Experiments on subway traffic-flow prediction

  • Experiment Setting

    • Dataset

|        Attributes        |  **Chongqing**  |  **Shanghai**   |
| :----------------------: | :-------------: | :-------------: |
|        Time span         | 2016.08-2017.07 | 2017.07-2017.09 |
| Number of riding records |    409277117    |    403071370    |
|    Number of stations    |       113       |       288       |
|     Number of lines      |        5        |       14        |

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.

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.

| Parameter  | Value |                  Meaning                   |
| :--------: | :---: | :----------------------------------------: |
|    GLL     |   2   |          number of GCLSTM layers           |
| LSTMUnits  |  256  |       number of hidden units in LSTM       |
|  GALUnits  |  256  |       number of units used in GAtteL       |
|  GALHeads  |   2   |       number of multi-head in GAtteL       |
| DenseUnits |  32   | number of units in penultimate dense layer |
|     TC     |   0   |           correlation threshold            |
|     TD     | 1000  |             distance threshold             |
|     TI     |  500  |           interaction threshold            |
|     lr     | 3e-4  |               learning rate                |
  • Experiment Results

    • STMeta uses correlation graph and neighbor graph.

Chongqing Shanghai
HM 786.01197 1247.56662
ARIMA 660.28378 967.16123
HMM 660.28378 614.19177
XGBoost 289.70050 416.58629
LSTM 239.97653 408.09871
STMeta 138.81463 251.38817

Adding period and closeness feature into mode

Chongqing Shanghai
HM 227.0985
ARIMA
XGBoost 134.9760
GBRT 120.3337
LSTM 124.6012
DG-GCLSTM
IG-GCLSTM
CG-GCLSTM
STMeta

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