A Lightweight Framework for Fast Trajectory Simplification

Introduction

This repository holds source code for the paper "A Lightweight Framework for Fast Trajectory Simplification".

Environment Preparation

Python 3.8.8 (Recommend Anaconda)
CentOS 7.0
Pytorch == 1.8.1+cu111
A Nvidia GPU with cuda 11.7
Please refer to the requirement.txt to install all required packages of Python.

Datasets Description & Preprocessing

Prepare your own trajectory data with the following format and each trajectory is split by "\n". Put the data file into ./datasets and run python preprocess/createDataset.py. The preprocessed datasets will be split into training sets, validation sets, and testing sets which is stored in ./datasets folder.

[[116.51172, 39.92123], [116.51135, 39.93883], [116.69171, 39.85182]]

[[116.69171, 39.85184], [116.6917, 39.85184], [116.6916, 39.85177]]

python preprocess/createDataset.py

Besides, prepare the roadmap data named as "edge.edgelist" which is corresponding to the trajectory. Run the code like python preprocess/node2vec_main.py --input datasets/edge.edgelist --output datasets/beijing.emd to generate your own road node embedding vector with semantic information.

python preprocess/node2vec_main.py --input datasets/edge.edgelist --output datasets/beijing.emd

Running Procedures

Hyperparameters

You can create a config file like model_configs/s3.yamland specify your own hyperparameters for better performance, including learning rate, loss function, min/max compression ratio and some network layer dimension.

Training

Run s3.py, the generated models will be stored in the folder ./checkpoints automatically, and you can pick one model with best performance as your model to do some experiments.

python models/s3.py --config ./model_configs/camera/s3.yaml

Note: you can choose any config yaml file with the format like --config ./model_configs/camera/s3.yaml.

Evaluation

Run debug_outputs.py to generate the compressed trajectory and constructed trajectory and the output will be saved in the folder ./evaluation.

python generate/debug_outputs.py

Citation

If you use our code for research work, please cite our paper as below:

@article{,
  title={A Lightweight Framework for Fast Trajectory Simplification},
  author={Ziquan Fang, Changhao He, Lu Chen, Danlei Hu, Qichen Sun, Linsen Li, Yunjun Gao},
  year={2023},
}

ZJU-DAILY/S3