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 Citations (Scopus)

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

Fingerprint

Travel time
traffic
scenario
Sampling
Nonlinear programming
travel
Availability
optimization model
commuter
programming
Travellers
Scenarios
simulation
time
Open source
Route choice
Perfect information
Optimization model
Choice behavior
Simulation

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

Cite this

Multi-scenario optimization approach for assessing the impacts of advanced traffic information under realistic stochastic capacity distributions. / Li, Mingxin; Rouphail, Nagui M.; Mahmoudi, Monirehalsadat; Liu, Jiangtao; Zhou, Xuesong.

In: Transportation Research Part C: Emerging Technologies, Vol. 77, 01.04.2017, p. 113-133.

Research output: Contribution to journalArticle

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