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

被引：0

作者：

Chiou, Suh-Wen ^{[1
]}

机构：

[1] Natl Dong Hwa Univ, Dept Informat Management, Shoufeng Township, Taiwan

来源：

RELIABILITY ENGINEERING & SYSTEM SAFETY | 2024年 / 252卷

关键词：

Complex interdependent traffic systems; Resilience enhancement; Hazmat traffic system; Regular traffic system; Reinforcement learning-based model; HAZMAT NETWORK DESIGN; VALUE-AT-RISK;

D O I：

10.1016/j.ress.2024.110425

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页数：19