Restructure dataset to data/raw/<city>/ format with Dataset Card

- Move existing top-level NYC/, Chicago/, Seongnam/ to data/raw/{nyc,chicago,seongnam}/
- Add raw road network (OSM PBF, road_graph.gpkg, edge_index)
- Add boundary geojson and source raw parquet files
- Add Dataset Card (README.md) following CAMUS-LAB conventions
- Normalize city folder names to lowercase

.gitattributes

@@ -70,3 +70,15 @@ Chicago/road_graph.gpkg filter=lfs diff=lfs merge=lfs -text
 Chicago/road_graph_nodes.gpkg filter=lfs diff=lfs merge=lfs -text
 Chicago/tl_2024_17_tract/tl_2024_17_tract.dbf filter=lfs diff=lfs merge=lfs -text
 Chicago/tl_2024_17_tract/tl_2024_17_tract.shp filter=lfs diff=lfs merge=lfs -text
+data/raw/chicago/osm_simplified.osm.pbf filter=lfs diff=lfs merge=lfs -text
+data/raw/chicago/road_graph.gpkg filter=lfs diff=lfs merge=lfs -text
+data/raw/chicago/road_graph_nodes.gpkg filter=lfs diff=lfs merge=lfs -text
+data/raw/chicago/tl_2024_17_tract/tl_2024_17_tract.dbf filter=lfs diff=lfs merge=lfs -text
+data/raw/chicago/tl_2024_17_tract/tl_2024_17_tract.shp filter=lfs diff=lfs merge=lfs -text
+data/raw/nyc/osm_simplified.osm.pbf filter=lfs diff=lfs merge=lfs -text
+data/raw/nyc/road_graph.gpkg filter=lfs diff=lfs merge=lfs -text
+data/raw/nyc/road_graph_nodes.gpkg filter=lfs diff=lfs merge=lfs -text
+data/raw/nyc/taxi_zones/taxi_zones.shp filter=lfs diff=lfs merge=lfs -text
+data/raw/seongnam/osm_simplified.osm.pbf filter=lfs diff=lfs merge=lfs -text
+data/raw/seongnam/road_graph.gpkg filter=lfs diff=lfs merge=lfs -text
+data/raw/seongnam/road_graph_nodes.gpkg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,215 @@
+---
+license: other
+license_name: source-attribution
+language:
+  - en
+  - ko
+tags:
+  - drt
+  - demand-responsive-transport
+  - urban-mobility
+  - simulation
+  - benchmark
+  - multi-city
+pretty_name: "DRT Multi-City Benchmark Dataset"
+size_categories:
+  - 100M<n<1B
+---
+
+# DRT Multi-City Benchmark Dataset
+
+다도시 DRT(Demand-Responsive Transport) 시뮬레이션 벤치마킹을 위한 통합 입력 데이터셋. 서로 다른 밀도·면적·데이터 포맷을 가진 세 도시(**NYC 맨해튼**, **Chicago**, **성남시 수정구**)의 수요·도로 네트워크·차량 데이터를 공통 스키마로 정제하여 제공한다.
