JCLRNT

Code of CIKM'22 paper Jointly Contrastive Learning on Road Network and Trajectory. An unsupervised method for road and trajectory representation utilizing contrastive learning.

Experiment Results

Results on road segment-based tasks

Results on road trajectory-based tasks

Usage

Preparation

1. Download DiDi GAIA dataset from https://outreach.didichuxing.com/appEn-vue/dataList (Update Sept2024: this link has been shut down. For the dataset, please refer to our drive: https://drive.google.com/drive/folders/1P_bSoUXNjifA3uxi8_IsRYco_H0UWyrv?usp=drive_link; We also provide the processed data for reproduction: https://drive.google.com/file/d/1WmdeNLEK7-otzQVleQmr5t_RZW-lZTZ0/view?usp=drive_link)
2. Download and instal map matching tool from https://github.com/cyang-kth/fmm

Preprocess

Run preprocessing to get map data and other features.

python data_processor.py

Train and Evaluate

Train the model and evaluate it on different tasks

python main.py