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An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 6 new columns (time_window_end, pickup_gps_lat, pickup_gps_lng, pickup_time, pickup_gps_time, time_window_start) and 4 missing columns (delivery_time, delivery_gps_time, delivery_gps_lat, delivery_gps_lng). This happened while the csv dataset builder was generating data using hf://datasets/Cainiao-AI/LaDe/pickup/pickup_cq.csv (at revision 23362be42af39fa212cfb29514a6de770c5fe77a) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Error code:   UnexpectedError

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order_id
int64
region_id
int64
city
string
courier_id
int64
lng
float64
lat
float64
aoi_id
int64
aoi_type
int64
accept_time
string
accept_gps_time
string
accept_gps_lng
float64
accept_gps_lat
float64
delivery_time
string
delivery_gps_time
string
delivery_gps_lng
float64
delivery_gps_lat
float64
ds
int64
2,031,782
10
Chongqing
73
108.71571
30.90228
50
14
10-22 10:26:00
10-22 10:26:00
108.71826
30.95587
10-22 17:04:00
10-22 17:04:00
108.66361
30.96702
1,022
4,285,071
10
Chongqing
3,605
108.71639
30.90269
50
14
09-07 10:13:00
09-07 10:13:00
108.71791
30.95635
09-09 15:44:00
09-09 15:44:00
108.71644
30.90266
907
4,056,800
10
Chongqing
3,605
108.71645
30.90259
50
14
06-26 09:49:00
06-26 09:49:00
108.71798
30.95635
06-27 16:03:00
06-27 16:03:00
108.71647
30.90251
626
3,589,481
10
Chongqing
3,605
108.7165
30.90347
50
14
09-11 11:01:00
09-11 11:01:00
108.71823
30.95596
09-13 17:14:00
09-13 17:14:00
108.7165
30.90341
911
2,752,329
10
Chongqing
3,605
108.71608
30.90409
50
14
10-01 09:52:00
10-01 09:52:00
108.7182
30.95598
10-01 18:30:00
10-01 18:30:00
108.71413
30.90397
1,001
659,996
10
Chongqing
3,605
108.71644
30.9047
50
14
08-08 19:01:00
08-08 19:01:00
108.71796
30.9563
08-11 10:50:00
08-11 10:50:00
108.71632
30.90479
808
4,481,765
10
Chongqing
3,605
108.71605
30.9041
50
14
09-30 10:00:00
09-30 10:00:00
108.71824
30.95583
09-30 16:38:00
09-30 16:38:00
108.71429
30.90416
930
2,365,752
10
Chongqing
3,605
108.71633
30.90266
50
14
09-30 10:00:00
09-30 10:00:00
108.71826
30.95585
09-30 18:38:00
09-30 18:38:00
108.71425
30.90416
930
20,671
10
Chongqing
3,605
108.71643
30.90253
50
14
05-20 10:06:00
05-20 10:06:00
108.71795
30.95621
05-21 15:30:00
05-21 15:30:00
108.71643
30.9025
520
965,648
10
Chongqing
3,605
108.71554
30.90256
50
14
08-10 10:52:00
08-10 10:52:00
108.71797
30.9563
08-12 15:50:00
08-12 15:50:00
108.71542
30.90243
810
4,486,215
10
Chongqing
3,605
108.71543
30.9039
50
14
10-17 09:39:00
10-17 09:39:00
108.71816
30.95588
10-19 16:58:00
10-19 16:58:00
108.71545
30.90394
1,017
1,984,854
10
Chongqing
3,605
108.71631
30.90296
50
14
10-03 10:05:00
10-03 10:05:00
108.71825
30.95582
10-03 18:06:00
10-03 18:06:00
108.71422
30.90408
1,003
2,334,342
10
Chongqing
3,605
108.71604
30.90409
50
14
10-05 10:21:00
10-05 10:21:00
108.71821
30.95593
10-07 15:18:00
10-07 15:18:00
108.71422
30.90403
1,005
2,395,322
10
Chongqing
3,605
108.71598
30.90405
50
14
05-13 09:22:00
05-13 09:22:00
108.72268
30.95712
05-15 09:11:00
05-15 09:11:00
108.71604
30.90393
513
629,776
10
Chongqing
3,605
108.71649
30.90336
50
14
07-26 10:21:00
07-26 10:21:00
108.71793
30.95622
07-27 16:35:00
07-27 16:35:00
108.71654
30.90349
726
397,825
10
Chongqing
3,605
108.71581
30.90413
50
14
