/SIGSPATIAL_2021_GISCUP_ETA_WDRR_DFMRR

SIGSPATIAL 2021 GISCUP ETA Contest

Primary LanguagePython

SIGSPATIAL_2021_GISCUP_ETA_WDR_DEEPFM

Codes for the SIGSPATIAL 2021 GISCUP ETA Contest

Models

  • WDR
  • DeepFM

Experiments

  • Baseline: Wide-Deep-Recurrent (WDR) from Didi's paper Learning to Estimate the Travel Time

  • WDR

    1. Bidirectional LSTM (Bi-LSTM)
    2. Initialize hidden states of Bi-LSTM using embeddings of non-link-cross features, which is also the input to "deep"
    3. Axuiliary LSTM loss (predict link status at arrival time)

  • DeepFM

    1. Bidirectional LSTM (Bi-LSTM)
    2. Initialize hidden states of Bi-LSTM using embeddings of non-link-cross features, which is also the input to "deep"
    3. Axuiliary LSTM loss (predict link status at arrival time)

For all models, we train in 2020.08.16 - 2020.08.27, evaluate in 2020.08.28 - 2020.08.29, and test in 2020.08.30 - 2020.08.31

Results

The reported MAPE on the test set

WDR DeepFM
no Bi-LSTM 0.1295 0.1323
Bi-LSTM (1) 0.1318 0.1325
Bi-LSTM + initialize LSTM (2) 0.1290 0.1292
Bi-LSTM + initialize LSTM (2) + auxiliary loss (3) 0.1279 0.1277