Traffic Accident Risk Forecasting using Contextual Vision Transformers

TL;DR

This paper introduces a novel contextual vision transformer for traffic accident risk forecasting, effectively capturing spatio-temporal features, achieving higher accuracy, and significantly reducing computational costs compared to existing methods.

Contribution

The paper proposes a unified, end-to-end framework using a contextual vision transformer that improves accuracy and efficiency in traffic accident risk prediction.

Findings

Achieved roughly 2% improvement in RMSE over state-of-the-art methods.

Outperformed existing techniques on two large-scale datasets.

Required 23 times less computational resources than previous approaches.

Abstract

Recently, the problem of traffic accident risk forecasting has been getting the attention of the intelligent transportation systems community due to its significant impact on traffic clearance. This problem is commonly tackled in the literature by using data-driven approaches that model the spatial and temporal incident impact, since they were shown to be crucial for the traffic accident risk forecasting problem. To achieve this, most approaches build different architectures to capture the spatio-temporal correlations features, making them inefficient for large traffic accident datasets. Thus, in this work, we are proposing a novel unified framework, namely a contextual vision transformer, that can be trained in an end-to-end approach which can effectively reason about the spatial and temporal aspects of the problem while providing accurate traffic accident risk predictions. We evaluate and compare the performance of our proposed methodology against baseline approaches from the literature across two large-scale traffic accident datasets from two different geographical locations. The results have shown a significant improvement with roughly 2\% in RMSE score in comparison to previous state-of-art works (SoTA) in the literature. Moreover, our proposed approach has outperformed the SoTA technique over the two datasets while only requiring 23x fewer computational requirements.