Multi-scenario optimization approach for assessing the impacts of advanced traffic information under realistic stochastic capacity distributions

Mingxin Li, Nagui M. Rouphail, Monirehalsadat Mahmoudi, Jiangtao Liu, Xuesong Zhou

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

In this study, to incorporate realistic discrete stochastic capacity distribution over a large number of sampling days or scenarios (say 30–100 days), we propose a multi-scenario based optimization model with different types of traveler knowledge in an advanced traveler information provision environment. The proposed method categorizes commuters into two classes: (1) those with access to perfect traffic information every day, and (2) those with knowledge of the expected traffic conditions (and related reliability measure) across a large number of different sampling days. Using a gap function framework or describing the mixed user equilibrium under different information availability over a long-term steady state, a nonlinear programming model is formulated to describe the route choice behavior of the perfect information (PI) and expected travel time (ETT) user classes under stochastic day-dependent travel time. Driven by a computationally efficient algorithm suitable for large-scale networks, the model was implemented in a standard optimization solver and an open-source simulation package and further applied to medium-scale networks to examine the effectiveness of dynamic traveler information under realistic stochastic capacity conditions.

Original languageEnglish (US)
Pages (from-to)113-133
Number of pages21
JournalTransportation Research Part C: Emerging Technologies
Volume77
DOIs
StatePublished - Apr 1 2017

Keywords

  • Risk-sensitive route choice behavior
  • Stochastic road capacity
  • Traffic assignment
  • Travel time variability
  • Value of dynamic traveler information

ASJC Scopus subject areas

  • Automotive Engineering
  • Transportation
  • Computer Science Applications
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Multi-scenario optimization approach for assessing the impacts of advanced traffic information under realistic stochastic capacity distributions'. Together they form a unique fingerprint.

  • Cite this