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10-10 16:33:00
10-10 16:33:00
10-10 18:33:00
126.9645
44.41117
425
3
10-10 18:00:00
10-10 18:00:00
126.96497
44.40956
10-10 16:33:00
126.96454
44.41436
1,010
3,679,521
9
Jilin
393
09-09 15:37:00
09-09 17:00:00
09-09 19:00:00
126.96453
44.41115
425
3
09-09 16:03:00
09-09 16:02:00
126.96564
44.40917
09-09 15:36:00
126.95564
44.41076
909
2,349,157
9
Jilin
393
10-30 14:06:00
10-30 14:06:00
10-30 16:06:00
126.96521
44.41123
425
3
10-30 14:57:00
10-30 14:57:00
126.96544
44.41143
10-30 14:06:00
126.96543
44.41264
1,030
3,583,860
9
Jilin
393
10-30 07:12:00
10-30 09:00:00
10-30 11:00:00
126.96501
44.41332
425
3
10-30 09:39:00
10-30 09:39:00
126.96544
44.41352
10-30 07:12:00
126.93953
44.40628
1,030
534,261
9
Jilin
393
10-28 07:23:00
10-28 11:00:00
10-28 13:00:00
126.96456
44.4112
425
3
10-28 12:05:00
10-28 12:05:00
126.96597
44.41316
10-28 07:23:00
126.94246
44.40718
1,028
664,212
9
Jilin
393
09-19 14:31:00
09-19 17:00:00
09-19 19:00:00
126.9646
44.41313
425
3
09-19 15:24:00
09-19 15:24:00
126.96515
44.40807
09-19 14:31:00
126.96106
44.40512
919
4,432,963
9
Jilin
393
10-17 08:03:00
10-18 09:00:00
10-18 11:00:00
126.96446
44.41122
425
3
10-18 09:06:00
10-18 09:06:00
126.96627
44.40959
null
null
null
1,018
5,944,076
9
Jilin
393
10-21 07:42:00
10-21 09:00:00
10-21 11:00:00
126.9645
44.41124
425
3
10-21 09:21:00
10-21 09:21:00
126.96421
44.41033
10-21 07:33:00
126.94221
44.40708
1,021
3,005,418
9
Jilin
393
10-24 07:55:00
10-24 09:00:00
10-24 23:59:00
126.96447
44.41124
425
3
10-24 10:17:00
null
null
null
10-24 07:55:00
126.96026
44.40612
1,024
5,403,005
9
Jilin
393
09-09 07:54:00
09-09 09:00:00
09-09 11:00:00
126.96501
44.41339
425
3
09-09 09:48:00
09-09 09:48:00
126.96533
44.41316
09-09 07:53:00
126.96173
44.40675
909
4,581,157
9
Jilin
393
09-22 07:48:00
09-22 09:00:00
09-22 11:00:00
126.96453
44.41321
425
3
09-22 08:52:00
09-22 08:51:00
126.96483
44.41323
09-22 07:47:00
126.96438
44.40958
922
4,123,099
9
Jilin
393
10-01 16:15:00
10-01 16:15:00
10-01 18:15:00
126.96447
44.41129
425
3
10-01 16:56:00
null
null
null
null
null
null
1,001
5,756,096
9
Jilin
393
10-05 07:42:00
10-05 09:00:00
10-05 11:00:00
126.96471
44.41146
425
3
10-05 10:23:00
10-05 10:23:00
126.96433
44.41127
null
null
null
1,005
4,998,703
9
Jilin
393
10-13 07:39:00
10-13 09:00:00
10-13 23:59:00
126.96371
44.41188
425
3
10-13 11:33:00
10-13 11:33:00
126.96382
44.41212
10-13 07:38:00
126.96192
44.40684
1,013
3,256,581
9
Jilin
393
10-15 08:45:00
10-15 09:00:00
10-15 11:00:00
126.96442
44.4112
425
3
10-15 10:31:00
10-15 10:31:00
126.96627
44.41226
10-15 08:45:00
126.95838
44.40639
1,015
5,113,922
9
Jilin
393
10-12 12:24:00
10-12 12:24:00
10-12 14:24:00
126.96446
44.41125
425
3
10-12 13:49:00
10-12 13:49:00
126.96646
44.40876
10-12 12:23:00
126.94233
44.4073
1,012
698,412
9
Jilin
393
09-25 07:38:00
09-25 09:00:00
09-25 11:00:00
126.96452
44.41128
425
3
09-25 10:00:00
09-25 09:54:00
126.96438
44.41349
null
null
null
925
3,608,640
9
Jilin
393
10-30 12:55:00
10-30 13:00:00
10-30 15:00:00
126.96412
44.41329
425
3
10-30 13:53:00
10-30 13:53:00
126.96439
44.4134
10-30 12:55:00
126.96648
44.4107
1,030
4,814,039
9
Jilin
393
09-19 11:37:00
09-19 15:00:00
09-19 17:00:00
126.96451
44.41124
425
3
09-19 11:50:00
09-19 11:50:00
126.96563
44.4113
09-19 11:37:00
126.96443
44.4142
919
349,697
9
Jilin
393
10-06 08:25:00
10-06 13:00:00
10-06 15:00:00
