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SIGSPATIAL-2021-GISCU-2nd-Place-Solution

Competition Page: DiDi-ETA

Final Official Ranking

Ranking Award (Cash Prize) Name MAPE
1 Champion ($10,000) 单模CBT 0.11974
2 Runner-ups
($5,000 each team)
Pims 0.12099
3 华南工农联盟 0.12116
4 Second Runner-ups
($2,500 each team)
机器算命 0.12177
5 pumbaa 0.12198
6 Recognition Award
($1,000 each team)
MobiLab 0.12478
7 悦智AI实验室 0.12511

Team Name: Pims

Team Members: Yunchong Gan, Mingjie Wang, Haoyu Zhang

Quick Start

Prepare Data

Download the dataset from here and change data_dir in dataset.py.

python dataset.py

It will preprocess the original .txt files, convert them into .json files and .pickle files to accelerate the data loading.

Then it will split the whole train dataset into 5Fold and 10Fold.

Train & Test

Train

python train.py

Test

python test.py

Data Ensemble

Use the simple average result to generate the final submission.

The final leaderboard result is the average of 5fold and 10fold (15 model in total).

python merge_submission.py

Details

Model Architecture

The whole model based on WDR, Didi ETA paper in KDD2018.

Wide \
      \
Deep --- concat - MLP - Prediction
      /
RNN -/
 |
 |----Predict Current Link Status

Input

Wide

Name Type Number of Embedding Embedding Dim Description
Simple ETA Numeric 1
Distance Numeric 1
Link Number Numeric 1
Cross Number Numeric 1
Approximate Speed Numeric 1
Weekday Categorical 7 1
Slice ID Categorical 48 1
Distance(Categorical) Categorical 5 1

Deep

Name Type Number of Embedding Embedding Dim Description
Simple ETA Numeric 1
Distance Numeric 1
Link Number Numeric 1
Cross Number Numeric 1
Approximate Speed Numeric 1
Weekday Categorical 7 20
Slice ID Categorical 48 20
Driver ID Categorical depend on dataset 64
Distance(Categorical) Categorical 5 20 Split in 3/7/12/20km

RNN - Link

Name Type Number of Embedding Embedding Dim Description
Link Time Numeric 1
Link Ratio Numeric 1
Link Status(Onehot) Numeric 5
Weekday Categorical 7 20
Slice ID Categorical 288 20 compute with slice id and link/cross time
Link ID Categorical depend on dataset 20

RNN - Cross

Name Type Number of Embedding Embedding Dim Description
Cross Time Numeric 1
Start Link ID Categorical depend on dataset 20
End Link ID Categorical depend on dataset 20

Different from WDR

  • Auxiliary Loss for Link Status Classification
  • Concat result from different branches
  • Random Split KFold
  • Model Ensemble

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