--- license: apache-2.0 --- # 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](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 can be used for research purposes. Before you download the dataset, please read these terms. And [Code link](xxx). 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. Below is the detailed field of each sub-dataset. # 3. Description ## 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 ## 4.1 Route Prediction | Method | HR@3 | KRC | LSD | ED | |--------------|----------------|----------------|----------------|----------------| | TimeGreedy | 59.38 | 39.65 | 5.30 | 2.26 | | DistanceGreedy | 60.81 | 42.78 | 5.46 | 1.95 | | OR-Tools | 62.23 | 44.87 | 4.77 | 1.90 | | LightGBM | 70.33 | 54.44 | 3.36 | 1.94 | | FDNET | 68.55 ± 0.10 | 51.99 ± 0.12 | 4.28 ± 0.02 | 1.89 ± 0.01 | | DeepRoute | 71.57 ± 0.07 | 56.33 ± 0.13 | 3.31 ± 0.06 | 1.86 ± 0.01 | | Graph2Route | 71.41 ± 0.04 | 56.46 ± 0.02 | 3.18 ± 0.01 | 1.88 ± 0.01 | ## 4.2 Estimated Time of Arrival Prediction | Model | 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 ± 2.2 | 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 | 5.26 | 11.39 | | 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}, } ```