Hierarchical Bayesian threshold excess model for real-time vehicle-based conflict prediction in dynamic traffic environ-ments
File(s)
Published version
Author(s)
Camarcat, Leah
Feng, Yuxiang
Formosa, Nicolette
Quddus, Mohammed
Type
Journal Article
Abstract
Vehicle-based collision risk assessment methods often exhibit a tradeoff between simplifying assumptions in physics-based models and the interpretability challenges of learning algorithms. To tackle this, methods based on Extreme Value Theory (EVT) have gained momentum in recent years, but there is a lack of studies employing EVT for vehicle-based applications. This paper proposes a new, context-aware conflict prediction algorithm using a hierarchical Bayesian threshold excess model. Contextual traffic data are integrated with vehicle sensor data to improve the robustness and accuracy of the model. The feasibility of real-time deployment is also examined by optimising computational efficiency, leveraging several implementations of the Hamiltonian Monte Carlo No-U-Turn Solver (NUTS). The results demonstrate that including traffic covariates improves the model goodness-of-fit by 4.80% in terms of Deviance Information Criterion, and generalisability with a decrease of 1.36% in mean absolute error. However, partially pooled models, while enhancing goodness-of-fit, result in a reduction of generalisation capabilities. Additionally, the No-U-Turn Sampler compiled in JAX demonstrated sufficient performance for both online training and inference, thus making this methodology a feasible solution for real-time deployment in vehicle-based applications.
Date Issued
2025-12-01
Date Acceptance
2025-05-19
Citation
Communications in Transportation Research, 2025, 5
ISSN
2772-4247
Publisher
Elsevier
Journal / Book Title
Communications in Transportation Research
Volume
5
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd on behalf of Tsinghua University Press. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
License URL
Subjects
Conflict prediction
Connected and autonomous vehicles (CAVs)
Extreme value theory
Hierarchical Bayesian model
Science & Technology
Technology
Transportation
Transportation Science & Technology
Vehicle-based risk assessment
Publication Status
Published
Article Number
100210
Date Publish Online
2025-09-18