A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics

Fizza Hussain; Yasir Ali; Yuefeng Li; Md Mazharul Haque

doi:10.1038/s41598-024-54391-4

A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics

Sci Rep. 2024 Feb 19;14(1):4121. doi: 10.1038/s41598-024-54391-4.

Authors

Fizza Hussain¹, Yasir Ali², Yuefeng Li³, Md Mazharul Haque⁴

Affiliations

¹ School of Civil and Environmental Engineering, Faculty of Engineering, Queensland University of Technology, Brisbane, 4001, Australia.
² School of Architecture, Building, Civil Engineering, Loughborough University, Leicestershire, LE11 3TU, UK.
³ School of Computer Science, Faculty of Science, Queensland University of Technology, Brisbane, 4001, Australia.
⁴ School of Civil and Environmental Engineering, Faculty of Engineering, Queensland University of Technology, Brisbane, 4001, Australia. m1.haque@qut.edu.au.

Abstract

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20-25 min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.