A learning optimization for resilience enhancement of risk-informed traffic control system with hazardous materials transportation under uncertainty

A learning optimization is proposed to enhance overall resilience of interdependent traffic systems with hazardous materials (hazmat) transportation under uncertainty. To this end, three interconnected systems are proposed against traffic dynamics and congestion in a complex environment of regular traffic and hazmat carriers incurred with time-varying travel cost. In order to increase control resilience against traffic instability, a reinforcement learning hazmat network is proposed. In order to efficiently improve computation resilience against disruption of risk uncertainty, a resilience-aware learning optimization is proposed to obtain optimal solutions. In order to demonstrate computational performance of proposed approach, numerical experiments are performed at a real-world city under various kinds of traffic conditions. Computational comparisons are numerically made with stochastic and robust optimization at large-scale traffic control systems. As it reported, the proposed learning-based optimization can better greatly enhance overall traffic system resilience, control resilience and computation resilience compared to other models under mixed risk of hazmat transportation and uncertain demand.

• Record URL:
  • https://doi.org/10.1016/j.ress.2024.110425
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0951832024004976
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09518320
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Chiou, Suh-Wen
  • 0000-0003-4743-7544
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 110425
• Serial:
  • Reliability Engineering & System Safety
  • Volume: 252
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0951-8320
  • Serial URL: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety

Subject/Index Terms

• TRT Terms: Freight transportation; Hazardous materials; Highway traffic control systems; Optimization; Reliability; Risk
• Subject Areas: Freight Transportation; Highways; Planning and Forecasting; Safety and Human Factors; Security and Emergencies;

Filing Info

• Accession Number: 01933477
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 14 2024 9:04AM