+
+- **대상**: DRT/모빌리티 연구자, 교통공학 대학원생, 도시 시뮬레이션 개발자
+- **용도**: 다도시 DRT 알고리즘 벤치마킹, 배차/리밸런싱 비교, 차량대수 산정 검증, 도시 카테고리 일반화 연구
+- **연계 시뮬레이터**: [DTUMOS](https://github.com/DTUMOS/DTUMOS) — Digital Twin Urban Mobility Simulator
+
+## 핵심 사실
+
+- **데이터 이질성**: NYC = Zone ID(좌표 없음), Chicago = 15분 binned + Census Tract centroid, 성남 = 초 단위 GPS 좌표
+- 세 도시의 원본 정밀도 차이를 통합 스키마(`pickup_time`, `pickup_lon`, `pickup_lat`, `dropoff_lon`, `dropoff_lat`)로 정규화
+- 도로 네트워크는 OSM 기반 `road_graph.gpkg` (노드/엣지)와 `osm_simplified.osm.pbf` 동봉 — Rust CH 라우팅 즉시 사용 가능
+- 모든 좌표계: **EPSG:4326 (WGS84)**
+
+---
+
+## 폴더 구조
+
+```
+data/raw/
+├── nyc/                              NYC Yellow Taxi (TLC, 2017-10-19 목)
+│   ├── demand.csv                    정제된 수요 (통합 스키마)
+│   ├── nyc_avg_demand_5day.csv       5일 평균 수요 (Poisson 시드용)
+│   ├── nyc_manhattan_2017-10-19.parquet   해당일 필터된 원본
+│   ├── yellow_tripdata_2017-10.parquet    원본 한 달치 (TLC raw)
+│   ├── nyc_taxi_preprocessing.ipynb       전처리 노트북 (NYC Zone → 좌표 매핑 + 통합 스키마 변환)
+│   ├── explore_taxi_data.ipynb            데이터 탐색 노트북 (TLC raw 구조·컬럼 분석)
+│   ├── taxi+_zone_lookup.csv              TLC 263개 Zone ID ↔ 자치구·이름 매핑표
+│   ├── boundary.geojson              맨해튼 경계
+│   ├── road_graph.gpkg               OSM 도로 그래프 (LineString)
+│   ├── road_graph_nodes.gpkg         노드 (Point)
+│   ├── road_graph.meta.json          메타 (노드/엣지 수)
+│   ├── osm_simplified.osm.pbf        OSM PBF (라우팅 엔진용)
+│   ├── edge_index.pkl                Rust CH용 엣지 인덱스
+│   ├── taxi_zones/                   TLC 263개 택시존 shapefile
+│   └── seeds/                        시드별 샘플 3종
+│       ├── demand_seed1.csv
+│       ├── demand_seed2.csv
+│       └── demand_seed3.csv
+│
+├── chicago/                          Chicago TNC Ride-hail (2024-03-14 목)
+│   ├── demand.csv                    정제된 수요 (15분 binned → 분 단위 disaggregation)
+│   ├── Taxi_Trips_2024-03-14.parquet 원본 일자 추출본
+│   ├── boundary.geojson              시카고 경계
+│   ├── road_graph.gpkg
+│   ├── road_graph_nodes.gpkg
+│   ├── road_graph.meta.json
+│   ├── osm_simplified.osm.pbf
+│   ├── edge_index.pkl
+│   ├── tl_2024_17_tract/             Census Tract shapefile (centroid 매핑용)
+│   └── seeds/
+│       ├── demand_seed1.csv
+│       ├── demand_seed2.csv
+│       └── demand_seed3.csv
+│
+└── seongnam/                         성남시 수정구 스마트카드 택시 (2024-04-18 목)
+    ├── demand.csv                    정제된 수요 (초 단위 GPS)
+    ├── vehicles.csv                  실측 차량 풀
+    ├── boundary.geojson              수정구 경계
+    ├── road_graph.gpkg
+    ├── road_graph_nodes.gpkg
+    ├── road_graph_edges.parquet
+    ├── road_graph_nodes.parquet
+    ├── road_graph.meta.json
+    ├── osm_simplified.osm.pbf
+    ├── edge_index.pkl
+    ├── semantic_graph.json           격자 기반 임시 정류장(Virtual Stop) 시드
+    └── seeds/
+        ├── demand_seed1.csv
+        └── demand_seed2.csv
+```
+
+---
+
+## 도시별 데이터 명세
+
+| 항목 | NYC (맨해튼) | Chicago | 성남시 (수정구) |
+|------|-------------|---------|----------------|
+| **원본 출처** | NYC TLC Yellow Taxi | City of Chicago Open Data (TNC) | 성남시 스마트카드 택시 |
+| **대상 일자** | 2017-10-19 (목) | 2024-03-14 (목) | 2024-04-18 (목) |
+| **원본 시간 정밀도** | 초 단위 | **15분 binned** | 초 단위 |
+| **원본 공간 정밀도** | **Zone ID만 (263개)** | Census Tract centroid | **GPS 좌표** |
+| **수요 건수 (정제 후)** | 65,894건 | 53,553건 | 8,041건 |
+| **시뮬 면적** | ~60 km² (맨해튼) | ~600 km² (시카고시) | ~25 km² (수정구) |
+| **수요 밀도 (건/km²/h)** | ~258 (초고밀도) | ~18 (저밀도 광역) | ~43.6 (중밀도) |
+| **차량대수 산정 (다반조 GM)** | 1,000대 | 1,600대 | 130대 |
+
+### 통합 스키마 (`demand.csv` 컬럼)
+
+| 컬럼 | 타입 | 설명 |
+|------|------|------|
+| `pickup_time` | datetime | 픽업 시각 (ISO 8601) |
+| `pickup_lon`, `pickup_lat` | float | WGS84 픽업 좌표 |
+| `dropoff_lon`, `dropoff_lat` | float | WGS84 하차 좌표 |
+| `pax_count` | int | 승객 수 (기본 1) |
+
+> **시드 파일(`seeds/demand_seed*.csv`)**: 동일 스키마. 평일 5일 평균을 Poisson 시드로 샘플링한 3종. 재현성을 위해 시드 1·2·3 동봉.
