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---
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.
# 2 Download
LaDe is composed of two subdatasets: i) [LaDe-D](https://huggingface.co/datasets/Anonymous-LaDe/Anonymous/tree/main/delivery), which comes from the package delivery scenario.
ii) [LaDe-P](https://huggingface.co/datasets/Anonymous-LaDe/Anonymous/tree/main/pickup), 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](https://anonymous.4open.science/r/Anonymous-64B3/). 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 |
|