Dataset in LibCity

This repository is used to introduce the dataset in LibCity.

Dataset Conversion Tools

The dataset used in LibCity is stored in a unified data storage format named atomic files. In order to directly use the datasets we collected in LibCity, we have converted all datasets into the format of atomic files, and provide the conversion tools in this repository.

All conversion tools take the original dataset in the ./input/ directory as input, and output the converted atomic files to the ./output/ directory. In addition, we provide a link to obtain the original dataset in the first line of each conversion tool. You can download the original dataset through this link and place it in the ./input/ directory. Imitating our conversion tools, you can easily convert your own traffic dataset to adapt it to LibCity.

Besides, you can simply download the datasets we have processed, the data link is BaiduDisk with code 1231 or Google Drive.

Dataset Statistics Infomation

Here we present the statistics of the datasets we have processed.

DATASET	#GEO	#REL	#USR	#DYNA	PLACE	DURATION	INTERVAL
METR_LA	207	11,753	—	7,094,304	Los Angeles, USA	Mar. 1, 2012 - Jun. 27, 2012	5min
LOS_LOOP	207	42,849	—	7,094,304	Los Angeles, USA	Mar. 1, 2012 - Jun. 27, 2012	5min
SZ_TAXI	156	24,336	—	464,256	Shenzhen, China	Jan. 1, 2015 - Jan. 31, 2015	15min
LOOP_SEATTLE	323	104,329	—	33,953,760	Greater Seattle Area, USA	over the entirely of 2015	5min
Q_TRAFFIC	45,148	63,422	—	264,386,688	Beijing, China	Apr. 1, 2017 - May 31, 2017	15min
PEMSD3	358	547	—	9,382,464	California, USA	Sept. 1, 2018 - Nov. 30, 2018	5min
PEMSD4	307	340	—	5,216,544	San Francisco Bay Area, USA	Jan. 1, 2018 - Feb. 28, 2018	5min
PEMSD7	883	866	—	24,921,792	California, USA	Jul. 1, 2016 - Aug. 31, 2016	5min
PEMSD8	170	277	—	3,035,520	San Bernardino Area, USA	Jul. 1, 2016 - Aug. 31, 2016	5min
PEMSD7(M)	228	51,984	—	2,889,216	California, USA	weekdays of May and June, 2012	5min
PEMS_BAY	325	8,358	—	16,937,700	San Francisco Bay Area, USA	Jan. 1, 2017 - Jun. 30, 2017	5min
BEIJING_SUBWAY	276	76,176	—	248,400	Beijing, China	Feb. 29, 2016 - Apr. 3, 2016	30min
M_DENSE	30	—	—	525,600	Madrid, Spain	Jan. 1, 2018 - Dec. 21, 2019	60min
ROTTERDAM	208	—	—	4,813,536	Rotterdam, Holland	135 days of 2018	2min
SHMETRO	288	82,944	—	1,934,208	Shanghai, China	Jul. 1, 2016 - Sept. 30, 2016	15min
HZMETRO	80	6,400	—	146,000	Hangzhou, China	Jan. 1, 2019 - Jan. 25, 2019	15min
TAXIBJ	32*32	—	—	5,652,480	Beijing, China	Mar. 1, 2015 - Jun. 30, 2015	30min
T_DRIVE	32*32	1,048,576	—	3,686,400	Beijing, China	Feb. 1, 2015 - Jun. 30, 2015	60min
PORTO	20*10	—	—	441,600	Porto, Portugal	Jul. 1, 2013 - Sept. 30, 2013	60min
NYCTAXI_DYNA	263	69,169	—	574,392	New York, USA	Jan. 1, 2020 - Mar. 30, 2020	60min
NYCTAXI_OD	263	69,169	—	150,995,927	New York, USA	Apr. 1, 2020 - Jun. 30, 2020	60min
NYCTAXI_GRID	10*20	—	—	432,000	New York, USA	Jan. 1, 2014 - Mar. 31, 2014	60min
NYCBIKE	10*20	—	—	441,600	New York, USA	Jul. 1, 2020 - Sept. 30, 2020	60min
AUSTINRIDE	16*8	—	—	282,624	Austin, USA	Jul. 1, 2016 - Sept. 30, 2016	60min
BIKEDC	16*8	—	—	282,624	Washington, USA	Jul. 1, 2020 - Sept. 30, 2020	60min
BIKECHI	15*18	—	—	670,680	Chicago, USA	Jul. 1, 2020 - Sept. 30, 2020	60min
Foursquare_TKY	94,890	—	11,589	1,112,156	Tokyo, Japan	Apr. 3, 2012 - Sep. 16, 2013	—
Foursquare_NYC	64,735	—	17,175	568,444	New York, USA	Apr. 3, 2012 - Sep. 16, 2013	—
Gowalla	1,280,969	913,660	107,092	6,442,892	Global	Feb. 4, 2009 - Oct. 23, 2010	—
BrightKite	772,966	394,334	51,406	4,747,287	Global	Mar. 21, 2008 - Oct. 18, 2010	—
Instagram	13,187	—	78,233	2,205,794	New York, USA	Jun. 15, 2011 - Nov. 8, 2016	—
Seattle	613645	857406	1	7531	Seattle WA, USA	Jan.17,2009 20:27:37 - 22:34:28	1s
Global	11045	18196	1	2502	Neftekamsk, Republic of Bashkortostan, Russian Federation	—	1s
bj_roadmap_edge	38027	95660	—	—	Beijing, China	—	—
bj_roadmap_node	16927	38027	—	—	Beijing, China	—	—
NYC_RISK	243	59049	—	3504000	New York, USA	Jan. 01, 2013 - Dec. 31, 2013	60min
CHICAGO_RISK	197	38809	—	2332800	Chicago, USA	Feb. 01, 2016 - Sep. 30, 2016	60min
Chengdu_Taxi_Sample1	—	—	4565	712360	Chengdu, China	Aug. 03, 2014 - Aug. 30, 2014	—
Beijing_Taxi_Sample	16384	—	76	518424	Beijing, China	Oct. 01, 2013 - Oct. 31, 2013	—
NYC_TOD	15*5	—	—	98,550,000	New York, USA

Note：

• NYCTAXI_DYNA is a dataset that counts the inflow and outflow of the region with an irregular area division method.
• NYCTAXI_OD is a dataset that counts the origin-destination flow between regions with an irregular area division method.
• NYCTAXI_GRID is a dataset that counts the inflow and outflow of the region with a grid-base division method.
• NYC_TOD is a dataset that counts the origin-destination flow between regions with a grid-base division method.

Cite

Our paper is accepted by ACM SIGSPATIAL 2021. If you find LibCity useful for your research or development, please cite our paper.

@inproceedings{10.1145/3474717.3483923,
  author = {Wang, Jingyuan and Jiang, Jiawei and Jiang, Wenjun and Li, Chao and Zhao, Wayne Xin},
  title = {LibCity: An Open Library for Traffic Prediction},
  year = {2021},
  isbn = {9781450386647},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3474717.3483923},
  doi = {10.1145/3474717.3483923},
  booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems},
  pages = {145–148},
  numpages = {4},
  keywords = {Spatial-temporal System, Reproducibility, Traffic Prediction},
  location = {Beijing, China},
  series = {SIGSPATIAL '21}
}

Jingyuan Wang, Jiawei Jiang, Wenjun Jiang, Chao Li, and Wayne Xin Zhao. 2021. LibCity: An Open Library for Traffic Prediction. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems (SIGSPATIAL '21). Association for Computing Machinery, New York, NY, USA, 145–148. DOI:https://doi.org/10.1145/3474717.3483923