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Anonymous-LaEx
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Anonymous-LaDe

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10M<n<100M
Tags:
Logistics
Last-mile Delivery
Spatial-Temporal
Graph
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - Logistics
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+ - Last-mile Delivery
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+ - Spatial-Temporal
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+ - Graph
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+ size_categories:
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+ - 10M<n<100M
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+ ---
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+
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+ Dataset Download: https://huggingface.co/datasets/Anonymous-LaEx/Anonymous/tree/main
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+ Code Link:https://anonymous.4open.science/r/Anonymous-64B3/
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+
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+ # 1 About Dataset
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+ **LaEx** is a publicly available last-mile delivery dataset with millions of packages from industry.
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+ It has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation.
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+ (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.
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+ (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.
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+ ![LaDe.png](./img/LaEx.png)
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+
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+
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+
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+ # 2 Download
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+ LaEx is composed of two subdatasets: i) [LaEx-D](https://huggingface.co/datasets/Anonymous-LaEx/Anonymous/tree/main/delivery), which comes from the package delivery scenario.
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+ ii) [LaEx-P](https://huggingface.co/datasets/Anonymous-LaEx/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.
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+
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+ LaEx 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/".
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+ The structure of "./data/raw/" should be like:
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+ ```
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+ * ./data/raw/
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+ * delivery
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+ * delivery_sh.csv
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+ * ...
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+ * pickup
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+ * pickup_sh.csv
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+ * ...
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+ ```
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+
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+ 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.
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+
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+
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+ | City | Description |
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+ |------------|----------------------------------------------------------------------------------------------|
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+ | Shanghai | One of the most prosperous cities in China, with a large number of orders per day. |
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+ | Hangzhou | A big city with well-developed online e-commerce and a large number of orders per day. |
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+ | Chongqing | A big city with complicated road conditions in China, with a large number of orders. |
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+ | Jilin | A middle-size city in China, with a small number of orders each day. |
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+ | Yantai | A small city in China, with a small number of orders every day. |
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+
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+
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+ # 3 Description
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+ Below is the detailed field of each sub-dataset.
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+ ## 3.1 LaEx-P
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+ | Data field | Description | Unit/format |
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+ |----------------------------|----------------------------------------------|--------------|
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+ | **Package information** | | |
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+ | package_id | Unique identifier of each package | Id |
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+ | time_window_start | Start of the required time window | Time |
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+ | time_window_end | End of the required time window | Time |
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+ | **Stop information** | | |
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+ | lng/lat | Coordinates of each stop | Float |
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+ | city | City | String |
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+ | region_id | Id of the Region | String |
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+ | aoi_id | Id of the AOI (Area of Interest) | Id |
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+ | aoi_type | Type of the AOI | Categorical |
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+ | **Courier Information** | | |
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+ | courier_id | Id of the courier | Id |
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+ | **Task-event Information** | | |
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+ | accept_time | The time when the courier accepts the task | Time |
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+ | accept_gps_time | The time of the GPS point closest to accept time | Time |
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+ | accept_gps_lng/lat | Coordinates when the courier accepts the task | Float |
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+ | pickup_time | The time when the courier picks up the task | Time |
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+ | pickup_gps_time | The time of the GPS point closest to pickup_time | Time |
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+ | pickup_gps_lng/lat | Coordinates when the courier picks up the task | Float |
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+ | **Context information** | | |
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+ | ds | The date of the package pickup | Date |
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+
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+
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+ ## 3.2 LaEx-D
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+ | Data field | Description | Unit/format |
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+ |-----------------------|--------------------------------------|---------------|
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+ | **Package information** | | |
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+ | package_id | Unique identifier of each package | Id |
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+ | **Stop information** | | |
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+ | lng/lat | Coordinates of each stop | Float |
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+ | city | City | String |
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+ | region_id | Id of the region | Id |
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+ | aoi_id | Id of the AOI | Id |
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+ | aoi_type | Type of the AOI | Categorical |
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+ | **Courier Information** | | |
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+ | courier_id | Id of the courier | Id |
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+ | **Task-event Information**| | |
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+ | accept_time | The time when the courier accepts the task | Time |
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+ | accept_gps_time | The time of the GPS point whose time is the closest to accept time | Time |
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+ | accept_gps_lng/accept_gps_lat | Coordinates when the courier accepts the task | Float |
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+ | delivery_time | The time when the courier finishes delivering the task | Time |
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+ | delivery_gps_time | The time of the GPS point whose time is the closest to the delivery time | Time |
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+ | delivery_gps_lng/delivery_gps_lat | Coordinates when the courier finishes the task | Float |
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+ | **Context information** | | |
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+ | ds | The date of the package delivery | Date |
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+
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+
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+ # 4 Leaderboard
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+ Blow shows the performance of different methods in Shanghai.
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+ ## 4.1 Route Prediction
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+
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+ Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively.
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+
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+ | Method | HR@3 | KRC | LSD | ED |
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+ |--------------|--------------|--------------|-------------|-------------|
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+ | TimeGreedy | 57.65 | 31.81 | 5.54 | 2.15 |
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+ | DistanceGreedy | 60.77 | 39.81 | 5.54 | 2.15 |
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+ | OR-Tools | 66.21 | 47.60 | 4.40 | 1.81 |
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+ | LightGBM | 73.76 | 55.71 | 3.01 | 1.84 |
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+ | FDNET | 73.27 ± 0.47 | 53.80 ± 0.58 | 3.30 ± 0.04 | 1.84 ± 0.01 |
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+ | DeepRoute | 74.68 ± 0.07 | 56.60 ± 0.16 | 2.98 ± 0.01 | 1.79 ± 0.01 |
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+ | Graph2Route | 74.84 ± 0.15 | 56.99 ± 0.52 | 2.86 ± 0.02 | 1.77 ± 0.01 |
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+
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+
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+ ## 4.2 Estimated Time of Arrival Prediction
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+
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+ | Method | MAE | RMSE | ACC@30 |
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+ | ------ |--------------|--------------|-------------|
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+ | LightGBM | 17.48 | 20.39 | 0.85 |
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+ | SPEED | 23.75 | 27.86 | 0.73 |
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+ | KNN | 21.28 | 25.36 | 0.75 |
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+ | MLP | 21.54 ± 2.20 | 25.05 ± 2.46 | 0.79 ± 0.04 |
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+ | FDNET | 18.47 ± 0.25 | 21.44 ± 0.28 | 0.84 ± 0.01 |
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+
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+
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+ ## 4.3 Spatio-temporal Graph Forecasting
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+
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+
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+ | Method | MAE | RMSE |
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+ |-------|-------------|-------------|
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+ | HA | 4.63 | 9.91 |
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+ | DCRNN | 3.69 ± 0.09 | 7.08 ± 0.12 |
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+ | STGCN | 3.04 ± 0.02 | 6.42 ± 0.05 |
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+ | GWNET | 3.16 ± 0.06 | 6.56 ± 0.11 |
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+ | ASTGCN | 3.12 ± 0.06 | 6.48 ± 0.14 |
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+ | MTGNN | 3.13 ± 0.04 | 6.51 ± 0.13 |
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+ | AGCRN | 3.93 ± 0.03 | 7.99 ± 0.08 |
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+ | STGNCDE | 3.74 ± 0.15 | 7.27 ± 0.16 |
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+
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