+
+---
+
+## 데이터 출처 및 라이선스
+
+| 데이터 | 출처 | 라이선스 |
+|--------|------|---------|
+| NYC Yellow Taxi 2017-10 | [NYC TLC Trip Record Data](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page) | NYC Open Data Terms |
+| NYC TLC Taxi Zones | NYC TLC | NYC Open Data Terms |
+| Chicago Taxi Trips 2024-03 | [City of Chicago Open Data Portal](https://data.cityofchicago.org/) | City of Chicago Open Data |
+| Chicago Census Tract 2024 | US Census TIGER/Line 2024 | Public Domain |
+| 성남시 스마트카드 택시 2024-04 | 성남시 (연구 협약 데이터) | 비공개 — 정제·집계본만 공개 |
+| OSM 도로 네트워크 | [OpenStreetMap](https://www.openstreetmap.org/) | ODbL 1.0 |
+
+**본 데이터셋 라이선스**: `source-attribution` — 재배포 시 반드시 출처를 명기할 것.
+
+---
+
+## 다운로드 및 사용법
+
+### 전체 다운로드
+
+```bash
+# (권장) 풀 데이터셋 — 약 420MB
+hf download CAMUS-LAB/drt --repo-type dataset --local-dir ./data
+```
+
+### 도시별 부분 다운로드
+
+```bash
+# NYC만
+hf download CAMUS-LAB/drt --repo-type dataset \
+    --include "data/raw/nyc/**" --local-dir ./data
+```
+
+### Python에서 직접 로드
+
+```python
+from huggingface_hub import snapshot_download
+local_path = snapshot_download(
+    repo_id="CAMUS-LAB/drt",
+    repo_type="dataset",
+    allow_patterns=["data/raw/seongnam/**"],
+)
+
+import pandas as pd
+df = pd.read_csv(f"{local_path}/data/raw/seongnam/demand.csv")
+```
+
+### DTUMOS 시뮬레이터에 연결
+
+다운로드한 데이터를 DTUMOS의 `data/cities/<city>/` 에 배치하면 자동 감지된다:
+
+```
+DTUMOS/data/cities/
+├── NYC/        ← data/raw/nyc/  의 내용
+├── Chicago/    ← data/raw/chicago/  의 내용
+└── Seongnam/   ← data/raw/seongnam/  의 내용
+```
+
+```bash
+cd DTUMOS
+python -m dtumos.cli simulate --city NYC --dispatch D3R --rebalancing R1b
+```
+
+---
+
+## 관련 연구
+
+- **시뮬레이터**: [DTUMOS](https://github.com/DTUMOS/DTUMOS) — Digital Twin Urban Mobility Simulator (Python + Rust CH + Java RAPTOR)
+- **수요 모델**: [dtumos-demand-model](https://github.com/DTUMOS/dtumos-demand-model) — 수요 프로파일 생성 모델
+- **연구 발표**: 2026 ITS 춘계학회 — "다도시 DRT 알고리즘 벤치마킹: 도시 맥락 기반 성능 비교 프레임워크" (방혜원, 가천대 스마트시티융합학과)
+
+### 향후 확장
+
+- **도시 추가**: 한국 5+ 도시 (서울/대구/대전/수원 등 스마트카드), 해외 추가 (싱가포르 등)
+- **시간 이질성**: 일자 → 한 달 평균 (계절성), 시간대별 (피크/오프피크/심야)
+- **Virtual Stop**: 격자 기반 임시 정류장 데이터 (저밀도 도시용)
+
+---
+
+## 인용
+
+```bibtex
+@dataset{drt_multi_city_benchmark_2026,
+  author       = {Bang, Hyewon and CAMUS Lab},
+  title        = {DRT Multi-City Benchmark Dataset (NYC, Chicago, Seongnam)},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/CAMUS-LAB/drt},
+  note         = {Multi-city DRT simulation input dataset for benchmarking dispatch and rebalancing algorithms}
+}
+```
+
+---
+
+## 변경 이력
+
+- **2026-05** — 초기 공개판: NYC / Chicago / Seongnam 3개 도시 raw 입력 데이터, Dataset Card 및 통합 스키마 명세 추가

data/raw/nyc/explore_taxi_data.ipynb

@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NYC Yellow Taxi 2017-10 데이터 탐색"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_parquet('yellow_tripdata_2017-10.parquet')\n",
