This repository is the implementation of our KDD'22 Applied Data Science Track paper:
Interpreting Trajectories from Multiple Views: A Hierarchical Self-Attention Network for Estimating the Time of Arrival. Zebin Chen, Xiaolin Xiao, Yue-Jiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao. KDD 2022.
The code has been tested running under Python 3.8.5, with the following packages installed (along with their dependencies):
- numpy==1.19.2
- scipy==1.6.2
- torch==1.8.0
- tensorboardX==2.2
Here we provide the source code and part of desensitized sample data. You can replace the samples with your own data easily.
The folder is organised as follows:
data-info/
contains:data_info.json
is the statistical information of different route attributes.segment_attrs.json
mapps segID to segment attributes, such as length, functional_level, lane number, et.al.
models/
contains the implementation of HierETA network.samples/
contains some desensitized data, each row represents a unique travel order.dataloading.py
contains tools for loading dataset.log.py
manages log write.main.py
provides full training/testing run on the dataset.utils.py
contains tools for metric calculation.
python main.py
You can perform training/testing or parameter tuning by adjusting the ArgumentParser's
options. Please refer to main.py
for details.
@inproceedings{chen2022hiereta,
title = {Interpreting Trajectories from Multiple Views: A Hierarchical Self-Attention Network for Estimating the Time of Arrival},
author = {Chen, Zebin and Xiao, Xiaolin and Gong, Yue-Jiao and Fang, Jun and Ma, Nan and Chai, Hua and Cao, Zhiguang},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
year = {2022}