simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import itertools\n",
    "import requests\n",
    "import polyline\n",
    "import json\n",
    "import os\n",
    "\n",
    "import random as rd\n",
    "import pandas as pd\n",
    "\n",
    "from shapely.geometry import Point\n",
    "\n",
    "from requests.adapters import HTTPAdapter\n",
    "from requests.packages.urllib3.util.retry import Retry\n",
    "\n",
    "import warnings \n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 좌표 거리 생성 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 직선 거리 게산 함수\n",
    "def calculate_straight_distance(lat1, lon1, lat2, lon2):\n",
    "    # 지구 반경 (킬로미터 단위)\n",
    "    km_constant = 3959* 1.609344\n",
    "    # 위도와 경도를 라디안으로 변환\n",
    "    lat1, lon1, lat2, lon2 = map(np.deg2rad, [lat1, lon1, lat2, lon2])\n",
    "    # 위도 및 경도 차이 계산\n",
    "    dlat = lat2 - lat1 \n",
    "    dlon = lon2 - lon1\n",
    "    # Haversine 공식 계산\n",
    "    a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n",
    "    c = 2 * np.arcsin(np.sqrt(a)) \n",
    "    # 거리 계산 (킬로미터 단위)\n",
    "    km = km_constant * c\n",
    "    \n",
    "    return km"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### trips 데이터 생성 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### osrm 페키지로 경로 추출\n",
    "# 입력으로 받은 출발지와 목적지 좌표를 이용하여 경로 정보를 가져오는 함수\n",
    "def get_res(point):\n",
    "\n",
    "   status = 'defined'\n",
    "\n",
    "   # 요청을 재시도할 수 있도록 세션 객체 생성 및 설정\n",
    "   session = requests.Session()\n",
    "   retry = Retry(connect=3, backoff_factor=0.5)\n",
    "   adapter = HTTPAdapter(max_retries=retry)\n",
    "   session.mount('http://', adapter)\n",
    "   session.mount('https://', adapter)\n",
    "\n",
    "   #### url 생성 코드\n",
    "   # 전체 경로 정보를 요청\n",
    "   overview = '?overview=full'\n",
    "   # lon, lat, lon, lat 형식의 출발지 목적지 좌표\n",
    "   loc = f\"{point[0]},{point[1]};{point[2]},{point[3]}\"\n",
    "   # 보행경로 url\n",
    "   url = 'http://router.project-osrm.org/route/v1/foot/'\n",
    "   # 경로 정보 요청\n",
    "   r = session.get(url + loc + overview) \n",
    "   \n",
    "   # 만약 경로가 안뜰 때 대체 결과 생성\n",
    "   if r.status_code!= 200:\n",
    "      \n",
    "      status = 'undefined'\n",
    "      \n",
    "       # 직선 거리 계산\n",
    "      distance = calculate_straight_distance(point[1], point[0], point[3], point[2]) * 1000\n",
    "      \n",
    "      # 경로 정보 생성 (출발지와 목적지 좌표만 포함)\n",
    "      route = [[point[0], point[1]], [point[2], point[3]]]\n",
    "\n",
    "      # 소요 시간 및 타임스탬프 계산 (가정: 보행 속도 10km/h)\n",
    "      speed_km = 10#km\n",
    "      speed = (speed_km * 1000/60)      \n",
    "      duration = distance/speed\n",
    "      \n",
    "      timestamp = [0, duration]\n",
    "\n",
    "      result = {'route': route, 'timestamp': timestamp, 'duration': duration, 'distance' : distance}\n",
    "   \n",
    "      return result, status\n",
    "   \n",
    "   # 경로 정보를 성공적으로 가져온 경우, JSON 응답을 반환\n",
    "   res = r.json()   \n",
    "   return res, status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 경로를 가는데 걸리는 시간과 거리 추출 함수\n",
    "def extract_duration_distance(res):\n",
    "   # get_res함수에서 추출된 데이터에서 시간과 거리 뽑기\n",
    "   \n",
    "   duration = res['routes'][0]['duration']/(60)  # 분 단위로 변환\n",
    "   distance = res['routes'][0]['distance']\n",
    "   \n",
    "   return duration, distance\n",
    "\n",
    "# 경로 추출 함수\n",
    "def extract_route(res):\n",
    "   \n",
    "    # get_res함수에서 추출된 데이터에서 경로 뽑기\n",
    "    # 경로가 인코딩 되어 있기 때문에 아래 함수를 써서 디코딩해주어야지 위경도로 이루어진 경로가 나옴\n",
    "    route = polyline.decode(res['routes'][0]['geometry'])\n",
    "    \n",
    "    # 사용할 형식에 맞춰 위경도 좌표의 위치를 바꿔주는 것\n",