+    "print(f'총 {len(df):,}건, {len(df.columns)}개 컬럼')\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Zone Lookup 매핑"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zones = pd.read_csv('taxi+_zone_lookup.csv')\n",
+    "zones.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 픽업/드롭오프에 Zone 이름 붙이기\n",
+    "df_named = df.merge(zones[['LocationID','Borough','Zone']], left_on='PULocationID', right_on='LocationID', how='left')\n",
+    "df_named = df_named.rename(columns={'Borough': 'PU_Borough', 'Zone': 'PU_Zone'}).drop(columns='LocationID')\n",
+    "df_named = df_named.merge(zones[['LocationID','Borough','Zone']], left_on='DOLocationID', right_on='LocationID', how='left')\n",
+    "df_named = df_named.rename(columns={'Borough': 'DO_Borough', 'Zone': 'DO_Zone'}).drop(columns='LocationID')\n",
+    "\n",
+    "df_named[['tpep_pickup_datetime','PU_Borough','PU_Zone','DO_Borough','DO_Zone','trip_distance','total_amount']].head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 기본 분포"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 픽업 많은 Zone Top 15\n",
+    "top_pu = df_named['PU_Zone'].value_counts().head(15)\n",
+    "print('=== 픽업 Top 15 ===')\n",
+    "print(top_pu)\n",
+    "print()\n",
+    "\n",
+    "# 자치구별 건수\n",
+    "print('=== 자치구별 픽업 ===')\n",
+    "print(df_named['PU_Borough'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 시간대별 수요\n",
+    "df['hour'] = df['tpep_pickup_datetime'].dt.hour\n",
+    "hourly = df['hour'].value_counts().sort_index()\n",
+    "hourly.plot(kind='bar', figsize=(12,4), title='시간대별 픽업 건수')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 거리 & 요금 분포 (이상치 제거)\n",
+    "reasonable = df[(df['trip_distance'] > 0) & (df['trip_distance'] < 30) & (df['total_amount'] > 0) & (df['total_amount'] < 100)]\n",
+    "reasonable[['trip_distance','total_amount']].hist(bins=50, figsize=(12,4))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

data/raw/nyc/taxi+_zone_lookup.csv

@@ -0,0 +1,266 @@
+"LocationID","Borough","Zone","service_zone"
+1,"EWR","Newark Airport","EWR"
+2,"Queens","Jamaica Bay","Boro Zone"
+3,"Bronx","Allerton/Pelham Gardens","Boro Zone"
+4,"Manhattan","Alphabet City","Yellow Zone"
+5,"Staten Island","Arden Heights","Boro Zone"
+6,"Staten Island","Arrochar/Fort Wadsworth","Boro Zone"
+7,"Queens","Astoria","Boro Zone"
+8,"Queens","Astoria Park","Boro Zone"
+9,"Queens","Auburndale","Boro Zone"
+10,"Queens","Baisley Park","Boro Zone"
+11,"Brooklyn","Bath Beach","Boro Zone"
+12,"Manhattan","Battery Park","Yellow Zone"
+13,"Manhattan","Battery Park City","Yellow Zone"
+14,"Brooklyn","Bay Ridge","Boro Zone"
+15,"Queens","Bay Terrace/Fort Totten","Boro Zone"
+16,"Queens","Bayside","Boro Zone"
+17,"Brooklyn","Bedford","Boro Zone"
+18,"Bronx","Bedford Park","Boro Zone"
+19,"Queens","Bellerose","Boro Zone"
+20,"Bronx","Belmont","Boro Zone"
