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4,152,509
31
Jilin
435
126.56369
43.86512
570
1
09-07 08:55:00
09-07 08:55:00
126.56611
43.87074
09-07 15:16:00
09-07 15:16:00
126.5639
43.86523
907
2,250,016
31
Jilin
435
126.56327
43.86591
570
1
09-25 12:30:00
09-25 12:30:00
null
null
09-25 16:58:00
09-25 16:58:00
126.56433
43.86586
925
2,610,260
31
Jilin
435
126.56218
43.86652
570
1
09-28 08:21:00
09-28 08:21:00
126.56616
43.87073
09-28 11:51:00
09-28 11:51:00
126.56225
43.86677
928
623,163
31
Jilin
435
126.56184
43.86596
570
1
08-19 08:02:00
08-19 08:02:00
126.5661
43.8708
08-19 13:17:00
08-19 13:17:00
126.56223
43.86606
819
692,200
31
Jilin
1,558
126.56255
43.86558
570
1
10-05 08:01:00
10-05 08:01:00
126.56616
43.87069
10-05 13:55:00
10-05 13:55:00
126.56618
43.87069
1,005
236,676
31
Jilin
1,558
126.5642
43.8656
570
1
10-10 09:14:00
10-10 09:14:00
126.56606
43.87065
10-10 14:36:00
10-10 14:36:00
126.56634
43.8707
1,010
2,237,855
31
Jilin
4,609
126.56192
43.86757
570
1
06-17 12:34:00
06-17 12:34:00
126.56612
43.87063
06-17 13:06:00
06-17 13:06:00
126.56173
43.86785
617
347,620
31
Jilin
4,609
126.56367
43.86539
570
1
06-18 12:18:00
06-18 12:18:00
126.56602
43.8712
06-18 13:03:00
06-18 13:03:00
126.56393
43.86555
618
2,305,133
31
Jilin
4,609
126.56203
43.86668
570
1
06-17 12:34:00
06-17 12:34:00
126.5661
43.87071
06-17 13:12:00
06-17 13:12:00
126.56245
43.86652
617
3,930,116
31
Jilin
4,609
126.56189
43.86751
570
1
06-17 08:25:00
06-17 08:25:00
126.56618
43.87104
06-17 09:05:00
06-17 09:05:00
126.56236
43.86842
617
1,629,224
31
Jilin
4,609
126.56185
43.86753
570
1
06-18 12:24:00
06-18 12:24:00
126.56603
43.87105
06-18 12:49:00
06-18 12:49:00
126.56177
43.86764
618
4,341,092
31
Jilin
949
126.58849
43.84201
819
14
09-10 08:04:00
09-10 08:04:00
126.56602
43.8711
09-10 10:00:00
09-10 10:00:00
126.58986
43.84527
910
188,410
31
Jilin
949
126.5871
43.8406
819
14
07-07 07:53:00
07-07 07:53:00
126.5661
43.87092
07-07 09:50:00
07-07 09:50:00
126.59042
43.83929
707
1,179,290
31
Jilin
949
126.58717
43.84076
819
14
08-28 08:53:00
08-28 08:53:00
126.5661
43.87108
08-28 10:38:00
08-28 10:38:00
126.58821
43.84214
828
3,541,419
31
Jilin
949
126.58712
43.84068
819
14
10-02 09:59:00
10-02 09:59:00
126.58786
43.84245
10-02 10:10:00
10-02 10:10:00
126.58696
43.84051
1,002
1,014,290
31
Jilin
949
126.58747
43.8407
819
14
09-30 08:10:00
09-30 08:10:00
126.56605
43.87106
09-30 11:44:00
09-30 11:44:00
126.58752
43.84069
930
999,258
31
Jilin
949
126.58713
43.84063
819
14
08-29 08:34:00
08-29 08:34:00
126.56598
43.87087
08-29 11:56:00
08-29 11:56:00
126.58644
43.84143
829
1,553,172
31
Jilin
949
126.58731
43.84069
819
14
05-30 14:52:00
05-30 14:52:00
126.56602
43.87124
05-30 16:27:00
05-30 16:27:00
126.58769
43.84103
530
601,532
31
Jilin
949
126.58852
43.84205
819
14
09-02 07:48:00
09-02 07:48:00
126.56618
43.87089
09-02 09:51:00
09-02 09:51:00
126.58761
43.84182
902
2,232,485
31
Jilin
949
126.58728
43.84071
819
14
08-26 08:55:00
08-26 08:55:00
126.56619
43.87092
08-26 10:48:00
08-26 10:48:00
126.58874
43.84182
826
3,277,890
31
Jilin
949
126.58845
43.8415
819
14
09-11 08:47:00
09-11 08:47:00
126.56603
43.87091
09-11 12:21:00
09-11 12:21:00
126.58884
43.84133
911
28,450
31
Jilin
949
126.5884
43.84098
819
14
09-22 07:57:00
09-22 07:57:00
126.56606
43.87103
09-22 09:54:00
09-22 09:54:00
126.58927
43.83943
922
2,157,859
31
Jilin
949
126.58713
43.84061
819
14
09-06 08:21:00
09-06 08:21:00
126.56603
43.87092
09-06 11:15:00
09-06 11:15:00
126.58847
43.83871
906
829,416
31
Jilin
949
126.58861
43.84145
819
14
09-01 09:03:00
09-01 09:03:00
126.56613
43.87114
09-01 10:46:00
09-01 10:46:00
126.58775
43.84524
901

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-D is the first 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/  
    * delivery    
        * delivery_sh.csv   
        * ...    

LaDe-D contains 5 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-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

To cite this repository:

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