섯 번째로 작은 start_unix - if len(start_unixes_lt_ghour) > 5: - base_unix = start_unixes_lt_ghour[-5] - # start_unixes_lt_ghour의 길이가 5 미만일 경우에는 맨 앞 start_unix로 base_unix를 지정 - else: - base_unix = rhist.start_unix.min() + # if list(self.hours[self.hours <= curr_unix]): + # ghour_lt_curr_unix = self.hours[self.hours <= curr_unix].max() # the greatest hour less than or equal to curr_unix + # else: + # ghour_lt_curr_unix = program_start + # start_unixes = rhist.start_unix.unique() + # start_unixes_lt_ghour = np.sort(start_unixes[start_unixes < ghour_lt_curr_unix]) # start unixes less than ghour_lt_curr_unix + # # 기준유닉스(base_unix) : curr_unix보다 작은 hour 중에서 가장 큰 값으로부터 다섯 번째로 작은 start_unix + # if len(start_unixes_lt_ghour) > 5: + # base_unix = start_unixes_lt_ghour[-5] + # # start_unixes_lt_ghour의 길이가 5 미만일 경우에는 맨 앞 start_unix로 base_unix를 지정 + # else: + # base_unix = rhist.start_unix.min() + base_unix = curr_unix - self.substractor D_n = curr_unix - base_unix S_n_durs = rhist[(rhist.start_unix > base_unix) & (rhist.start_unix <= curr_unix)] \ [[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] @@ -382,7 +381,7 @@ class SignalGenerator(): def make_hrhists(self): # 계층화된 형태로 변환 self.hrhists = [] # hierarchied recent history - for i, row in self.rhists.iterrows(): + for row in self.rhists.itertuples(index=True): inter_no = row.inter_no start_unix = row.start_unix @@ -392,8 +391,10 @@ class SignalGenerator(): self.isplit = self.isplits[(inter_no, start_hour, start_minute)] phas_As = [self.isplit[j][0] for j in self.isplit.keys()] phas_Bs = [self.isplit[j][1] for j in self.isplit.keys()] - durs_A = row[[f'dura_A{j}' for j in range(1,9)]] - durs_B = row[[f'dura_B{j}' for j in range(1,9)]] + # durs_A = row[[f'dura_A{j}' for j in range(1,9)]] + # durs_B = row[[f'dura_B{j}' for j in range(1,9)]] + durs_A = [getattr(row, f'dura_A{j}') for j in range(1, 9)] + durs_B = [getattr(row, f'dura_A{j}') for j in range(1, 9)] durations = [] for j in range(1, len(self.isplit)+1): ja = self.isplit[j][0] @@ -412,7 +413,9 @@ class SignalGenerator(): def process_movement(self): print("3. 이동류정보 테이블을 변환합니다.") self.make_movement() + self.time31 = datetime.now() self.update_movement() + self.time32 = datetime.now() # 3-1. movement def make_movement(self): @@ -492,30 +495,37 @@ class SignalGenerator(): movedur = movedur.sort_values(by=['start_unix', 'inter_no', 'phas_A','phas_B']) movedur = movedur[['inter_no', 'start_unix', 'phas_A', 'phas_B', 'move_A', 'move_B', 'duration']] - # 이동류 매칭 테이블에서 진입id, 진출id를 가져와서 붙임. - for i, row in movedur.iterrows(): + # matching DataFrame에 대해 multi-index 설정 + self.matching.set_index(['inter_no', 'move_no'], inplace=True) + self.matching.sort_index(inplace=True) + + for row in movedur.itertuples(index=True): inter_no = row.inter_no start_unix = row.start_unix - # incoming and outgoing edges A move_A = row.move_A + move_B = row.move_B + + # incoming and outgoing edges A if move_A in [17, 18]: inc_edge_A = np.nan - outhedge_A = np.nan + out_edge_A = np.nan else: - match_A = self.matching[(self.matching.inter_no == inter_no) & (self.matching.move_no == move_A)].iloc[0] - inc_edge_A = match_A.inc_edge - out_edge_A = match_A.out_edge - movedur.loc[i, ['inc_edge_A', 'out_edge_A']] = [inc_edge_A, out_edge_A] + match_A = self.matching.loc[(inter_no, move_A)] + inc_edge_A = match_A.inc_edge.values[0] + out_edge_A = match_A.out_edge.values[0] + movedur.at[row.Index, 'inc_edge_A'] = inc_edge_A + movedur.at[row.Index, 'out_edge_A'] = out_edge_A + # incoming and outgoing edges B - move_B = row.move_B if move_B in [17, 18]: inc_edge_B = np.nan out_edge_B = np.nan else: - match_B = self.matching[(self.matching.inter_no == inter_no) & (self.matching.move_no == move_B)].iloc[0] - inc_edge_B = match_B.inc_edge - out_edge_B = match_B.out_edge - movedur.loc[i, ['inc_edge_B', 'out_edge_B']] = [inc_edge_B, out_edge_B] + match_B = self.matching.loc[(inter_no, move_B)] + inc_edge_B = match_B.inc_edge.values[0] + out_edge_B = match_B.out_edge.values[0] + movedur.at[row.Index, 'inc_edge_B'] = inc_edge_B + movedur.at[row.Index, 'out_edge_B'] = out_edge_B # 이동류 컬럼 제거 movedur = movedur.drop(['move_A', 'move_B'], axis=1) @@ -549,21 +559,21 @@ class SignalGenerator(): for child_id in self.pa2ch[parent_id]: new_histid = self.histid.copy()[self.histid.node_id==parent_id] new_histid[['inc_edge_A', 'out_edge_A', 'inc_edge_B', 'out_edge_B']] = np.nan - for i, row in new_histid.iterrows(): + for