+21,"Brooklyn","Bensonhurst East","Boro Zone"
+22,"Brooklyn","Bensonhurst West","Boro Zone"
+23,"Staten Island","Bloomfield/Emerson Hill","Boro Zone"
+24,"Manhattan","Bloomingdale","Yellow Zone"
+25,"Brooklyn","Boerum Hill","Boro Zone"
+26,"Brooklyn","Borough Park","Boro Zone"
+27,"Queens","Breezy Point/Fort Tilden/Riis Beach","Boro Zone"
+28,"Queens","Briarwood/Jamaica Hills","Boro Zone"
+29,"Brooklyn","Brighton Beach","Boro Zone"
+30,"Queens","Broad Channel","Boro Zone"
+31,"Bronx","Bronx Park","Boro Zone"
+32,"Bronx","Bronxdale","Boro Zone"
+33,"Brooklyn","Brooklyn Heights","Boro Zone"
+34,"Brooklyn","Brooklyn Navy Yard","Boro Zone"
+35,"Brooklyn","Brownsville","Boro Zone"
+36,"Brooklyn","Bushwick North","Boro Zone"
+37,"Brooklyn","Bushwick South","Boro Zone"
+38,"Queens","Cambria Heights","Boro Zone"
+39,"Brooklyn","Canarsie","Boro Zone"
+40,"Brooklyn","Carroll Gardens","Boro Zone"
+41,"Manhattan","Central Harlem","Boro Zone"
+42,"Manhattan","Central Harlem North","Boro Zone"
+43,"Manhattan","Central Park","Yellow Zone"
+44,"Staten Island","Charleston/Tottenville","Boro Zone"
+45,"Manhattan","Chinatown","Yellow Zone"
+46,"Bronx","City Island","Boro Zone"
+47,"Bronx","Claremont/Bathgate","Boro Zone"
+48,"Manhattan","Clinton East","Yellow Zone"
+49,"Brooklyn","Clinton Hill","Boro Zone"
+50,"Manhattan","Clinton West","Yellow Zone"
+51,"Bronx","Co-Op City","Boro Zone"
+52,"Brooklyn","Cobble Hill","Boro Zone"
+53,"Queens","College Point","Boro Zone"
+54,"Brooklyn","Columbia Street","Boro Zone"
+55,"Brooklyn","Coney Island","Boro Zone"
+56,"Queens","Corona","Boro Zone"
+57,"Queens","Corona","Boro Zone"
+58,"Bronx","Country Club","Boro Zone"
+59,"Bronx","Crotona Park","Boro Zone"
+60,"Bronx","Crotona Park East","Boro Zone"
+61,"Brooklyn","Crown Heights North","Boro Zone"
+62,"Brooklyn","Crown Heights South","Boro Zone"
+63,"Brooklyn","Cypress Hills","Boro Zone"
+64,"Queens","Douglaston","Boro Zone"
+65,"Brooklyn","Downtown Brooklyn/MetroTech","Boro Zone"
+66,"Brooklyn","DUMBO/Vinegar Hill","Boro Zone"
+67,"Brooklyn","Dyker Heights","Boro Zone"
+68,"Manhattan","East Chelsea","Yellow Zone"
+69,"Bronx","East Concourse/Concourse Village","Boro Zone"
+70,"Queens","East Elmhurst","Boro Zone"
+71,"Brooklyn","East Flatbush/Farragut","Boro Zone"
+72,"Brooklyn","East Flatbush/Remsen Village","Boro Zone"
+73,"Queens","East Flushing","Boro Zone"
+74,"Manhattan","East Harlem North","Boro Zone"
+75,"Manhattan","East Harlem South","Boro Zone"
+76,"Brooklyn","East New York","Boro Zone"
+77,"Brooklyn","East New York/Pennsylvania Avenue","Boro Zone"
+78,"Bronx","East Tremont","Boro Zone"
+79,"Manhattan","East Village","Yellow Zone"
+80,"Brooklyn","East Williamsburg","Boro Zone"
+81,"Bronx","Eastchester","Boro Zone"
+82,"Queens","Elmhurst","Boro Zone"
+83,"Queens","Elmhurst/Maspeth","Boro Zone"
+84,"Staten Island","Eltingville/Annadale/Prince's Bay","Boro Zone"