07-07 09:53:00
07-07 09:53:00
108.71791
30.9563
07-09 16:26:00
07-09 16:26:00
108.71593
30.90406
707
2,742,862
10
Chongqing
3,605
108.71548
30.90314
50
14
10-16 10:02:00
10-16 10:02:00
108.71814
30.95584
10-18 15:26:00
10-18 15:26:00
108.71554
30.90322
1,016
1,952,141
10
Chongqing
1,326
108.79847
31.21657
67
14
05-08 09:11:00
05-08 09:11:00
108.72256
30.95727
05-10 16:10:00
05-10 16:10:00
108.7225
30.95731
508
3,667,238
10
Chongqing
1,326
109.06912
31.12534
126
14
05-05 09:03:00
05-05 09:03:00
108.72266
30.9572
05-06 14:28:00
05-06 14:28:00
108.72246
30.9573
505
1,734,009
10
Chongqing
1,326
109.01224
31.12924
126
14
06-27 08:42:00
06-27 08:42:00
108.71805
30.95639
06-29 16:22:00
06-29 16:22:00
108.72254
30.95712
627
3,098,203
10
Chongqing
1,635
108.71797
30.94364
296
14
07-10 08:33:00
07-10 08:33:00
108.71801
30.95637
07-10 13:24:00
07-10 13:24:00
108.71809
30.9426
710
356,619
10
Chongqing
1,635
108.71979
30.9413
296
14
09-09 09:04:00
09-09 09:04:00
108.71803
30.95629
09-09 10:49:00
09-09 10:49:00
108.7197
30.94235
909
1,484,207
10
Chongqing
1,635
108.72106
30.94164
296
14
10-19 08:29:00
10-19 08:29:00
108.7182
30.95598
10-19 10:11:00
10-19 10:11:00
108.72307
30.94201
1,019
2,628,104
10
Chongqing
1,635
108.71779
30.94341
296
14
10-28 10:08:00
10-28 10:08:00
108.71829
30.95598
10-28 16:36:00
10-28 16:36:00
108.71806
30.9426
1,028
3,602,373
10
Chongqing
1,635
108.71776
30.94351
296
14
10-04 08:51:00
10-04 08:51:00
108.71817
30.95594
10-04 13:45:00
10-04 13:45:00
108.71792
30.94255
1,004
4,241,487
10
Chongqing
1,635
108.72049
30.94159
296
14
09-12 08:50:00
09-12 08:50:00
108.71823
30.95595
09-12 10:36:00
09-12 10:36:00
108.72138
30.94177
912
15,020
10
Chongqing
1,635
108.72059
30.94154
296
14
10-28 10:05:00
10-28 10:05:00
108.71827
30.95581
10-28 11:58:00
10-28 11:58:00
108.72128
30.9417
1,028
3,619,671
10
Chongqing
1,635
108.7177
30.94341
296
14
10-14 08:47:00
10-14 08:47:00
108.71821
30.95582
10-14 13:33:00
10-14 13:33:00
108.71796
30.94267
1,014
2,800,580
10
Chongqing
1,635
108.71699
30.94418
296
14
09-13 08:33:00
09-13 08:33:00
108.71819
30.95595
09-13 13:49:00
09-13 13:49:00
108.71798
30.94236
913
4,480,417
10
Chongqing
1,635
108.71725
30.94339
296
14
06-08 09:17:00
06-08 09:17:00
108.71796
30.95622
06-08 15:17:00
06-08 15:17:00
108.71783
30.94258
608
1,778,761
10
Chongqing
1,635
108.71684
30.94365
296
14
05-29 09:33:00
05-29 09:33:00
108.71806
30.95627
05-29 15:09:00
05-29 15:09:00
108.71726
30.94652
529
1,442,393
10
Chongqing
1,635
108.71695
30.94378
296
14
10-27 09:33:00
10-27 09:33:00
108.71814
30.95592
10-27 16:03:00
10-27 16:03:00
108.71672
30.94365
1,027
3,800,594
10
Chongqing
1,635
108.71963
30.94127
296
14
10-19 08:32:00
10-19 08:32:00
108.71814
30.95599
10-19 13:56:00
10-19 13:56:00
108.7137
30.94528
1,019
2,074,315
10
Chongqing
1,635
108.71901
30.94355
296
14
10-18 08:39:00
10-18 08:39:00
108.71828
30.95595
10-18 10:37:00
10-18 10:37:00
108.71963
30.94245
1,018
1,227,214
10
Chongqing
1,635
108.71964
30.94128
296
14
08-26 08:56:00
08-26 08:56:00
108.71791
30.9563
08-26 10:44:00
08-26 10:44:00
108.71945
30.94215
826
2,999,896
10
Chongqing
1,635
108.71833
30.94146
296
14
09-09 09:20:00
09-09 09:20:00
108.71799
30.95631
09-09 16:22:00
09-09 16:22:00
108.718
30.94263
909
1,626,606
10
Chongqing
1,635
108.71946
30.94215
296
14
08-14 08:40:00
08-14 08:40:00
