We have added all the baseline models‘ results and codes to our GitHub page, including ST-GRAT, GMAN, AST-GAT, DCRNN, T-GCN, Etc! Note, we have not added the ST-GRAT and DCRNN results to our paper because the prediction precisions of DCRNN and ST-GRAT (implemented by PyTorch, torch==1.6.0) are further lower than GMAN, but we have gave the results on the dataset as the following shows. If you need, we will add both results to this paper!!! Our task is using the 6 historical time steps to prediction the future 6 target time steps traffic speed of highway, and our time granularity is 15 minutes. If you have any questions, don't hesitate to connect us, thanks!
需要注意的是,需要根据requirements.txt文件中指示的包进行安装,才能正常的运行程序!!!
首先,使用conda创建一个虚拟环境,如‘conda create traffic_speed’;
激活环境,conda activate traffic_speed;
安装环境,需要安装的环境已经添加在requirements.txt中,可以用conda安装,也可以使用pip安装,如:conda install tensorflow==1.12.0;
如果安装的是最新的tensorflow环境,也没问题,tensorflow的包按照以下方式进行导入即可:import tensorflow.compat.v1 as tf
tf.disable_v2_behavior();
点击 run_train.py文件即可运行代码。
需要注意的是,我们在tensorflow的1.12和1.14版本环境中都可以运行
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
6.185242
6.185242
6.185242
6.185242
6.185242
6.185242
RMSE
10.139710
10.139710
10.139710
10.139710
10.139710
10.139710
MAPE
0.126192
0.126192
0.126192
0.126192
0.126192
0.126192
R
0.941854
0.941854
0.941854
0.941854
0.941854
0.941854
R2
0.886221
0.886221
0.886221
0.886221
0.886221
0.886221
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.883667
6.046000
6.104512
6.374540
6.360256
6.168883
RMSE
9.429440
9.798609
9.769539
10.209594
10.030069
9.769304
MAPE
0.102238
0.159162
0.130914
0.110670
0.129478
0.131120
R
0.941224
0.937721
0.937789
0.930736
0.933817
0.937160
R2
0.885876
0.879284
0.879403
0.866113
0.871992
0.878266
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.622419
5.694858
5.636928
5.902129
5.898720
5.929169
RMSE
9.541193
9.459830
9.366811
9.714313
9.744125
9.761787
MAPE
0.111848
0.114599
0.126784
0.138657
0.142691
0.127152
R
0.948368
0.949696
0.950375
0.947028
0.946775
0.946549
R2
0.898497
0.901055
0.902324
0.895883
0.895211
0.894915
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.517776
5.632012
5.777946
5.946635
6.041769
6.079495
RMSE
9.125115
9.284576
9.433199
9.641906
9.626469
9.586535
MAPE
0.133428
0.140966
0.126444
0.119835
0.122028
0.134512
R
0.952999
0.951341
0.949730
0.946881
0.947458
0.947507
R2
0.908157
0.904874
0.901881
0.896313
0.897537
0.897657
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.524607
5.632932
5.771882
5.954062
6.055177
6.096319
RMSE
9.129195
9.285857
9.423597
9.640355
9.636107
9.591586
MAPE
0.132028
0.140429
0.127086
0.122740
0.127745
0.141035
R
0.953077
0.951397
0.949857
0.946916
0.947363
0.947457
R2
0.908075
0.904848
0.902081
0.896346
0.897331
0.897549
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.520539
5.626097
5.768340
5.932466
6.028442
6.048296
RMSE
9.116093
9.267610
9.432268
9.622239
9.603523
9.569430
MAPE
0.137907
0.144544
0.130363
0.123199
0.129686
0.140039
R
0.953136
0.951567
0.949828
0.947121
0.947781
0.947735
R2
0.908339
0.905221
0.901901
0.896736
0.898025
0.898022
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.386255
5.468801
5.567571
5.716054
5.768782
5.749586
RMSE
8.880815
9.022358
9.145341
9.307195
9.271415
9.167632
MAPE
0.130217
0.135699
0.120082
0.114041
0.116743
0.128589
R
0.955524
0.954207
0.952829
0.950642
0.951668
0.952150
R2
0.913009
0.910171
0.907778
0.903387
0.904956
0.906406
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.429035
5.509909
5.627575
5.784230
5.813673
5.804645
RMSE
8.916788
9.076988
9.176666
9.381263
9.304234
9.256170
MAPE
0.130555
0.134097
0.120055
0.115990
0.117240
0.129795
R
0.955434
0.953698
0.952556
0.949963
0.951238
0.951537
R2
0.912303
0.909080
0.907145
0.901843