+85,"Brooklyn","Erasmus","Boro Zone"
+86,"Queens","Far Rockaway","Boro Zone"
+87,"Manhattan","Financial District North","Yellow Zone"
+88,"Manhattan","Financial District South","Yellow Zone"
+89,"Brooklyn","Flatbush/Ditmas Park","Boro Zone"
+90,"Manhattan","Flatiron","Yellow Zone"
+91,"Brooklyn","Flatlands","Boro Zone"
+92,"Queens","Flushing","Boro Zone"
+93,"Queens","Flushing Meadows-Corona Park","Boro Zone"
+94,"Bronx","Fordham South","Boro Zone"
+95,"Queens","Forest Hills","Boro Zone"
+96,"Queens","Forest Park/Highland Park","Boro Zone"
+97,"Brooklyn","Fort Greene","Boro Zone"
+98,"Queens","Fresh Meadows","Boro Zone"
+99,"Staten Island","Freshkills Park","Boro Zone"
+100,"Manhattan","Garment District","Yellow Zone"
+101,"Queens","Glen Oaks","Boro Zone"
+102,"Queens","Glendale","Boro Zone"
+103,"Manhattan","Governor's Island/Ellis Island/Liberty Island","Yellow Zone"
+104,"Manhattan","Governor's Island/Ellis Island/Liberty Island","Yellow Zone"
+105,"Manhattan","Governor's Island/Ellis Island/Liberty Island","Yellow Zone"
+106,"Brooklyn","Gowanus","Boro Zone"
+107,"Manhattan","Gramercy","Yellow Zone"
+108,"Brooklyn","Gravesend","Boro Zone"
+109,"Staten Island","Great Kills","Boro Zone"
+110,"Staten Island","Great Kills Park","Boro Zone"
+111,"Brooklyn","Green-Wood Cemetery","Boro Zone"
+112,"Brooklyn","Greenpoint","Boro Zone"
+113,"Manhattan","Greenwich Village North","Yellow Zone"
+114,"Manhattan","Greenwich Village South","Yellow Zone"
+115,"Staten Island","Grymes Hill/Clifton","Boro Zone"
+116,"Manhattan","Hamilton Heights","Boro Zone"
+117,"Queens","Hammels/Arverne","Boro Zone"
+118,"Staten Island","Heartland Village/Todt Hill","Boro Zone"
+119,"Bronx","Highbridge","Boro Zone"
+120,"Manhattan","Highbridge Park","Boro Zone"
+121,"Queens","Hillcrest/Pomonok","Boro Zone"
+122,"Queens","Hollis","Boro Zone"
+123,"Brooklyn","Homecrest","Boro Zone"
+124,"Queens","Howard Beach","Boro Zone"
+125,"Manhattan","Hudson Sq","Yellow Zone"
+126,"Bronx","Hunts Point","Boro Zone"
+127,"Manhattan","Inwood","Boro Zone"
+128,"Manhattan","Inwood Hill Park","Boro Zone"
+129,"Queens","Jackson Heights","Boro Zone"
+130,"Queens","Jamaica","Boro Zone"
+131,"Queens","Jamaica Estates","Boro Zone"
+132,"Queens","JFK Airport","Airports"
+133,"Brooklyn","Kensington","Boro Zone"
+134,"Queens","Kew Gardens","Boro Zone"
+135,"Queens","Kew Gardens Hills","Boro Zone"
+136,"Bronx","Kingsbridge Heights","Boro Zone"
+137,"Manhattan","Kips Bay","Yellow Zone"
+138,"Queens","LaGuardia Airport","Airports"
+139,"Queens","Laurelton","Boro Zone"
+140,"Manhattan","Lenox Hill East","Yellow Zone"
+141,"Manhattan","Lenox Hill West","Yellow Zone"
+142,"Manhattan","Lincoln Square East","Yellow Zone"
+143,"Manhattan","Lincoln Square West","Yellow Zone"
+144,"Manhattan","Little Italy/NoLiTa","Yellow Zone"
+145,"Queens","Long Island City/Hunters Point","Boro Zone"