108.71807
30.95629
08-14 09:55:00
08-14 09:55:00
108.71972
30.94234
814
308,445
10
Chongqing
1,635
108.71652
30.94456
296
14
05-21 08:52:00
05-21 08:52:00
108.71795
30.95621
05-21 14:16:00
05-21 14:16:00
108.71794
30.94253
521
4,192,593
10
Chongqing
1,635
108.71839
30.94151
296
14
05-19 10:24:00
05-19 10:24:00
108.71806
30.95625
05-19 11:51:00
05-19 11:51:00
108.71952
30.94269
519
3,766,518
10
Chongqing
1,635
108.72105
30.94159
296
14
06-02 11:06:00
06-02 11:06:00
108.718
30.9563
06-02 14:50:00
06-02 14:50:00
108.71975
30.94211
602
3,003,321
10
Chongqing
1,635
108.71768
30.94358
296
14
10-29 10:02:00
10-29 10:02:00
108.71813
30.95593
10-29 15:58:00
10-29 15:58:00
108.718
30.94255
1,029
3,235,537
10
Chongqing
1,635
108.71729
30.94329
296
14
06-14 09:07:00
06-14 09:07:00
108.71795
30.95637
06-14 13:25:00
06-14 13:25:00
108.71797
30.94251
614
2,633,460
10
Chongqing
1,635
108.71736
30.94333
296
14
10-25 09:45:00
10-25 09:45:00
108.71821
30.95599
10-25 15:51:00
10-25 15:51:00
108.71804
30.94258
1,025
868,940
10
Chongqing
1,635
108.71948
30.94218
296
14
05-25 10:51:00
05-25 10:51:00
108.71798
30.95639
05-25 13:41:00
05-25 13:41:00
108.7197
30.94217
525
1,028,189
10
Chongqing
1,635
108.71966
30.94127
296
14
10-23 09:14:00
10-23 09:14:00
108.71819
30.95592
10-23 11:05:00
10-23 11:05:00
108.72352
30.94021
1,023
2,225,872
10
Chongqing
1,635
108.7177
30.94344
296
14
10-08 08:58:00
10-08 08:58:00
108.71829
30.95594
10-08 14:59:00
10-08 14:59:00
108.71791
30.94254
1,008
2,283,695
10
Chongqing
1,635
108.7183
30.94158
296
14
10-20 08:23:00
10-20 08:23:00
108.71814
30.9559
10-20 09:40:00
10-20 09:40:00
108.71944
30.9422
1,020
2,837,284
10
Chongqing
1,635
108.7177
30.94358
296
14
10-16 09:30:00
10-16 09:30:00
108.71825
30.95588
10-16 14:03:00
10-16 14:03:00
108.71809
30.9426
1,016
1,563,847
10
Chongqing
1,635
108.7211
30.94161
296
14
10-16 09:31:00
10-16 09:31:00
108.71814
30.95585
10-16 10:50:00
10-16 10:50:00
108.72292
30.94222
1,016
4,386,436
10
Chongqing
1,635
108.72112
30.94164
296
14
10-31 09:24:00
10-31 09:24:00
108.71827
30.95583
10-31 11:20:00
10-31 11:20:00
108.72286
30.9421
1,031
3,997,837
10
Chongqing
1,635
108.71695
30.94424
296
14
09-30 08:39:00
09-30 08:39:00
108.71819
30.95597
09-30 13:36:00
09-30 13:36:00
108.71803
30.9426
930
3,277,016
10
Chongqing
1,635
108.71827
30.94141
296
14
06-02 11:26:00
06-02 11:26:00
108.71791
30.95627
06-02 14:44:00
06-02 14:44:00
108.71982
30.94237
602
377,057
10
Chongqing
1,635
108.71764
30.94346
296
14
09-21 09:17:00
09-21 09:17:00
108.71827
30.95599
09-21 14:23:00
09-21 14:23:00
108.71791
30.94259
921
807,845
10
Chongqing
1,635
108.71774
30.94355
296
14
10-27 09:57:00
10-27 09:57:00
108.71814
30.95587
10-27 16:37:00
10-27 16:37:00
108.71795
30.9426
1,027
4,266,115
10
Chongqing
1,635
108.72102
30.94166
296
14
10-31 09:17:00
10-31 09:17:00
108.71822
30.95591
10-31 11:14:00
10-31 11:14:00
108.71972
30.94249
1,031
870,510
10
Chongqing
1,635
108.71724
30.94332
296
14
10-31 09:23:00
10-31 09:23:00
108.71826
30.95596
10-31 15:38:00
10-31 15:38:00
108.71674
30.94423
1,031
2,897,786
10
Chongqing
1,635
108.71773
30.94296
296
14
10-26 10:00:00
10-26 10:00:00
108.71823
30.95598
10-26 15:45:00
10-26 15:45:00
108.71787
30.94261
1,026
1,478,138
10
Chongqing
1,635
108.71767
30.94353
296
14
09-18 08:34:00
09-18 08:34:00
108.71818
30.95585
09-18 14:33:00
