Experiments on bike traffic-flow prediction

  • Experiment Setting

    • Dataset

|        Attributes        | **New York City** |   **Chicago**   |     **DC**      |
| :----------------------: | :---------------: | :-------------: | :-------------: |
|        Time span         |  2013.03-2017.09  | 2013.06-2017.12 | 2013.07-2017.09 |
| Number of riding records |    49,669,470     |   13,826,196    |   13,763,675    |
|    Number of stations    |        827        |       586       |       531       |

Following shows a map-visualization of bike stations in NYC, the latest built stations have deeper color.

In the data preprocessing stage, we removed the stations with average daily traffic flow smaller than 1, since predictions for these stations are not significant in real life. The remaining station number are 717, 444 and 378 for NYC, Chicago and DC, respectively.

  • Network parameter setting (STMeta model)

    Following shows the parameters we used and a short explanation of the parameter meaning. To know more about the parameter, please refer to the API reference.

  • Experiment Results

    • CG-GCLSTM Only use correlation graph in the model

    • DG-GCLSTM Only use distance graph in the model

    • IG-GCLSTM Only use interaction graph in the model

Only closeness feature (will delete in future version)

NYC Chicago DC
HM 6.79734 4.68078 3.66747
ARIMA 5.60477 3.79739 3.31826
HMM 5.42030 3.79743 3.20889
XGBoost 5.32069 3.75124 3.14101
LSTM 5.13307 3.69806 3.14331
CG-GCLSTM 4.64375 3.38255 2.87655
DG-GCLSTM 4.67169 3.51243 2.98404
IG-GCLSTM 4.77809 3.45625 2.68370
ST_MGCN (Multi-Graph) 4.41732
STMeta 4.22640 3.02301 2.58584

Latest result

NYC Chicago DC
HM(params searched) 3.99224 (C1-P1-T3) 2.97693 (C1-P1-T2) 2.63165 (C2-P1-T3)
XGBoost 4.14909 3.02530 2.73286
GBRT 3.94348 2.85847 2.63935
LSTM 3.78497 2.79078 2.54752
DG-GCLSTM 3.63207 2.71876 2.53863
IG-GCLSTM 3.78816 2.70131 2.46214
CG-GCLSTM 3.69656 2.79812 2.45815
STMeta-V1 3.50475 2.65511 2.42582
STMeta-V2 3.43870 2.66379 2.41111
  • Model training records

    Following data was collected on a Windows PC with CPU : Interl i7 8700K, Memory: 32 GB, GPU: Nvidia GTX 1080Ti.

NYC City SingleGraph-GCLSTM(Average) STMeta ST_MGCN
Number of trainable variables 249245
Converaged training time 51 hours
  • Source Code

Use the ./Experiment/STMeta_Master_Bike.py to train the model or view evaluation results.

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