    "    route = list(map(lambda data: [data[1],data[0]] ,route))\n",
    "    \n",
    "    return route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 총 걸리는 시간을 경로의 거리 기준으로 쪼개주는 함수\n",
    "def extract_timestamp(route, duration):\n",
    "    \n",
    "    # 리스트를 numpy이 배열로 변경\n",
    "    rt = np.array(route)\n",
    "    # 리스트를 수평 기준으로 합치기\n",
    "    rt = np.hstack([rt[:-1,:], rt[1:,:]])\n",
    "    # 각각 직선거리 추출(리스트 형태)\n",
    "    per = calculate_straight_distance(rt[:,1], rt[:,0], rt[:,3], rt[:,2])\n",
    "    # 각각의 직선거리를 전체 직선거리의 합으로 나누기\n",
    "    per = per / np.sum(per)\n",
    "\n",
    "    # 계산된 비율을 기반으로 각 지점 도착 예상 시간 계산\n",
    "    timestamp = per * duration\n",
    "    timestamp = np.hstack([np.array([0]),timestamp])\n",
    "    timestamp = list(itertools.accumulate(timestamp)) \n",
    "    \n",
    "    return timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모든 함수를 한번에 실행하는 코드(trips 데이터의 형태로 저장)\n",
    "def osrm_routing_machine(O, D):\n",
    "\n",
    "   # osrm 데이터 생성\n",
    "   osrm_base, status = get_res([O.x, O.y, D.x, D.y])\n",
    "   \n",
    "   # osrm 데이터가 생성 됬으면 진행\n",
    "   if status == 'defined':\n",
    "      # 거리 및 걸리는 시간 추출\n",
    "      duration, distance = extract_duration_distance(osrm_base)\n",
    "      # 경로 추출\n",
    "      route = extract_route(osrm_base)\n",
    "      # timestamp 생성\n",
    "      timestamp = extract_timestamp(route, duration)\n",
    "      # 결과 저장\n",
    "      result = {'route': route, 'timestamp': timestamp, 'duration': duration, 'distance' : distance}\n",
    "      \n",
    "      return result\n",
    "   else: \n",
    "      return osrm_base\n",
    "# OD_data 한쌍일 때 osrm_routing_machine작동함수\n",
    "def osrm_routing_machine_multiprocess(OD):\n",
    "   O, D = OD\n",
    "   result = osrm_routing_machine(O, D)\n",
    "   return result\n",
    "# OD_data 데이터가 리스트쌍 일때의 osrm_routing_machine 작동함수\n",
    "def osrm_routing_machine_multiprocess_all(OD_data):\n",
    "    results = list(map(osrm_routing_machine_multiprocess, OD_data))\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### od 데이터 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 랜덤한 쌍의 od 데이터(시작점과 도착점이 같이 않게 뜸)\n",
    "def get_OD_data(point, num = 10) :\n",
    "    OD_data = []\n",
    "\n",
    "    # 10개의 랜덤쌍 좌표 생성\n",
    "    for _ in range(num):\n",
    "        # 포인트 좌표의 key값을 이용하여 랜덤 쌍 생성\n",
    "        neighborhood1, neighborhood2 = rd.sample(point.keys(), 2)\n",
    "        # 랜덤쌍의 첫번째 값을 시작점으로 두번째 값을 도착점으로 설정\n",
    "        start_point = point[neighborhood1]\n",
    "        end_point = point[neighborhood2]\n",
    "        \n",
    "        # 시작점과 끝점을 포인트 좌표로 변경 \n",
    "        O = Point(start_point)\n",
    "        D = Point(end_point)\n",
    "        # 시작점과 출발점을 리스트로 만들어 리스트에 추가\n",
    "        OD_data.append([O, D])\n",
    "    \n",
    "    return OD_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 좌표\n",
    "point = {\n",
    "    \"가천대역\" : [127.126858, 37.449621],\n",
    "    \"가천대_반도체대학\" : [127.127384 , 37.450910],\n",
    "    \"가천대_일반대학원\" : [127.130112 , 37.452589],\n",
    "    \"가천대_교육대학원\" : [127.131698 , 37.452066],\n",
    "    \"가천대_학생회관\" : [127.134042 , 37.453336],\n",
    "    \"가천대_ai_공학관\" : [127.133374 , 37.455009],\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[<POINT (127.132 37.452)>, <POINT (127.134 37.453)>],\n",
       " [<POINT (127.13 37.453)>, <POINT (127.134 37.453)>],\n",
       " [<POINT (127.133 37.455)>, <POINT (127.134 37.453)>],\n",
       " [<POINT (127.127 37.451)>, <POINT (127.134 37.453)>],\n",
       " [<POINT (127.132 37.452)>, <POINT (127.133 37.455)>],\n",
       " [<POINT (127.127 37.45)>, <POINT (127.133 37.455)>],\n",