09-18 14:33:00
108.71795
30.94267
918
3,939,876
10
Chongqing
1,635
108.72351
30.94107
296
14
10-27 09:57:00
10-27 09:57:00
108.71821
30.95597
10-27 12:01:00
10-27 12:01:00
108.7238
30.94208
1,027
2,273,200
10
Chongqing
1,635
108.71768
30.94355
296
14
09-03 09:49:00
09-03 09:49:00
108.71802
30.9563
09-03 14:34:00
09-03 14:34:00
108.71803
30.9426
903
1,411,013
10
Chongqing
1,635
108.71909
30.94348
296
14
10-12 08:54:00
10-12 08:54:00
108.71821
30.95599
10-12 10:26:00
10-12 10:26:00
108.71971
30.94233
1,012
2,623,086
10
Chongqing
1,635
108.72206
30.9416
296
14
10-03 08:47:00
10-03 08:47:00
108.71824
30.95592
10-03 10:26:00
10-03 10:26:00
108.72311
30.93981
1,003
3,349,653
10
Chongqing
1,635
108.72119
30.94147
296
14
08-07 08:44:00
08-07 08:44:00
108.71804
30.95639
08-07 09:51:00
08-07 09:51:00
108.72307
30.93982
807
102,905
10
Chongqing
1,635
108.71772
30.94343
296
14
10-22 10:28:00
10-22 10:28:00
108.71829
30.95594
10-22 17:07:00
10-22 17:07:00
108.71809
30.94262
1,022
4,206,100
10
Chongqing
1,635
108.71722
30.94332
296
14
10-17 08:50:00
10-17 08:50:00
108.71818
30.95597
10-17 13:57:00
10-17 13:57:00
108.71793
30.94253
1,017
1,529,956
10
Chongqing
1,635
108.71769
30.94352
296
14
10-18 08:47:00
10-18 08:47:00
108.71821
30.9559
10-18 14:28:00
10-18 14:28:00
108.71805
30.94262
1,018
1,621,057
10
Chongqing
1,635
108.72111
30.94159
296
14
10-29 09:48:00
10-29 09:48:00
108.71828
30.95599
10-29 11:38:00
10-29 11:38:00
108.71961
30.94228
1,029
3,443,201
10
Chongqing
1,635
108.72242
30.94151
296
14
07-14 09:04:00
07-14 09:04:00
108.71793
30.95635
07-14 10:38:00
07-14 10:38:00
108.72277
30.94202
714
199,430
10
Chongqing
1,635
108.71963
30.94127
296
14
09-28 09:05:00
09-28 09:05:00
108.7182
30.95597
09-28 10:30:00
09-28 10:30:00
108.71985
30.94241
928
4,240,373
10
Chongqing
1,635
108.72114
30.94156
296
14
10-10 09:06:00
10-10 09:06:00
108.71825
30.95598
10-10 10:54:00
10-10 10:54:00
108.71982
30.94237
1,010
2,566,777
10
Chongqing
1,635
108.71763
30.94351
296
14
10-23 09:16:00
10-23 09:16:00
108.71815
30.95591
10-23 14:54:00
10-23 14:54:00
108.71788
30.94266
1,023
4,181,188
10
Chongqing
1,635
108.71979
30.94135
296
14
07-16 09:27:00
07-16 09:27:00
108.71798
30.95621
07-16 11:05:00
07-16 11:05:00
108.71973
30.94231
716
1,736,306
10
Chongqing
1,635
108.71766
30.94341
296
14
08-29 08:46:00
08-29 08:46:00
108.71804
30.95623
08-29 14:18:00
08-29 14:18:00
108.7181
30.9426
829
595,517
10
Chongqing
1,635
108.71824
30.94148
296
14
07-04 09:04:00
07-04 09:04:00
108.71798
30.95633
07-04 11:32:00
07-04 11:32:00
108.71972
30.94241
704
4,191,331
10
Chongqing
1,635
108.72056
30.94148
296
14
09-04 08:57:00
09-04 08:57:00
108.71796
30.95632
09-04 10:16:00
09-04 10:16:00
108.72233
30.93955
904
101,246
10
Chongqing
1,635
108.72217
30.94152
296
14
06-19 09:44:00
06-19 09:44:00
108.71798
30.95625
06-19 17:08:00
06-19 17:08:00
108.72336
30.94
619
3,298,992
10
Chongqing
1,635
108.72215
30.94162
296
14
10-12 08:53:00
10-12 08:53:00
108.71824
30.95596
10-12 10:33:00
10-12 10:33:00
108.72361
30.94023
1,012
3,899,253
10
Chongqing
1,635
108.72352
30.94094
296
14
10-24 09:27:00
10-24 09:27:00
108.71823
30.95583
10-24 11:07:00
10-24 11:07:00
108.72362
30.94282
1,024
100,963
10
Chongqing
1,635
108.72043
30.94156
296
14
06-03 10:20:00
06-03 10:20:00
108.71803
30.95635
06-03 16:50:00
06-03 16:50:00
