减轻危险材料运输风险的基于代理的框架

H. Kanj, J. Flaus
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引用次数: 6

摘要

危险货物运输(DGT)代表了暴露人群、基础设施和环境的技术和环境风险。历史证据表明,公路交通事故可导致以死亡、受伤、疏散、财产损失、环境恶化和交通中断为特征的各种潜在后果。由于这些产品在日常生活活动中的重要性以及对这些材料需求的增加,开发风险分析和缓解工具成为一项战略目标,特别是在法国等大多数货物通过公路运输的国家。基于危险品运输系统DGTS及其相关风险的复杂性(表征风险的因素是时间依赖的,如交通条件、天气条件、事件概率和人口暴露),这种分析只能通过模拟进行。本文描述了一种使用基于代理的建模的通用方法,这是一种有趣的方法,用于建模由自治和相互作用的代理组成的系统,用于风险分析。提出了一种新的通用模型面,用于在智能体模型中表示风险分析和故障树传播,其目标是利用多智能体系统来分析与系统相关的风险,并模拟其在正常模式和降级模式下的行为。该方法用于分析与危险品运输相关的风险,并通过基于代理的模型(确定运输风险最低的最佳道路)将这些风险最小化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Agent-based framework for mitigating hazardous materials transport risk
Dangerous goods transportation (DGT) represents technological and environmental risks for exposed populations, infrastructures and environment. Historical evidence has shown that road-accidents in DGT can lead to various potential consequences characterized by fatalities, injuries, evacuation, property damage, environmental degradation, and traffic disruption. Due to the importance of these products in everyday civil life activities and the increase in demand for these materials, developing tools for risk analysis and mitigation becomes a strategic goal in particular in those countries, like France, in which the majority of goods are transported by road. Based on the complexity of the dangerous goods transportation system DGTS and its related risk (factors that characterized risks are time dependent as traffic conditions, weather conditions, incident probability and population exposure), this analysis can only be made via simulation. This paper describes a generic approach to use agent-based modeling, an interesting approach to modeling systems comprised of autonomous and interacting agents, for risk analysis. It presents a novel generic model facet for representing risk analysis and fault tree propagation in an agent model, where the goal is to analyze the risk related to a system and to simulate its behavior in normal and degraded mode by using multi-agents systems. This approach is used to analyze the risks related to dangerous goods transportation and to minimize these risks by using agent-based model (identifying the best road that having the minimum risk level for transport).
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