File size: 8,065 Bytes
43340a6 d7c2cf2 43340a6 d7c2cf2 43340a6 d7c2cf2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
---
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},
}
``` |