108.71963
30.9423
603
1,146,472
10
Chongqing
1,635
108.72111
30.94158
296
14
10-22 10:30:00
10-22 10:30:00
108.71825
30.95593
10-22 12:04:00
10-22 12:04:00
108.71968
30.94246
1,022
252,323
10
Chongqing
1,635
108.71961
30.94126
296
14
09-03 09:46:00
09-03 09:46:00
108.71799
30.9563
09-03 11:06:00
09-03 11:06:00
108.71946
30.94211
903
4,374,564
10
Chongqing
1,635
108.71947
30.94229
296
14
10-19 08:34:00
10-19 08:34:00
108.71811
30.95588
10-19 10:02:00
10-19 10:02:00
108.71957
30.9423
1,019
2,949,356
10
Chongqing
1,635
108.72059
30.94153
296
14
10-19 08:35:00
10-19 08:35:00
108.7182
30.95589
10-19 10:19:00
10-19 10:19:00
108.72319
30.9399
1,019
3,860,525
10
Chongqing
1,635
108.71735
30.94349
296
14
10-19 08:31:00
10-19 08:31:00
108.71823
30.95586
10-19 13:32:00
10-19 13:32:00
108.71807
30.94272
1,019
1,086,282
10
Chongqing
1,635
108.71808
30.94245
296
14
07-31 08:49:00
07-31 08:49:00
108.71794
30.95636
07-31 13:41:00
07-31 13:41:00
108.71803
30.94261
731
3,765,009
10
Chongqing
1,635
108.7194
30.94224
296
14
09-01 08:50:00
09-01 08:50:00
108.71809
30.95634
09-01 10:02:00
09-01 10:02:00
108.71962
30.9423
901
3,191,559
10
Chongqing
1,635
108.71727
30.94339
296
14
10-11 08:30:00
10-11 08:30:00
108.71823
30.95584
10-11 13:56:00
10-11 13:56:00
108.71798
30.94245
1,011
686,728
10
Chongqing
1,635
108.71768
30.94345
296
14
08-28 08:07:00
08-28 08:07:00
108.71806
30.95636
08-28 13:46:00
08-28 13:46:00
108.71786
30.94246
828
3,098,891
10
Chongqing
1,635
108.71766
30.94355
296
14
10-12 08:52:00
10-12 08:52:00
108.71824
30.9559
10-12 14:27:00
10-12 14:27:00
108.71798
30.94251
1,012
673,108
10
Chongqing
1,635
108.71914
30.94326
296
14
05-29 09:38:00
05-29 09:38:00
108.71808
30.95632
05-29 11:22:00
05-29 11:22:00
108.72259
30.94209
529
743,762
10
Chongqing
1,635
108.7197
30.9413
296
14
09-16 09:11:00
09-16 09:11:00
108.71818
30.9559
09-16 11:24:00
09-16 11:24:00
108.71967
30.94226
916
22,134
10
Chongqing
1,635
108.72103
30.94153
296
14
06-16 09:07:00
06-16 09:07:00
108.71797
30.95625
06-16 10:32:00
06-16 10:32:00
108.72378
30.94054
616
667,134
10
Chongqing
1,635
108.71688
30.94364
296
14
06-07 08:43:00
06-07 08:43:00
108.71792
30.95634
06-07 14:46:00
06-07 14:46:00
108.71379
30.94478
607
3,310,572
10
Chongqing
1,635
108.72113
30.94154
296
14
06-26 08:23:00
06-26 08:23:00
108.71799
30.95637
06-26 09:25:00
06-26 09:25:00
108.71981
30.94249
626
4,271,164
10
Chongqing
1,635
108.72115
30.94154
296
14
05-16 09:03:00
05-16 09:03:00
108.71804
30.95634
05-16 11:28:00
05-16 11:28:00
108.72363
30.94041
516
1,501,745
10
Chongqing
1,635
108.71775
30.94349
296
14
10-30 09:00:00
10-30 09:00:00
108.71825
30.95592
10-30 14:23:00
10-30 14:23:00
108.718
30.94251
1,030
303,794
10
Chongqing
1,635
108.71766
30.94346
296
14
08-26 09:01:00
08-26 09:01:00
108.71799
30.95636
08-26 14:52:00
08-26 14:52:00
108.71763
30.94348
826
3,771,847
10
Chongqing
1,635
108.71778
30.94354
296
14
10-13 09:18:00
10-13 09:18:00
108.71823
30.95581
10-13 15:09:00
10-13 15:09:00
108.70933
30.94038
1,013
3,101,905
10
Chongqing
1,635
108.71692
30.94353
296
14
05-06 09:02:00
05-06 09:02:00
108.72264
30.95717
05-06 10:15:00
05-06 10:15:00
108.7159
30.94575
506
3,077,304
10
Chongqing
1,635
108.71776
30.94359
296
14
09-11 09:57:00
09-11 09:57:00
108.71812
30.95592
09-11 15:19:00
09-11 15:19:00
108.71817
30.94255
911
End of preview.

