Update README.md

README.md

@@ -1,7 +1,5 @@
 ---
 license: apache-2.0
-task_categories:
-- time-series-forecasting
 tags:
 - Logistics
 - Last-mile Delivery
@@ -10,22 +8,26 @@ tags:
 size_categories:
 - 10M<n<100M
 ---
-# 1. About Dataset
+# 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.
+![LaDe.png](./img/LaDe.png)
 
-If you use this dataset for your research, please cite this paper: {xxx}
+Dataset Website: https://wenhaomin.github.io/LaDe-website/   
+Code Link:https://github.com/wenhaomin/LaDe  
+Paper Link: https://arxiv.org/abs/2306.10675  
 
-# 2. Download
+
+# 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](https://github.com/wenhaomin/LaDe). Then put the data into "/data/raw/".  
-The structure of "/data/raw/" should be like:  
+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/  
+* ./data/raw/  
     * delivery    
         * delivery_sh.csv   
         * ...    
@@ -46,7 +48,7 @@ Each sub-dataset contains 5 csv files, with each representing the data from a sp
 | Yantai     | A small city in China, with a small number of orders every day.                               |
 
 
-# 3. Description
+# 3 Description
 Below is the detailed field of each sub-dataset.
 ## 3.1 LaDe-P
 | Data field                 | Description                                  | Unit/format  |
@@ -98,7 +100,7 @@ Below is the detailed field of each sub-dataset.
 | ds                    | The date of the package delivery      | Date          |
 
 
-# 4. Leaderboard
+# 4 Leaderboard
 Blow shows the performance of different methods in Shanghai.
 ## 4.1 Route Prediction
 
@@ -142,15 +144,16 @@ Experimental results of route prediction. We use bold and underlined fonts to de
 
 
 
-# 5. Citation
-To cite this repository:
+# 5 Citation
+If you find this helpful, please cite our paper:
 
 ```shell
-@software{pytorchgithub,
-    author = {xx},
-    title = {xx},
-    url = {xx},
-    version = {0.6.x},
-    year = {2021},
+@misc{wu2023lade,
+      title={LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry}, 
+      author={Lixia Wu and Haomin Wen and Haoyuan Hu and Xiaowei Mao and Yutong Xia and Ergang Shan and Jianbin Zhen and Junhong Lou and Yuxuan Liang and Liuqing Yang and Roger Zimmermann and Youfang Lin and Huaiyu Wan},
+      year={2023},
+      eprint={2306.10675},
+      archivePrefix={arXiv},
+      primaryClass={cs.DB}
 }
 ```