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{
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{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import os\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# a = pd.read_csv('./DOT_Traffic_Speeds_NBE.csv')\n",
"# save it as parquet\n",
"# a.to_parquet('./DOT_Traffic_Speeds_NBE.parquet')\n",
"# a = pd.read_parquet('./DOT_Traffic_Speeds_NBE.parquet')\n",
"\n",
"a = pd.read_parquet('./all_data.parquet')\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"a = a.drop(columns=['ENCODED_POLY_LINE', 'ENCODED_POLY_LINE_LVLS', 'OWNER', 'TRANSCOM_ID', 'LINK_ID'])"
]
},
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" ID SPEED TRAVEL_TIME STATUS DATA_AS_OF \\\n",
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"\n",
" LINK_POINTS BOROUGH \\\n",
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"1 40.8240706,-73.874311 40.8247,-73.86959 40.825... Bronx \n",
"2 40.8271606,-73.84993 40.82771,-73.84671 40.828... Bronx \n",
"3 40.84064,-73.83831 40.83881,-73.83853 40.83463... Bronx \n",
"4 40.8563506,-73.87233 40.85219,-73.871371 40.85... Bronx \n",
"\n",
" LINK_NAME \n",
"0 SIE E SOUTH AVENUE - RICHMOND AVENUE \n",
"1 BE N STRATFORD AVENUE - CASTLE HILL AVE \n",
"2 BE N Castle Hill Avenue - Griswold Ave \n",
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{
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"source": [
"a['DATA_AS_OF'] = pd.to_datetime(a['DATA_AS_OF'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%Y%m%d%H%M%S')"
]
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" ID SPEED TRAVEL_TIME STATUS DATA_AS_OF \\\n",
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"1 129 54.05 86 0 20250306225809 \n",
"2 126 54.05 139 0 20250306225809 \n",
"3 295 0 0 -101 20250306225809 \n",
"4 159 36.66 151 0 20250306225809 \n",
"\n",
" LINK_POINTS BOROUGH \\\n",
"0 40.6210105,-74.168861 40.6207604,-74.168 40.61... Staten Island \n",
"1 40.8240706,-73.874311 40.8247,-73.86959 40.825... Bronx \n",
"2 40.8271606,-73.84993 40.82771,-73.84671 40.828... Bronx \n",
"3 40.84064,-73.83831 40.83881,-73.83853 40.83463... Bronx \n",
"4 40.8563506,-73.87233 40.85219,-73.871371 40.85... Bronx \n",
"\n",
" LINK_NAME \n",
"0 SIE E SOUTH AVENUE - RICHMOND AVENUE \n",
"1 BE N STRATFORD AVENUE - CASTLE HILL AVE \n",
"2 BE N Castle Hill Avenue - Griswold Ave \n",
"3 HRP N LAFAYETTE AVENUE - E TREMONT AVENUE \n",
"4 BRP N WATSON AVENUE - FORDHAM ROAD "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---------------------------------"
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},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
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"execution_count": 7,
"metadata": {},
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"source": [
"# get the unique values of ID\n",
"id_list = a['ID'].unique().tolist()\n",
"id_list"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
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"metadata": {},
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],
"source": [
"df = a[a['ID'].apply(str).str.contains(\"444\", regex=False, na=False, case=False)]\n",
"# convert DATA_AS_OF from %m/%d/%Y %I:%M:%S %p to %Y%m%d%H%M%S\n",
"# df['DATA_AS_OF'] = pd.to_datetime(df['DATA_AS_OF'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%Y%m%d%H%M%S')\n",
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{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# check if all the previous id in this\n",
"original_id = json.load(open('./id_info.json'))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"original_id = original_id.keys()\n",
"original_id = list(original_id)\n"
]
},
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"execution_count": 11,
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{
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"execution_count": 12,
"metadata": {},
"outputs": [
{
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],
"source": [
"# find the id that is not in the original_id\n",
"id_list = a['ID'].unique().tolist()\n",
"id_list = [str(i) for i in id_list]\n",
"id_list = set(id_list)\n",
"id_list = list(id_list)\n",
"id_list = [i for i in original_id if i not in id_list]\n",
"id_list"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"id_list = a['ID'].unique().tolist()"
]
},
{
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"execution_count": 14,
"metadata": {},
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"name": "stdout",
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]
}
],
"source": [
"from multiprocessing import Pool\n",
"\n",
"os.makedirs('./2025_full_data', exist_ok=True)\n",
"os.makedirs('./2025_slim_data', exist_ok=True)\n",
"\n",
"def process_id(id):\n",
" print(f'doing {id}')\n",
" # filter the data by id\n",
" df = a[a['ID'] == id]\n",
" \n",
" # convert DATA_AS_OF from %m/%d/%Y %I:%M:%S %p to %Y%m%d%H%M%S\n",
" df = df.sort_values(['DATA_AS_OF'])\n",
" # reset the index\n",
" df = df.reset_index(drop=True)\n",
" df.to_parquet('./2025_full_data/id_{}.parquet'.format(id))\n",
" # get the value of BOROUGH column of first raw\n",
" borough = df['BOROUGH'].iloc[0]\n",
" link = df['LINK_NAME'].iloc[0]\n",
" length = len(df)\n",
" id_info = {id: {'borough': borough, 'link': link, 'len': length}}\n",
" df = df.drop(columns=['BOROUGH', 'LINK_NAME', 'ID','LINK_POINTS', 'STATUS'])\n",
" # rearrange the columns\n",
" df = df[['DATA_AS_OF', 'SPEED', 'TRAVEL_TIME']]\n",
" df.to_parquet('./2025_slim_data/id_{}.parquet'.format(id))\n",
" return id_info\n",
"\n",
"with Pool() as pool:\n",
" results = pool.map(process_id, id_list)\n",
"\n",
"# Combine all the id_info dictionaries into one\n",
"id_info = {}\n",
"for result in results:\n",
" id_info.update(result)\n",
"\n",
"with open('./id_info_2025.json', 'w') as f:\n",
" json.dump(id_info, f)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
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"\n",
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