Dataset Download: https://huggingface.co/datasets/Cainiao-AI/LaDe/tree/main
Dataset Website: https://cainiaotechai.github.io/LaDe-website/
Code Link:https://github.com/wenhaomin/LaDe
Paper Link: https://arxiv.org/abs/2306.10675

1 About Dataset

LaDe is a publicly available last-mile delivery dataset with millions of packages from industry. It has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. (2) Comprehensive information, it offers original package information, such as its location and time requirements, as well as task-event information, which records when and where the courier is while events such as task-accept and task-finish events happen. (3) Diversity: the dataset includes data from various scenarios, such as package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations. LaDe.png

2 Download

LaDe is composed of two subdatasets: i) LaDe-D, which comes from the package delivery scenario. ii) LaDe-P, which comes from the package pickup scenario. To facilitate the utilization of the dataset, each sub-dataset is presented in CSV format.

LaDe can be used for research purposes. Before you download the dataset, please read these terms. And Code link. Then put the data into "./data/raw/".
The structure of "./data/raw/" should be like:

* ./data/raw/  
    * delivery    
        * delivery_sh.csv   
        * ...    
    * pickup  
        * pickup_sh.csv  
        * ...  

Each sub-dataset contains 5 csv files, with each representing the data from a specific city, the detail of each city can be find in the following table.

