KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningJuly 2018
Predicting traffic accidents is a crucial problem to improving transportation and public safety as well as safe routing. The problem is also challenging due to the rareness of accidents in space and time and spatial heterogeneity of the environment (e.g., urban vs. rural). Most previous research on traffic accident prediction conducted by domain researchers simply applied classical prediction models on limited data without addressing the above challenges properly, thus leading to unsatisfactory performance. A small number of recent works have attempted to use deep learning for traffic accident prediction. However, they either ignore time information or use only data from a small and homogeneous study area (a city), without handling spatial heterogeneity and temporal auto-correlation properly at the same time.
In this paper we perform a comprehensive study on the traffic accident prediction problem using the Convolutional Long ShortTerm Memory (ConvLSTM) neural network model. A number of detailed features such as weather, environment, road condition, and traffic volume are extracted from big datasets over the state of Iowa across 8 years. To address the spatial heterogeneity challenge in the data, we propose a Hetero-ConvLSTM framework, where a few novel ideas are implemented on top of the basic ConvLSTM model, such as incorporating spatial graph features and spatial model ensemble. Extensive experiments on the 8-year data over the entire state of Iowa show that the proposed framework makes reasonably accurate predictions and significantly improves the prediction accuracy over baseline approaches.
- python 3.7.0
- torch 1.12.1
- matplotlib 3.5.2
- numpy 1.21.5
- sklearn 1.1.1
Modify path and Run HeteroConvLSTM.py
@inproceedings{10.1145/3219819.3219922,
author = {Yuan, Zhuoning and Zhou, Xun and Yang, Tianbao},
title = {Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data},
year = {2018},
isbn = {9781450355520},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3219819.3219922},
doi = {10.1145/3219819.3219922},
booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
pages = {984–992},
numpages = {9},
keywords = {convolutional lstm, deep learning, traffic accident prediction, spatial heterogeneity},
location = {London, United Kingdom},
series = {KDD '18}
}