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Some Thoughts on Future Directions for Managing Uncertainty in Stochastic Traffic Models Abstract Only

机译：关于随机交通模型中不确定性管理未来方向的一些思考

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摘要

A rapidly expanding range of traffic and transportation applications call for accurate dynamicrnmodelling of traffic flow due to their potential impact on community and environmental decisionrnmaking. The complexity of these applications dictates that detailed traffic simulation models arernincreasingly being used for such purposes.rnThese models, having either stochastic inputs or stochastic model components, yieldrnstochastic outputs, which is relevant and a required feature being reality inherently uncertain. Inrnorder to consider a model valid, therefore, the analyst has to verify that the uncertainty in thernmodel outputs be close enough to the uncertainty in the real world. Though apparently obvious,rnthe requirement has nontrivial implications as it calls for a structured process of thern‘management of the modelling uncertainty’, intended as the identification, quantification andrnreduction of the model uncertainty (De Rocquigny et al. 2008). When applying mathematicalrnmodels in support of policy decision making, this process can be considered as a step of arnbroader practice also referred as “sensitivity auditing”: “a practice of organised scepticismrntoward the inference provided by mathematical models” (Saltelli et al. 2013). Among theserntechniques are those commonly known as uncertainty and sensitivity analysis.
机译：由于交通和运输应用程序对社区和环境决策的潜在影响，因此交通和运输应用程序范围的迅速扩大要求对交通流进行精确的动态建模。这些应用程序的复杂性表明，越来越多的详细交通仿真模型正用于此类目的。这些模型具有随机输入或随机模型成分，会产生随机输出，这是相关的，而所需的功能实际上是不确定的。因此，为了使模型有效，分析人员必须验证模型输出中的不确定性是否足够接近真实世界中的不确定性。尽管显然很明显，但该要求具有非平凡的含义，因为它要求对“模型不确定性的管理”进行结构化的过程，旨在识别，量化和减少模型不确定性（De Rocquigny等人，2008年）。当应用数学模型支持政策决策时，该过程可被视为arnbroader实践的一个步骤，也被称为“敏感性审计”：“一种对数学模型提供的推论进行有组织的怀疑的实践”（Saltelli等，2013）。在现代技术中，有一些通常称为不确定性和敏感性分析。

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