+146,"Queens","Long Island City/Queens Plaza","Boro Zone"
+147,"Bronx","Longwood","Boro Zone"
+148,"Manhattan","Lower East Side","Yellow Zone"
+149,"Brooklyn","Madison","Boro Zone"
+150,"Brooklyn","Manhattan Beach","Boro Zone"
+151,"Manhattan","Manhattan Valley","Yellow Zone"
+152,"Manhattan","Manhattanville","Boro Zone"
+153,"Manhattan","Marble Hill","Boro Zone"
+154,"Brooklyn","Marine Park/Floyd Bennett Field","Boro Zone"
+155,"Brooklyn","Marine Park/Mill Basin","Boro Zone"
+156,"Staten Island","Mariners Harbor","Boro Zone"
+157,"Queens","Maspeth","Boro Zone"
+158,"Manhattan","Meatpacking/West Village West","Yellow Zone"
+159,"Bronx","Melrose South","Boro Zone"
+160,"Queens","Middle Village","Boro Zone"
+161,"Manhattan","Midtown Center","Yellow Zone"
+162,"Manhattan","Midtown East","Yellow Zone"
+163,"Manhattan","Midtown North","Yellow Zone"
+164,"Manhattan","Midtown South","Yellow Zone"
+165,"Brooklyn","Midwood","Boro Zone"
+166,"Manhattan","Morningside Heights","Boro Zone"
+167,"Bronx","Morrisania/Melrose","Boro Zone"
+168,"Bronx","Mott Haven/Port Morris","Boro Zone"
+169,"Bronx","Mount Hope","Boro Zone"
+170,"Manhattan","Murray Hill","Yellow Zone"
+171,"Queens","Murray Hill-Queens","Boro Zone"
+172,"Staten Island","New Dorp/Midland Beach","Boro Zone"
+173,"Queens","North Corona","Boro Zone"
+174,"Bronx","Norwood","Boro Zone"
+175,"Queens","Oakland Gardens","Boro Zone"
+176,"Staten Island","Oakwood","Boro Zone"
+177,"Brooklyn","Ocean Hill","Boro Zone"
+178,"Brooklyn","Ocean Parkway South","Boro Zone"
+179,"Queens","Old Astoria","Boro Zone"
+180,"Queens","Ozone Park","Boro Zone"
+181,"Brooklyn","Park Slope","Boro Zone"
+182,"Bronx","Parkchester","Boro Zone"
+183,"Bronx","Pelham Bay","Boro Zone"
+184,"Bronx","Pelham Bay Park","Boro Zone"
+185,"Bronx","Pelham Parkway","Boro Zone"
+186,"Manhattan","Penn Station/Madison Sq West","Yellow Zone"
+187,"Staten Island","Port Richmond","Boro Zone"
+188,"Brooklyn","Prospect-Lefferts Gardens","Boro Zone"
+189,"Brooklyn","Prospect Heights","Boro Zone"
+190,"Brooklyn","Prospect Park","Boro Zone"
+191,"Queens","Queens Village","Boro Zone"
+192,"Queens","Queensboro Hill","Boro Zone"
+193,"Queens","Queensbridge/Ravenswood","Boro Zone"
+194,"Manhattan","Randalls Island","Yellow Zone"
+195,"Brooklyn","Red Hook","Boro Zone"
+196,"Queens","Rego Park","Boro Zone"
+197,"Queens","Richmond Hill","Boro Zone"
+198,"Queens","Ridgewood","Boro Zone"
+199,"Bronx","Rikers Island","Boro Zone"
+200,"Bronx","Riverdale/North Riverdale/Fieldston","Boro Zone"
+201,"Queens","Rockaway Park","Boro Zone"
+202,"Manhattan","Roosevelt Island","Boro Zone"
+203,"Queens","Rosedale","Boro Zone"
+204,"Staten Island","Rossville/Woodrow","Boro Zone"
+205,"Queens","Saint Albans","Boro Zone"
+206,"Staten Island","Saint George/New Brighton","Boro Zone"
+207,"Queens","Saint Michaels Cemetery/Woodside","Boro Zone"
