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Publication Type : Journal Article
Publisher : Journal of Intelligent Transportation Systems, Taylor & Francis.
Source : Journal of Intelligent Transportation Systems, Taylor & Francis, Volume 22, Issue 5, p.446-461 (2018)
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2018
Abstract : Dynamic Traffic Assignment (DTA) is a mathematical framework that with a System Optimal (SO) objective is often used for long-term transport planning, design, and traffic management. However, the conventional SO-DTA formulation gives optimal solutions having an unrealistic vehicle Holding–Back (HB) property. Existing approaches in the literature aiming to resolve the HB problem are either computationally intractable or suffer from recursive parameter selection problem. In addition, most of the existing Signal Control (SC) models considered in the DTA formulation are mixed-integer or nonlinear in nature that are not scalable for large networks. With an exception, there exists a linear signal control model that can only set signal control cycle-length equal to DTA time-slot duration, and thus trades the accuracy of the SO-DTA solution for a more realistic cycle-length. In this article, we address the above issues by proposing a linear Non-Holding-Back SO-DTA with SC (NHB DTA-SC) formulation for single destination networks. The embedded signal control in the proposed framework enables us to set realistic cycle-length using any DTA time-slot (i.e., flexible time-scale). We find that the time-scale has a significant impact on traffic density which affects vehicle-discharged emissions. To this end, we develop a novel linear Emission-Based DTA with SC (EB DTA-SC) formulation that obtains NHB flows as well as lowest possible emission. Our results show that there is a 32% difference between emission estimated by 60-second and 5-second time-scales.
Cite this Research Publication : Tarikul Islam, Hai Le Vu, Manoj Kumar Panda, and Dong Ngoduy, “A study of realistic dynamic traffic assignment with signal control, time-scale, and emission”, Journal of Intelligent Transportation Systems, vol. 22, no. 5, pp. 446-461, 2018.