Evaluation of incident management impacts using stochastic dynamic traffic assignment

Anil Yazici, Camille Kamga, Kaan Ozbay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a dynamic traffic assignment (DTA) formulation with probabilistic capacity constraints is suggested in order to incorporate accident-induced random capacity reductions into evaluation of incident management strategies. For this purpose, a cell transmission model (CTM) based system optimal dynamic traffic assignment (SODTA) formulation is used as the underlying network model. Hypothetical scenarios are devised in which the potential incident management (IM) strategies are assumed to reduce either the average or the variation of the incident duration. For each case, a small scale Monte Carlo simulation is also performed and compared with the analytic results of the stochastic DTA model. It was shown that the stochastic DTA model not only provides the changes in total system travel time within the reliability measure, but it also provides the analytical results which requires significantly less computational burden. The model also incorporates the impacts of rerouting which is not possible with a queuing theory based analysis on a single link. The results also show that rather than reducing the average duration, comparable delay reductions can be achieved by reducing the variance while keeping the average accident duration unchanged. Hence, IM strategies, solely targeting average duration may be deemed not to be successful, yet, they may be an effective policy to reduce delay. Overall, the proposed model provides a computationally efficient network-wide analysis of incident induced delay without ignoring the highly stochastic nature of roadway incidents.

Original languageEnglish (US)
Title of host publicationTransportation Research Procedia
PublisherElsevier
Pages186-196
Number of pages11
Volume10
DOIs
StatePublished - 2015

Fingerprint

incident
traffic
evaluation
management
Accidents
accident
Optimal systems
Travel time
travel
scenario
simulation

Keywords

  • Dynamic Traffic Assignment
  • Incident Management
  • Stochastic Programming

ASJC Scopus subject areas

  • Transportation

Cite this

Yazici, A., Kamga, C., & Ozbay, K. (2015). Evaluation of incident management impacts using stochastic dynamic traffic assignment. In Transportation Research Procedia (Vol. 10, pp. 186-196). Elsevier. https://doi.org/10.1016/j.trpro.2015.09.068

Evaluation of incident management impacts using stochastic dynamic traffic assignment. / Yazici, Anil; Kamga, Camille; Ozbay, Kaan.

Transportation Research Procedia. Vol. 10 Elsevier, 2015. p. 186-196.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yazici, A, Kamga, C & Ozbay, K 2015, Evaluation of incident management impacts using stochastic dynamic traffic assignment. in Transportation Research Procedia. vol. 10, Elsevier, pp. 186-196. https://doi.org/10.1016/j.trpro.2015.09.068
Yazici A, Kamga C, Ozbay K. Evaluation of incident management impacts using stochastic dynamic traffic assignment. In Transportation Research Procedia. Vol. 10. Elsevier. 2015. p. 186-196 https://doi.org/10.1016/j.trpro.2015.09.068
Yazici, Anil ; Kamga, Camille ; Ozbay, Kaan. / Evaluation of incident management impacts using stochastic dynamic traffic assignment. Transportation Research Procedia. Vol. 10 Elsevier, 2015. pp. 186-196
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