126.96517
44.41331
425
3
10-06 10:55:00
10-06 10:54:00
126.96518
44.41416
10-06 08:25:00
126.95987
44.40557
1,006
4,736,956
9
Jilin
393
09-09 14:06:00
09-09 15:00:00
09-09 17:00:00
126.96501
44.41338
425
3
09-09 16:16:00
09-09 16:16:00
126.96569
44.41311
09-09 14:06:00
126.96535
44.41336
909
4,700,520
9
Jilin
393
10-16 11:58:00
10-16 13:00:00
10-16 15:00:00
126.96448
44.41117
425
3
10-16 12:23:00
10-16 12:22:00
126.96485
44.40984
10-16 11:58:00
126.96752
44.41297
1,016
5,967,256
9
Jilin
393
10-24 13:55:00
10-24 15:00:00
10-24 17:00:00
126.96382
44.41252
425
3
10-24 15:11:00
null
null
null
null
null
null
1,024
4,305,560
9
Jilin
393
10-10 11:36:00
10-10 17:00:00
10-10 19:00:00
126.96617
44.41116
425
3
10-10 18:06:00
10-10 18:06:00
126.96609
44.41069
null
null
null
1,010
2,584,767
9
Jilin
393
09-28 08:00:00
09-28 09:00:00
09-28 11:00:00
126.96468
44.41319
425
3
09-28 10:07:00
null
null
null
null
null
null
928
4,943,544
9
Jilin
393
10-26 16:37:00
10-26 17:00:00
10-26 19:00:00
126.9645
44.4112
425
3
10-26 16:41:00
10-26 16:41:00
126.96506
44.40952
10-26 16:37:00
126.96563
44.41037
1,026
4,232,333
9
Jilin
393
09-29 07:49:00
09-29 09:00:00
09-29 11:00:00
126.9651
44.41324
425
3
09-29 09:54:00
null
null
null
null
null
null
929
1,286,057
9
Jilin
393
10-28 09:22:00
10-28 11:00:00
10-28 13:00:00
126.96443
44.41126
425
3
10-28 11:32:00
10-28 11:32:00
126.96566
44.41041
10-28 09:22:00
126.95891
44.40412
1,028
3,050,367
9
Jilin
393
09-10 09:01:00
09-10 11:00:00
09-10 13:00:00
126.96462
44.41312
425
3
09-10 11:04:00
09-10 11:04:00
126.96473
44.41341
09-10 09:00:00
126.95741
44.40377
910
4,897,660
9
Jilin
393
09-09 07:49:00
09-09 09:00:00
09-09 23:59:00
126.9651
44.41331
425
3
09-09 11:43:00
09-09 11:42:00
126.96481
44.4132
09-09 07:49:00
126.96131
44.40681
909
4,416,478
9
Jilin
393
10-16 13:09:00
10-16 15:00:00
10-16 17:00:00
126.96386
44.41238
425
3
10-16 14:32:00
10-16 14:32:00
126.96365
44.41238
10-16 13:09:00
126.96628
44.40823
1,016
5,317,576
9
Jilin
393
10-22 15:31:00
10-22 17:00:00
10-22 19:00:00
126.96383
44.41242
425
3
10-22 15:51:00
10-22 15:50:00
126.9638
44.41238
10-22 15:31:00
126.96048
44.4063
1,022
3,999,209
9
Jilin
393
09-09 12:44:00
09-09 13:00:00
09-09 15:00:00
126.96451
44.41115
425
3
09-09 13:47:00
09-09 13:47:00
126.96604
44.41041
09-09 12:44:00
126.94212
44.4073
909
164,585
9
Jilin
393
09-19 10:31:00
09-19 11:00:00
09-19 13:00:00
126.96464
44.41324
425
3
09-19 11:37:00
09-19 11:37:00
126.9642
44.41303
09-19 10:31:00
126.96298
44.41126
919
1,984,065
9
Jilin
393
10-07 16:33:00
10-07 16:33:00
10-07 18:33:00
126.9651
44.41339
425
3
10-07 17:22:00
10-07 17:21:00
126.96558
44.4143
10-07 16:33:00
126.95828
44.40625
1,007

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.

If you use this dataset for your research, please cite this paper: {xxx}

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-P is the second subdataset from LaDe 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/  
    * pickup  
        * pickup_sh.csv  
        * ...  

LaDe-P contains 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 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

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

To cite this repository:

@software{pytorchgithub,
    author = {xx},
    title = {xx},
    url = {xx},
    version = {0.6.x},
    year = {2021},
}
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