Quantifying travel time variability at a single bottleneck based on stochastic capacity and demand distributions

Mingxin Li, Xuesong Zhou, Nagui M. Rouphail

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Travel time reliability, an essential factor in traveler route and departure time decisions, serves as an important quality of service measure for dynamic transportation systems. This article investigates a fundamental problem of quantifying travel time variability from its root sources: stochastic capacity and demand variations that follow commonly used log-normal distributions. A volume-to-capacity ratio-based travel time function and a point queue model are used to demonstrate how day-to-day travel time variability can be explained from the underlying demand and capacity variations. One important finding is that closed-form solutions can be derived to formulate travel time variations as a function of random demand/capacity distributions, but there are certain cases in which a closed-form expression does not exist and numerical approximation methods are required. This article also uses probabilistic capacity reduction information to estimate time-dependent travel time variability distributions under conditions of non-recurring traffic congestion. The proposed models provide theoretically rigorous and practically useful tools for understanding the causes of travel time unreliability and evaluating the system-wide benefit of reducing demand and capacity variability.

Original languageEnglish (US)
Pages (from-to)79-93
Number of pages15
JournalJournal of Intelligent Transportation Systems: Technology, Planning, and Operations
Volume21
Issue number2
DOIs
StatePublished - Mar 4 2017

Fingerprint

Travel Time
Travel time
Traffic Congestion
Traffic congestion
Log Normal Distribution
Normal distribution
Demand
Numerical Approximation
Closed-form Solution
Approximation Methods
Quality of Service
Queue
Quality of service
Closed-form
Numerical Methods
Roots
Model
Estimate
Demonstrate

Keywords

  • bottleneck
  • stochastic capacity
  • stochastic demand
  • travel time variability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

Cite this

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