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Robust dynamic traffic assignment for single destination networks under demand and capacity uncertainty
Journal of Intelligent Transportation Systems ( IF 2.8 ) Pub Date : 2019-07-23 , DOI: 10.1080/15472450.2019.1638780
Giuseppe C. Calafiore 1, 2 , Marco Ghirardi 3 , Alessandro Rizzo 1
Affiliation  

Abstract In this article, we discuss the system-optimum dynamic traffic assignment (SO-DTA) problem in the presence of time-dependent uncertainties on both traffic demands and road link capacities. Building on an earlier formulation of the problem based on the cell transmission model, the SO-DTA problem is robustly solved, in a probabilistic sense, within the framework of random convex programs (RCPs). Different from traditional robust optimization schemes, which find a solution that is valid for all the values of the uncertain parameters, in the RCP approach we use a fixed number of random realizations of the uncertainty, and we are able to guarantee a priori a desired upper bound on the probability that a new, unseen realization of the uncertainty would make the computed solution unfeasible. The particular problem structure and the introduction of an effective domination criterion for discarding a large number of generated samples enables the computation of a robust solution for medium- to large-scale networks, with low desired violation probability, with a moderate computational effort. The proposed approach is quite general and applicable to any problem that can be formulated through a linear programing model, where the stochastic parameters appear in the constraint constant terms only. Simulation results corroborate the effectiveness of our approach.

中文翻译:

需求和容量不确定下单目标网络的稳健动态流量分配

摘要 在本文中,我们讨论了在交通需求和道路连接能力存在时间相关不确定性的情况下的系统最优动态交通分配 (SO-DTA) 问题。在基于单元传输模型的问题的早期公式的基础上,SO-DTA 问题在概率意义上在随机凸程序 (RCP) 的框架内得到了稳健的解决。与传统的鲁棒优化方案不同,后者寻找对所有不确定参数值都有效的解决方案,在 RCP 方法中,我们使用不确定性的固定数量的随机实现,并且我们能够先验地保证期望的上限不确定性的新的、看不见的实现将使计算出的解决方案不可行的概率受到限制。特定的问题结构和用于丢弃大量生成样本的有效支配标准的引入使得能够以中等的计算工作量以较低的期望违规概率为中到大型网络计算稳健的解决方案。所提出的方法非常通用,适用于任何可以通过线性规划模型制定的问题,其中随机参数仅出现在约束常数项中。仿真结果证实了我们方法的有效性。所提出的方法非常通用,适用于任何可以通过线性规划模型制定的问题,其中随机参数仅出现在约束常数项中。仿真结果证实了我们方法的有效性。所提出的方法非常通用,适用于任何可以通过线性规划模型制定的问题,其中随机参数仅出现在约束常数项中。仿真结果证实了我们方法的有效性。
更新日期:2019-07-23
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