README.md

---
license: apache-2.0
tags:
- Spatial-Temporal
- Graph
- Logistic
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: accept_time
    dtype: string
  - name: time_window_start
    dtype: string
  - name: time_window_end
    dtype: string
  - name: lng
    dtype: float64
  - name: lat
    dtype: float64
  - name: aoi_id
    dtype: int64
  - name: aoi_type
    dtype: int64
  - name: pickup_time
    dtype: string
  - name: pickup_gps_time
    dtype: string
  - name: pickup_gps_lng
    dtype: float64
  - name: pickup_gps_lat
    dtype: float64
  - name: accept_gps_time
    dtype: string
  - name: accept_gps_lng
    dtype: float64
  - name: accept_gps_lat
    dtype: float64
  - name: ds
    dtype: int64
  splits:
  - name: pickup_jl
    num_bytes: 54225579
    num_examples: 261801
  - name: pickup_cq
    num_bytes: 243174931
    num_examples: 1172703
  - name: pickup_yt
    num_bytes: 237146694
    num_examples: 1146781
  - name: pickup_sh
    num_bytes: 293399390
    num_examples: 1424406
  - name: pickup_hz
    num_bytes: 436103754
    num_examples: 2130456
  download_size: 443251368
  dataset_size: 1264050348
---
# 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](https://huggingface.co/datasets/Cainiao-AI/LaDe) is composed of two subdatasets: i) [LaDe-D](https://huggingface.co/datasets/Cainiao-AI/LaDe-D), which comes from the package delivery scenario.
ii) [LaDe-P](https://huggingface.co/datasets/Cainiao-AI/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-P is the second subdataset from [LaDe](https://huggingface.co/datasets/Cainiao-AI/LaDe)
LaDe can be used for research purposes. Before you download the dataset, please read these terms. And [Code link](https://github.com/wenhaomin/LaDe). Then put the data into "./data/raw/".  
The structure of "./data/raw/" should be like:  
```
* ./data/raw/  
    * pickup  
        * pickup_sh.csv  
        * ...  
```

LaDe-P contains 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-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         |


# 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},
}
```