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TaxiBJ

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Jun 28, 2019

TaxiBJ: InFlow/OutFlow, Meteorology and Holidays at Beijing

If you use the data, please cite the following paper.

Junbo Zhang, Yu Zheng, Dekang Qi. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI 2017.

Download data from OneDrive or BaiduYun

Please check the data with md5sum command:

md5sum -c md5sum.txt

TaxiBJ consists of the following SIX datasets:

  • BJ16_M32x32_T30_InOut.h5
  • BJ15_M32x32_T30_InOut.h5
  • BJ14_M32x32_T30_InOut.h5
  • BJ13_M32x32_T30_InOut.h5
  • BJ_Meteorology.h5
  • BJ_Holiday.txt

where the first four files are crowd flows in Beijing from the year 2013 to 2016, BJ_Meteorology.h5 is the Meteorological data, BJ_Holiday.txt includes the holidays (and adjacent weekends) of Beijing.

Note: *.h5 is hdf5 file, one can use the follow code to view the data:

import h5py
f = h5py.File('BJ16_M32x32_T30_InOut.h5')
for ke in f.keys():
    print(ke, f[ke].shape)

Flows of Crowds

File names: BJ[YEAR]_M32x32_T30_InOut.h5, where

  • YEAR: one of {13, 14, 15, 16}
  • M32x32: the Beijing city is divided into a 32 x 32 grid map
  • T30: timeslot (a.k.a. time interval) is equal to 30 minites, meaning there are 48 timeslots in a day
  • InOut: Inflow/Outflow are defined in the following paper [1].

[1] Junbo Zhang, Yu Zheng, Dekang Qi. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI 2017.

Each *.h5 file has two following subsets:

  • date: a list of timeslots, which is associated the data.
  • data: a 4D tensor of shape (number_of_timeslots, 2, 32, 32), of which data[i] is a 3D tensor of shape (2, 32, 32) at the timeslot date[i], data[i][0] is a 32x32 inflow matrix and data[i][1] is a 32x32 outflow matrix.

Example

You can get the data info with following command:

python -c "from deepst.datasets import stat; stat('BJ16_M32x32_T30_InOut.h5')"

The output looks like:

=====stat=====
data shape: (7220, 2, 32, 32)
# of days: 162, from 2015-11-01 to 2016-04-10
# of timeslots: 7776
# of timeslots (available): 7220
missing ratio of timeslots: 7.2%
max: 1250.000, min: 0.000
=====stat=====

Meteorology

File name: BJ_Meteorology.h5, which has four following subsets:

  • date: a list of timeslots, which is associated the following kinds of data.
  • Temperature: a list of continuous value, of which the i^{th} value is temperature at the timeslot date[i].
  • WindSpeed: a list of continuous value, of which the i^{th} value is wind speed at the timeslot date[i].
  • Weather: a 2D matrix, each of which is a one-hot vector (dim=17), showing one of the following weather types:
Sunny = 0,  
Cloudy = 1, 
Overcast = 2, 
Rainy = 3, 
Sprinkle = 4,  
ModerateRain = 5,  
HeavyRain = 6, 
Rainstorm = 7, 
Thunderstorm = 8, 
FreezingRain = 9, 
Snowy = 10,  
LightSnow = 11, 
ModerateSnow = 12, 
HeavySnow = 13, 
Foggy = 14,  
Sandstorm = 15, 
Dusty = 16, 

Holiday

File name: BJ_Holiday.txt, which inclues a list of the holidays (and adjacent weekends) of Beijing.

Each line a holiday with the data format [yyyy][mm][dd]. For example, 20150601 is June 1st, 2015.