Anonymous-LaDe / README.md
Anonymous-LaEx's picture
Update README.md
188ed69
metadata
license: apache-2.0
tags:
  - Logistics
  - Last-mile Delivery
  - Spatial-Temporal
  - Graph
size_categories:
  - 10M<n<100M

Dataset Download: https://huggingface.co/datasets/Anonymous-LaEx/Anonymous-LaDe

Code Link:https://anonymous.4open.science/r/Anonymous-64B3/

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 59.81 39.93 5.20 2.24
DistanceGreedy 61.07 42.84 5.35 1.94
OR-Tools 62.50 44.81 4.69 1.88
LightGBM 70.63 54.48 3.27 1.92
FDNET 69.05 ± 0.47 52.72 ± 1.98 4.08 ± 0.29 1.86 ± 0.03
DeepRoute 71.66 ± 0.11 56.20 ± 0.27 3.26 ± 0.08 1.86 ± 0.01
Graph2Route 71.69 ± 0.12 56.53 ± 0.12 3.12 ± 0.01 1.86 ± 0.01
DRL4Route 72.18 ± 0.18 57.20 ± 0.20 3.06 ± 0.02 1.84 ± 0.01

4.2 Estimated Time of Arrival Prediction

Method MAE RMSE ACC@20
LightGBM 17.48 20.39 0.68
SPEED 23.75 27.86 0.58
KNN 21.28 25.36 0.60
MLP 18.58 ± 0.37 21.54 ± 0.34 0.66 ± 0.02
FDNET 18.47 ± 0.31 21.44 ± 0.34 0.67 ± 0.02
RANKETPA 17.18 ± 0.06 20.18 ± 0.08 0.70 ± 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