       " [<POINT (127.127 37.451)>, <POINT (127.127 37.45)>],\n",
       " [<POINT (127.127 37.45)>, <POINT (127.127 37.451)>],\n",
       " [<POINT (127.133 37.455)>, <POINT (127.134 37.453)>],\n",
       " [<POINT (127.127 37.45)>, <POINT (127.13 37.453)>]]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# O는 출발지 D는 도착지로 생각하면 편함\n",
    "OD_data = get_OD_data(point, 10)\n",
    "OD_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### trips 데이터 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OD, DO 포인트에 대해서 각각의 trips데이터를 생성\n",
    "OD_results = osrm_routing_machine_multiprocess_all(OD_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### timestamp 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 6, 14, 18, 20, 29, 33, 35, 41, 45]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 원하는 범위에서 랜덤함 숫자를 원하는 만큼 뽑아내는 함수\n",
    "def sample_interval(start, end, count, num_samples):\n",
    "    # 시작과 끝을 count만 큼 나눔(최종 : 나눈 만큼의 숫자가 생성됨)\n",
    "    interval_size = (end - start) / count\n",
    "    samples = []\n",
    "    # 랜덤 숫자 생성\n",
    "    for i in range(count):\n",
    "        interval_start = start + interval_size * i\n",
    "        interval_end = interval_start + interval_size\n",
    "        samples.extend(rd.sample(range(int(interval_start), int(interval_end)), num_samples))\n",
    "    return samples\n",
    "\n",
    "### ex.\n",
    "# 시작 시간과 출발 시간 사이의 랜덤 숫자 생성\n",
    "# 초 기준\n",
    "sample_interval(0, 50, 10, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시각화 할때 겹치지 않기 하기 위해서 임의의 시간을 더해주는 함수\n",
    "# 시간 범위를 정하고 싶으면 아래의  sample_interval의 앞의 두개의 인풋값 바꿔주기!\n",
    "def timestamp_change(OD_results) :\n",
    "    random_numbers = sample_interval(0, 50, len(OD_results), 1)\n",
    "    for i in range(0, len(OD_results)) :\n",
    "        # (i+1)*3을 더해주는 이유는 출발 시간이 겹치지 않기 하기 위해서\n",
    "        OD_results[i]['timestamp'] = list(np.array(OD_results[i]['timestamp']) + random_numbers[i])\n",
    "        \n",
    "    return OD_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# timestamp 변경\n",
    "OD_results = timestamp_change(OD_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 46.0)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시작 시간이 잘 변경 되었나 확인\n",
    "OD_results[0]['timestamp'][0], OD_results[-1]['timestamp'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 데이터 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 저장\n",
    "path = './src/data/'\n",
    "\n",
    "with open(os.path.join(path + 'trips.json'), 'w', encoding='utf-8') as file:\n",
    "    json.dump(OD_results, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 실습"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 100개의 od를 뽑아서 시뮬레이션을 만들고 깃허브에 올려서 시뮬레이션 링크를 사이버캠퍼스에 제출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 좌표\n",
    "point = {\n",
    "    \"중앙시장사거리\" : [127.131770, 37.440888],\n",
    "    \"숯골사거리\" : [127.142398 , 37.444055],\n",
    "    \"동부센트레빌2단지아파트\" : [127.129460 , 37.447540],\n",
    "    \"수진역\" : [127.140851 , 37.437443],\n",
    "    \"개별용달\" : [127.139292 , 37.446605],\n",
    "    \"버거킹\" : [127.150505 , 37.442235],\n",
    "}"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "과제 할때 simulation-class/smulation/src/components/Trips.json에 들가면 maxTime 최소 350이상으로 설정하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "longitude = 127.135840\n",
    "latitude = 37.442836\n",
    "로 설정하기"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 과제 할때 위의 함수에서 아래와 같이 수정(안하면 시뮬레이션이 잘림)\n",
    "- random_numbers = sample_interval(0, 50, len(OD_results), 1) ->  random_numbers = sample_interval(0, 300, len(OD_results), 1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}