+208,"Bronx","Schuylerville/Edgewater Park","Boro Zone"
+209,"Manhattan","Seaport","Yellow Zone"
+210,"Brooklyn","Sheepshead Bay","Boro Zone"
+211,"Manhattan","SoHo","Yellow Zone"
+212,"Bronx","Soundview/Bruckner","Boro Zone"
+213,"Bronx","Soundview/Castle Hill","Boro Zone"
+214,"Staten Island","South Beach/Dongan Hills","Boro Zone"
+215,"Queens","South Jamaica","Boro Zone"
+216,"Queens","South Ozone Park","Boro Zone"
+217,"Brooklyn","South Williamsburg","Boro Zone"
+218,"Queens","Springfield Gardens North","Boro Zone"
+219,"Queens","Springfield Gardens South","Boro Zone"
+220,"Bronx","Spuyten Duyvil/Kingsbridge","Boro Zone"
+221,"Staten Island","Stapleton","Boro Zone"
+222,"Brooklyn","Starrett City","Boro Zone"
+223,"Queens","Steinway","Boro Zone"
+224,"Manhattan","Stuy Town/Peter Cooper Village","Yellow Zone"
+225,"Brooklyn","Stuyvesant Heights","Boro Zone"
+226,"Queens","Sunnyside","Boro Zone"
+227,"Brooklyn","Sunset Park East","Boro Zone"
+228,"Brooklyn","Sunset Park West","Boro Zone"
+229,"Manhattan","Sutton Place/Turtle Bay North","Yellow Zone"
+230,"Manhattan","Times Sq/Theatre District","Yellow Zone"
+231,"Manhattan","TriBeCa/Civic Center","Yellow Zone"
+232,"Manhattan","Two Bridges/Seward Park","Yellow Zone"
+233,"Manhattan","UN/Turtle Bay South","Yellow Zone"
+234,"Manhattan","Union Sq","Yellow Zone"
+235,"Bronx","University Heights/Morris Heights","Boro Zone"
+236,"Manhattan","Upper East Side North","Yellow Zone"
+237,"Manhattan","Upper East Side South","Yellow Zone"
+238,"Manhattan","Upper West Side North","Yellow Zone"
+239,"Manhattan","Upper West Side South","Yellow Zone"
+240,"Bronx","Van Cortlandt Park","Boro Zone"
+241,"Bronx","Van Cortlandt Village","Boro Zone"
+242,"Bronx","Van Nest/Morris Park","Boro Zone"
+243,"Manhattan","Washington Heights North","Boro Zone"
+244,"Manhattan","Washington Heights South","Boro Zone"
+245,"Staten Island","West Brighton","Boro Zone"
+246,"Manhattan","West Chelsea/Hudson Yards","Yellow Zone"
+247,"Bronx","West Concourse","Boro Zone"
+248,"Bronx","West Farms/Bronx River","Boro Zone"
+249,"Manhattan","West Village","Yellow Zone"
+250,"Bronx","Westchester Village/Unionport","Boro Zone"
+251,"Staten Island","Westerleigh","Boro Zone"
+252,"Queens","Whitestone","Boro Zone"
+253,"Queens","Willets Point","Boro Zone"
+254,"Bronx","Williamsbridge/Olinville","Boro Zone"
+255,"Brooklyn","Williamsburg (North Side)","Boro Zone"
+256,"Brooklyn","Williamsburg (South Side)","Boro Zone"
+257,"Brooklyn","Windsor Terrace","Boro Zone"
+258,"Queens","Woodhaven","Boro Zone"
+259,"Bronx","Woodlawn/Wakefield","Boro Zone"
+260,"Queens","Woodside","Boro Zone"
+261,"Manhattan","World Trade Center","Yellow Zone"
+262,"Manhattan","Yorkville East","Yellow Zone"
+263,"Manhattan","Yorkville West","Yellow Zone"
+264,"Unknown","N/A","N/A"
+265,"N/A","Outside of NYC","N/A"