Holistic Semantic Representation for Navigational Trajectory Generation

   

This is the official implementation of paper "Holistic Semantic Representation for Navigational Trajectory Generation" [arXiv].

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Framework Overview

HOSER predicts the next spatio-temporal point based on the current state and generates the trajectory between the given OD pair through a search-based method. As illustrated above, HOSER first employs a Road Network Encoder to model the road network at different levels. Based on the road network representation, a Multi-Granularity Trajectory Encoder is proposed to extract the semantic information from the current partial trajectory. To better incorporate prior knowledge of human mobility, a Destination-Oriented Navigator is used to seamlessly integrate the current partial trajectory semantics with the destination guidance.

Requirements

The required packages with Python environment is:

torch
torch_geometric
tqdm
PyYAML
numpy
pandas
scikit-learn
shapely
tensorboard
haversine
loguru

Running

• Data Preprocessing

  First, download the required dataset from Hugging Face and place it in the data folder.

  Next, We use KaHIP, a graph partitioning framework, to partition the road network. Install KaHIP by running the following commands in your terminal:

  git clone --branch v3.17 https://github.com/KaHIP/KaHIP.git
  cd KaHIP
  mkdir build
  cd build 
  cmake ../ -DCMAKE_BUILD_TYPE=Release     
  make
  cd ../..

  Finally, run our script to preprocess the data:

  cd data/preprocess
  python partition_road_network.py
  python get_zone_trans_mat.py
  cd ../..

• Model Training

  python train.py

  • --dataset specifies the dataset, such as Beijing, Porto, or San Francisco
  • --seed specifies the random seed
  • --cuda specifies the GPU device number

• Trajectory Generation

  python gene.py

  • --dataset specifies the dataset, such as Beijing, Porto, or San Francisco
  • --seed specifies the random seed
  • --cuda specifies the GPU device number
  • --num_gene specifies the number of trajectories to generate
  • --processes specifies the number of processes to use when generating trajectories in parallel

Citation

If our work contributes to your research, please consider citing it:

@inproceedings{cao2025hoser,
  title={Holistic Semantic Representation for Navigational Trajectory Generation},
  author={Cao, Ji and Zheng, Tongya and Guo, Qinghong and Wang, Yu and Dai, Junshu and Liu, Shunyu and Yang, Jie and Song, Jie and Song, Mingli},
  booktitle={AAAI Conference on Artificial Intelligence (AAAI)},
  year={2025},
}

Acknowledgments

This work is supported by the Zhejiang Province "JianBingLingYan+X" Research and Development Plan (2024C01114), Zhejiang Province High-Level Talents Special Support Program "Leading Talent of Technological Innovation of Ten-Thousands Talents Program" (No.2022R52046), the Fundamental Research Funds for the Central Universities (No.226-2024-00058), and the Scientific Research Fund of Zhejiang Provincial Education Department (Grant No.Y202457035). Also, we thank Bayou Tech (Hong Kong) Limited for providing the data used in this paper free of charge.

Contact

If you have any question, please feel free to contact

Ji Cao, [email protected].