Experiment Setting on different datasets

STMeta Version

Version NameTemporal Feature ProcessTemporal Merge MethodMulti-Graph Merge Method
STMeta-V1GCLSTMGALGAL
STMeta-V2GCLSTMConcat&DenseGAL
STMeta-V3DCRNNGALGAL

By default, we use STMeta-V1 to run LSTM and single graph model tests.

Search Space

ModelSearch Space
HMCT: 0~6, PT: 0~7, TT: 0~4
ARIMACT:168,p:3, d:0, q:0
XGBoostCT: 0~12, PT: 0~14, TT: 0~4, estimater: 10~200, depth: 2~10
GBRTCT: 0~12, PT: 0~14, TT: 0~4, estimater: 10~200, depth: 2~10

Results on Bike

Dataset Statistics

AttributesNew York CityChicagoDC
Time span2013.03-2017.092013.07-2017.092013.07-2017.09
Number of riding records49,100,69413,130,96913,763,675
Number of stations820585532

Experiment Setting

Experiment Results

 NYCChicagoDC
HM3.99224 1-1-32.97693 1-1-22.63165 2-1-3
ARIMA(C)5.60928 168-3-0-03.83584 168-3-0-03.60450 168-3-0-0
XGBoost4.12407 12-10-0-20-52.92569 9-7-0-20-52.65671 12-14-2-20-5
GBRT3.99907 12-7-4-100-52.84257 12-14-2-100-52.61768 12-14-2-100-5
ST_MGCN (G/DCI)3.723802.883002.48560
DCRNN(G/D C)4.186663.277593.08616
LSTM (C)4.556773.370042.91518
STMeta-V13.504752.655112.42582
STMeta-V23.438702.663792.41111
STMeta-V33.478342.661802.38844

Results on DiDi

Dataset Statistics

AttributesXi'anChengdu
Time span2016.10-2016.112016.10-2016.11
Number of riding records5,922,9618,439,537
Number of stations256256

Experiment Setting

Experiment Results

 Xi’anChengdu
HM6.18623 1-1-27.35477 0-1-4
ARIMA(C)9.47478 168-3-0-012.52656 168-3-0-0
XGBoost6.73346 12-0-2-10-57.738369-14-4-20-2
GBRT6.44639 9-0-2-50-27.58831 12-7-2-50-5
ST-ResNet6.084767.14638
ST_MGCN (G/DCI)5.874567.03217
DCRNN(G/D C)8.2025411.50550
LSTM (C)7.3997010.11386
STMeta-V1 (G/DCI)5.891547.06246
STMeta-V2(G/DCI)5.755967.09710
STMeta-V3(G/DCI)5.955077.04358

Results on Metro

Dataset Statistics

AttributesChongqingShanghai
Time span2016.08-2017.072016.07-2016.09
Number of riding records409,277,117333,149,034
Number of stations113288

Experiment Setting

Experiment Results

 ChongqingShanghai
HM120.30723 0-1-4197.970920-1-4
ARIMA(C)578.18563 168-3-0-0792.1597 168-3-0-0
XGBoost117.05069 9-14-2-200-5185.004473-7-0-50-5
GBRT113.92276 12-10-4-200-5186.74502 12-10-0-100-2
ST_MGCN (G/DCI)118.86668181.55171
DCRNN(G/D C)122.31121326.97357
LSTM (C)196.175732368.8468
STMeta-V1 (G/DCI)92.74990151.11746
STMeta-V2(G/DCI)98.86152158.21953
STMeta-V3(G/DCI)101.7806156.58867

The period and trend features are more obvious in Metro dataset, so the performance is poor if only use closeness feature.

Results on Charge-Station

Dataset Statistics

AttributesBeijing
Time span2018.03-2018.08
Number of riding records1,272,961
Number of stations629

Experiment Setting

Experiment Results

 Beijing
HM1.01610 2-0-2
ARIMA(C)0.98236 168-3-0-0
XGBoost0.83381 12-7-0-20-2
GBRT0.82814 12-10-0-100-2
ST_MGCN (G/DC)0.82714
DCRNN(G/D C)0.98871
LSTM (C)1.58560
STMeta-V1 (G/DC)0.815518
STMeta-V2(G/DC)0.82144
STMeta-V3(G/DC)0.81541