0.904282
0.904590
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.670292
5.675068
5.746703
5.863256
5.888195
5.831183
RMSE
9.134161
9.173527
9.245162
9.387154
9.317363
9.195807
MAPE
0.138232
0.141509
0.124293
0.118078
0.119514
0.132048
R
0.953061
0.952621
0.951832
0.949719
0.950864
0.951782
R2
0.907975
0.907136
0.905754
0.901720
0.904011
0.905830
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.808297
5.788443
5.794671
5.819892
5.836586
5.867232
RMSE
9.389112
9.371624
9.375622
9.404970
9.420036
9.460167
MAPE
0.137371
0.133297
0.133757
0.134122
0.133960
0.135183
R
0.950188
0.950462
0.950404
0.950128
0.949942
0.949522
R2
0.902386
0.902738
0.902651
0.902037
0.901720
0.900876
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.248924
5.258951
5.329265
5.428566
5.421416
5.314834
RMSE
8.705578
8.784430
8.828416
8.943468
8.809244
8.712925
MAPE
0.124102
0.128212
0.114900
0.110572
0.110653
0.121733
R
0.957302
0.956514
0.956069
0.954439
0.956152
0.956983
R2
0.916408
0.914847
0.914059
0.910791
0.914195
0.915460
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.233438
5.208744
5.256062
5.329036
5.347667
5.204790
RMSE
8.730351
8.795372
8.797104
8.908348
8.818618
8.598270
MAPE
0.130036
0.131432
0.116220
0.107743
0.111887
0.117915
R
0.957150
0.956506
0.956505
0.954913
0.956238
0.958076
R2
0.915932
0.914634
0.914668
0.911490
0.914013
0.917670
GMAN_1 (without decoder on spatiotemporal attention)
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.234585
5.178617
5.211098
5.287373
5.322027
5.223792
RMSE
8.734820
8.744925
8.779705
8.879185
8.817018
8.640688
MAPE
0.130630
0.128092
0.114170
0.106051
0.110179
0.120951
R
0.957045
0.956970
0.956621
0.955153
0.956184
0.957628
R2
0.915846
0.915611
0.915005
0.912069
0.914044
0.916856
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.126567
5.224280
5.335337
5.456082
5.562169
5.662242
RMSE
8.719842
8.848616
8.980233
9.120206
9.243639
9.359533
MAPE
0.117111
0.119548
9.243639
0.124147
0.126001
0.127665
R
0.957088
0.956256
0.126001
0.954598
0.953828
0.953095
R2
0.915784
0.913271
0.910662
0.907852
0.905335
0.902939
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.175827
5.100160
5.149714
5.222380
5.245451
5.150738
RMSE
8.644908
8.640150
8.701153
8.819650
8.724352
8.527315
MAPE
0.126175
0.127834
0.112100
0.104818
0.108805
0.117945
R
0.957921
0.957968
0.957378
0.955765
0.957101
0.958707
R2
0.917550
0.917624
0.916518
0.913230
0.915819
0.919006
STGIN_1 (Multi-steps) without semantic transformation
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.177275
5.109780
5.187932
5.239958
5.257546
5.156742
RMSE
8.631874
8.640737
8.729155
8.806985
8.720966
8.515376
MAPE
0.132412
0.131180
0.117357
0.109338
0.113217
0.120528
R
0.958046
0.957964
0.957082
0.955788
0.957056
0.958787
R2
0.917799
0.917613
0.915979
0.913479
0.915884
0.919233
STGIN_2 (Multi-steps) uses an encoder of GMAN to replace the of ST-Block STGIN
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.195487
5.116568
5.174414
5.246917
5.274146
5.178238
RMSE
8.718395
8.693000
8.744741
8.864868
8.775015
8.614857
MAPE
0.127035
0.129896
0.113813
0.105365
0.108400
0.119232
R
0.957350
0.957625
0.957099
0.955481
0.956771
0.958061
R2
0.916143
0.916614
0.915679
0.912338
0.914838
0.917335
STGIN_3 (Multi-steps) dynamic step-by-step method to replace the generative inference of STGIN
评价指标
6-1 steps
6-2 steps
6-3 steps
6-4 steps
6-5 steps
6-6 steps
MAE
5.182895
5.167289
5.197474
5.254837
5.308890
5.164660
RMSE
8.666809
8.727748
8.737943
8.846064
8.761161
8.555215
MAPE
0.126229
0.129717
0.114406
0.107089
0.108753
0.117827
R
0.957731
0.957168
0.957062
0.955487
0.956708
0.958486
R2
0.917151
0.915942
0.915812
0.912723
0.915129
0.918493
如果你想保证模型的精准度更高,请将 bridge.py 中的代码,改为以下代码 (block set to 1, and input length set to 12)