City Description
Shanghai One of the most prosperous cities in China, with a large number of orders per day.
Hangzhou A big city with well-developed online e-commerce and a large number of orders per day.
Chongqing A big city with complicated road conditions in China, with a large number of orders.
Jilin A middle-size city in China, with a small number of orders each day.
Yantai A small city in China, with a small number of orders every day.

3 Description

Below is the detailed field of each sub-dataset.

3.1 LaDe-P

Data field Description Unit/format
Package information
package_id Unique identifier of each package Id
time_window_start Start of the required time window Time
time_window_end End of the required time window Time
Stop information
lng/lat Coordinates of each stop Float
city City String
region_id Id of the Region String
aoi_id Id of the AOI (Area of Interest) Id
aoi_type Type of the AOI Categorical
Courier Information
courier_id Id of the courier Id
Task-event Information
accept_time The time when the courier accepts the task Time
accept_gps_time The time of the GPS point closest to accept time Time
accept_gps_lng/lat Coordinates when the courier accepts the task Float
pickup_time The time when the courier picks up the task Time
pickup_gps_time The time of the GPS point closest to pickup_time Time
pickup_gps_lng/lat Coordinates when the courier picks up the task Float
Context information
ds The date of the package pickup Date

3.2 LaDe-D

Data field Description Unit/format
Package information
package_id Unique identifier of each package Id
Stop information
lng/lat Coordinates of each stop Float
city City String
region_id Id of the region Id
aoi_id Id of the AOI Id
aoi_type Type of the AOI Categorical
Courier Information
courier_id Id of the courier Id
Task-event Information
accept_time The time when the courier accepts the task Time
accept_gps_time The time of the GPS point whose time is the closest to accept time Time
accept_gps_lng/accept_gps_lat Coordinates when the courier accepts the task Float
delivery_time The time when the courier finishes delivering the task Time
delivery_gps_time The time of the GPS point whose time is the closest to the delivery time Time
delivery_gps_lng/delivery_gps_lat Coordinates when the courier finishes the task Float
Context information
ds The date of the package delivery Date

4 Leaderboard

Blow shows the performance of different methods in Shanghai.

4.1 Route Prediction

Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively.

Method HR@3 KRC LSD ED
TimeGreedy 57.65 31.81 5.54 2.15
DistanceGreedy 60.77 39.81 5.54 2.15
OR-Tools 66.21 47.60 4.40 1.81
LightGBM 73.76 55.71 3.01 1.84
FDNET 73.27 ± 0.47 53.80 ± 0.58 3.30 ± 0.04 1.84 ± 0.01
DeepRoute 74.68 ± 0.07 56.60 ± 0.16 2.98 ± 0.01 1.79 ± 0.01
Graph2Route 74.84 ± 0.15 56.99 ± 0.52 2.86 ± 0.02 1.77 ± 0.01

4.2 Estimated Time of Arrival Prediction

Method MAE RMSE ACC@30
LightGBM 30.99 35.04 0.59
SPEED 23.75 27.86 0.73
KNN 36.00 31.89 0.58
MLP 21.54 ± 2.20 25.05 ± 2.46 0.79 ± 0.04
FDNET 18.47 ± 0.25 21.44 ± 0.28 0.84 ± 0.01

4.3 Spatio-temporal Graph Forecasting

Method MAE RMSE
HA 4.63 9.91
DCRNN 3.69 ± 0.09 7.08 ± 0.12
STGCN 3.04 ± 0.02 6.42 ± 0.05
GWNET 3.16 ± 0.06 6.56 ± 0.11
ASTGCN 3.12 ± 0.06 6.48 ± 0.14
MTGNN 3.13 ± 0.04 6.51 ± 0.13
AGCRN 3.93 ± 0.03 7.99 ± 0.08
STGNCDE 3.74 ± 0.15 7.27 ± 0.16

5 Citation

If you find this helpful, please cite our paper:

@misc{wu2023lade,
      title={LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry}, 
      author={Lixia Wu and Haomin Wen and Haoyuan Hu and Xiaowei Mao and Yutong Xia and Ergang Shan and Jianbin Zhen and Junhong Lou and Yuxuan Liang and Liuqing Yang and Roger Zimmermann and Youfang Lin and Huaiyu Wan},
      year={2023},
      eprint={2306.10675},
      archivePrefix={arXiv},
      primaryClass={cs.DB}
}
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