Regulating Hazardous-materials Transportation with Behavioral Modeling of Drivers

Su, Lui; Kwon, Changhyun · 2018 · ROSA P / University of South Florida. Department of Civil and Environmental Engineering

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Summary

This paper addresses the problem of regulating hazardous materials (hazmat) transportation networks to minimize the risk of accidents and environmental damage. The authors identify a gap in existing literature: while previous studies often assume hazmat carriers choose the shortest path or use expected risk measures, they fail to account for the probabilistic nature of driver route choices and the severe consequences of low-probability, high-impact accidents. To address this, the study proposes a network design framework that incorporates driver behavioral modeling and risk-averse measures. The methodology formulates the problem as a bi-level optimization model. The upper level determines which road segments to ban for hazmat traffic to minimize risk, while the lower level predicts carrier route choices. Unlike deterministic models, the lower level utilizes the Random Utility Model (RUM), specifically the Multinomial Logit (MNL) model, to represent the probabilistic route choices of drivers. For the upper level, the authors replace the standard expected risk measure with Conditional Value-at-Risk (CVaR), a coherent risk measure that accounts for the tail of the loss distribution. This approach simultaneously mitigates uncertainty in both driver behavior and accident consequences. The resulting model is a nonlinear programming problem, which the authors solve using a computational scheme involving a discrete Golden Section line search to identify optimal risk thresholds and Benders decomposition to handle the mixed-integer linear programming subproblems. The study validates the proposed model through a case study on the real road network of Ravenna, Italy, involving 105 nodes, 134 arcs, and 31 shipments of various hazmat types (methanol, chlorine, gasoline, and LPG). The results demonstrate that the CVaR-based approach effectively protects the network from undesirable route choices that could lead to severe consequences. The computational scheme proves capable of generating high-quality feasible solutions for large-scale networks, even when exact optimal solutions are computationally intensive. The significance of this work lies in its integration of behavioral modeling with advanced risk management in transportation planning. By applying CVaR to stochastic route-choice problems, the authors provide a more robust tool for regulators to design road bans that account for the non-optimal, probabilistic behavior of drivers. This contributes to the field by offering a method to mitigate catastrophic risks that traditional expected-value models might overlook, thereby enhancing public safety and environmental protection in hazmat transportation.

Key finding

Integrating conditional value-at-risk with probabilistic route choice models in network design yields superior risk mitigation for hazardous materials transportation compared to deterministic shortest-path assumptions.

Methodology

modeling

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• exposure measurement

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• Theoretical Contribution: computational model