license: apache-2.0
tags:
- Spatial-Temporal
- Graph
- Logistic
- Last-mile Delivery
size_categories:
- 10M<n<100M
dataset_info:
features:
- name: order_id
dtype: int64
- name: region_id
dtype: int64
- name: city
dtype: string
- name: courier_id
dtype: int64
- name: lng
dtype: float64
- name: lat
dtype: float64
- name: aoi_id
dtype: int64
- name: aoi_type
dtype: int64
- name: accept_time
dtype: string
- name: accept_gps_time
dtype: string
- name: accept_gps_lng
dtype: float64
- name: accept_gps_lat
dtype: float64
- name: delivery_time
dtype: string
- name: delivery_gps_time
dtype: string
- name: delivery_gps_lng
dtype: float64
- name: delivery_gps_lat
dtype: float64
- name: ds
dtype: int64
splits:
- name: delivery_jl
num_bytes: 5568309
num_examples: 31415
- name: delivery_cq
num_bytes: 168574531
num_examples: 931351
- name: delivery_yt
num_bytes: 36796326
num_examples: 206431
- name: delivery_sh
num_bytes: 267095520
num_examples: 1483864
- name: delivery_hz
num_bytes: 335088000
num_examples: 1861600
download_size: 290229555
dataset_size: 813122686
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},
}