📅 19/02/2025: WE RELEASE THE EXPANDED VERSIONS OF BOTH DATASETS AND FIXED THE ISSUES WITH THE PREVIOUS DATASET HERE. Unzip password is ‘dataset’
📅 18/04/2024: FULL SLIDE IS UPDATED.
📅 18/03/2024: A SHORT VIDEO OF PRESENTATION AT WWW2024.
This is a Pytorch implementation of JGRM as described in the paper More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning.
We build this project by Python 3.7.12 with the following packages:
torch==1.7.1
torch-geometric==2.3.1
scikit-learn==1.0.2
pandas==1.3.5
pickleshare==0.7.5
shapely==2.0.1
faiss-cpu==1.7.4
We recently revisited the project and discovered that the previously released dataset could not be imported due to transmission issues. We have re-uploaded and verified the relevant data, and further supplemented the dataset with a 200k version to facilitate comparison for everyone.
You can access our data here 2025.06.21 UPDATED.
The folder contains a total of two datasets, Chengdu and Xi'an. Each folder contains 7 files with the following file directory:
|----Chengdu\
| |----chengdu_1101_1115_data_sample10w.pkl # Training data
| |----chengdu_1101_1115_data_seq_evaluation.pkl # Evaluation data
| |----transition_prob_mat.npy # The transition matrix is obtained from the full dataset, a choice for initializing the road network
| |----init_w2v_road_emb.pt # The section embedding is obtained from the word2vec
| |----edge_geometry.csv # Topology of the road section
| |----edge_features.csv # Attributes of the road section
| |----line_graph_edge_idx.npy # Adjacency matrix
If you have any questions related to the code or the paper, feel free to email mazhipeng1024@my.swjtu.edu.cn.
@inproceedings{10.1145/3589334.3645644,
author = {Ma, Zhipeng and Tu, Zheyan and Chen, Xinhai and Zhang, Yan and Xia, Deguo and Zhou, Guyue and Chen, Yilun and Zheng, Yu and Gong, Jiangtao},
title = {More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning},
year = {2024},
isbn = {9798400701719},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589334.3645644},
doi = {10.1145/3589334.3645644},
booktitle = {Proceedings of the ACM on Web Conference 2024},
pages = {3064–3075},
numpages = {12},
location = {, Singapore, Singapore, },
series = {WWW '24} }