Datasetpublic

Cainiao-AI/LaDe-D

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… See the full description on the dataset page: https://huggingface.co/datasets/Cainiao-AI/LaDe-D.

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face

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Dataset Card

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
acceptgpstime	The time of the GPS point whose time is the closest to accept time	Time
acceptgpslng/acceptgpslat	Coordinates when the courier accepts the task	Float
delivery_time	The time when the courier finishes delivering the task	Time
deliverygpstime	The time of the GPS point whose time is the closest to the delivery time	Time
deliverygpslng/deliverygpslat	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:

shell

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