Update README.md

README.md

@@ -1,4 +1,14 @@
 ---
+license: apache-2.0
+task_categories:
+- time-series-forecasting
+tags:
+- Spatial-Temporal
+- Graph
+- Logistic
+- Last-mile Delivery
+size_categories:
+- 10M<n<100M
 dataset_info:
   features:
   - name: order_id
@@ -54,6 +64,119 @@ dataset_info:
   download_size: 290229555
   dataset_size: 813122686
 ---
-# Dataset Card for "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 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.
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+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-D is the first 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/  
+    * 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:
+
+```shell
+@software{pytorchgithub,
+    author = {xx},
+    title = {xx},
+    url = {xx},
+    version = {0.6.x},
+    year = {2021},
+}
+```