row in new_histid.itertuples(index=True): phas_A = row.phas_A phas_B = row.phas_B new_match = self.match6[self.match6.node_id==child_id] Arow = new_match[(new_match.phase_no==phas_A) & (new_match.ring_type=='A')] - if ~ Arow[['inc_edge', 'out_edge']].isna().all().all(): + if not Arow[['inc_edge', 'out_edge']].isna().all().all(): inc_edge = Arow.iloc[0].inc_edge out_edge = Arow.iloc[0].out_edge - new_histid.loc[i, ['inc_edge_A', 'out_edge_A']] = [inc_edge, out_edge] + new_histid.loc[row.Index, ['inc_edge_A', 'out_edge_A']] = [inc_edge, out_edge] Brow = new_match[(new_match.phase_no==phas_B) & (new_match.ring_type=='B')] - if ~ Brow[['inc_edge', 'out_edge']].isna().all().all(): + if not Brow[['inc_edge', 'out_edge']].isna().all().all(): inc_edge = Brow.iloc[0].inc_edge out_edge = Brow.iloc[0].out_edge - new_histid.loc[i, ['inc_edge_B', 'out_edge_B']] = [inc_edge, out_edge] - new_histid.loc[i, 'node_id'] = child_id + new_histid.loc[row.Index, ['inc_edge_B', 'out_edge_B']] = [inc_edge, out_edge] + new_histid.loc[row.Index, 'node_id'] = child_id new_histids.append(new_histid) new_histids = pd.concat(new_histids) self.histids = pd.concat([self.histid.copy(), new_histids]) @@ -657,7 +667,7 @@ class SignalGenerator(): self.sigtable = self.histids.copy() self.sigtable['init_state'] = self.sigtable['node_id'].map(self.node2init) self.sigtable['state'] = self.sigtable['init_state'].map(lambda x:''.join(x)) - for i, row in self.sigtable.iterrows(): + for row in self.sigtable.itertuples(index=True): node_id = row.node_id inc_edge_A = row.inc_edge_A inc_edge_B = row.inc_edge_B @@ -673,7 +683,7 @@ class SignalGenerator(): index = conn.getTLLinkIndex() if index >= 0: state[index] = 'G' - self.sigtable.at[i, 'state'] = ''.join(state) + self.sigtable.at[row.Index, 'state'] = ''.join(state) if pd.isna(inc_edge_B) or pd.isna(out_edge_B): pass @@ -684,7 +694,7 @@ class SignalGenerator(): index = conn.getTLLinkIndex() if index >= 0: state[index] = 'G' - self.sigtable.at[i, 'state'] = ''.join(state) + self.sigtable.at[row.Index, 'state'] = ''.join(state) self.sigtable = self.sigtable.dropna(subset='state') self.sigtable = self.sigtable.reset_index(drop=True) self.sigtable['phase_sumo'] = self.sigtable.groupby(['node_id', 'start_unix']).cumcount() @@ -758,7 +768,7 @@ class SignalGenerator(): strings = ['\n'] for node_id, group in self.SIGTABLE.groupby('node_id'): strings.append(f' \n') - for i, row in group.iterrows(): + for row in group.itertuples(index=True): duration = row.duration state = row.state strings.append(f' \n') @@ -804,11 +814,18 @@ class SignalGenerator(): self.time6 = datetime.now() print('(1)', self.time1 - self.time0) + print('(1-1)', self.time11 - self.time0) + print('(1-2)', self.time12 - self.time11) + print('(1-3)', self.time13 - self.time12) + print('(1-4)', self.time14 - self.time13) + print('(1-5)', self.time15 - self.time14) + print('(2)', self.time2 - self.time1) print('(2-1)', self.time21 - self.time1) print('(2-2)', self.time22 - self.time21) print('(2-3)', self.time23 - self.time22) - print('(2)', self.time2 - self.time1) print('(3)', self.time3 - self.time2) + print('(3-1)', self.time31 - self.time2) + print('(3-2)', self.time32 - self.time31) print('(4)', self.time4 - self.time3) print('(4-1)', self.time41 - self.time3) print('(4-2)', self.time42 - self.time41) @@ -819,4 +836,9 @@ class SignalGenerator(): if __name__ == '__main__': self = SignalGenerator() self.main() + self.path_unit = os.path.join(self.path_root, 'Analysis', '0207_unit_test') + self.hrhists.to_csv(os.path.join(self.path_unit, 'hrhists.csv')) + self.histids.to_csv(os.path.join(self.path_unit, 'histids.csv')) + self.sigtable.to_csv(os.path.join(self.path_unit, 'sigtable.csv')) + self.Sigtable.to_csv(os.path.join(self.path_unit, 'ssigtable.csv')) # print("elapsed time :", datetime.now() - starting_time) \ No newline at end of file diff --git a/Scripts/preprocess_daily.py b/Scripts/preprocess_daily.py index 8932baf4b..9b3a22a5b 100644 --- a/Scripts/preprocess_daily.py +++ b/Scripts/preprocess_daily.py @@ -296,7 +296,7 @@ class DailyPreprocessor(): 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 @@ -452,23 +452,17 @@ class DailyPreprocessor(): 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 + 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] 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

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