Incorporating stochastic road capacity into day-to-day traffic simulation and traveler learning framework: Model development and case study

Anxi Jia, Xuesong Zhou, Mingxin Li, Nagui M. Rouphail, Billy M. Williams

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

A key foundation for developing strategies aimed at improving the efficiency and reliability of an urban transportation network is identifying the locations and impact of system bottlenecks. Although free-flow capacity and queue discharge rates at system bottlenecks have traditionally been modeled as fixed values, they are in fact random variables. Therefore, assessing the operational impact of network bottlenecks requires reliable and realistic tools that account for stochasticity in prebreakdown flow rates and queue discharge rates. Focusing on methodological and analytic enhancements to existing dynamic traffic assignment models, this paper presents a method to seamlessly incorporate stochastic capacity models at freeway bottlenecks and signalized intersections and develops adaptive day-to-day traveler learning and route choice behavioral models under the travel time variability introduced by random capacity variations. To account for different levels of information availability and cognitive limitations of individual travelers, a set of bounded rationality rules are adapted to describe route choice rules for a traffic system with inherent process noise and different information provision strategies. A case study based on a real-world Portland, Oregon, subarea network is presented to illustrate the capabilities of the enhanced simulator and highlight the advantage of modeling stochastic capacity in a dynamic mesoscopic traffic simulator as compared with conventional tools that assume deterministic road capacity.

Original languageEnglish (US)
Pages (from-to)112-121
Number of pages10
JournalTransportation Research Record
Issue number2254
DOIs
StatePublished - Dec 1 2011
Externally publishedYes

Fingerprint

Simulators
Urban transportation
Highway systems
Travel time
Random variables
Flow rate
Availability

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

Cite this

Incorporating stochastic road capacity into day-to-day traffic simulation and traveler learning framework : Model development and case study. / Jia, Anxi; Zhou, Xuesong; Li, Mingxin; Rouphail, Nagui M.; Williams, Billy M.

In: Transportation Research Record, No. 2254, 01.12.2011, p. 112-121.

Research output: Contribution to journalArticle

@article{4dc7e9fc859043b39f4905082d8f9a32,
title = "Incorporating stochastic road capacity into day-to-day traffic simulation and traveler learning framework: Model development and case study",
abstract = "A key foundation for developing strategies aimed at improving the efficiency and reliability of an urban transportation network is identifying the locations and impact of system bottlenecks. Although free-flow capacity and queue discharge rates at system bottlenecks have traditionally been modeled as fixed values, they are in fact random variables. Therefore, assessing the operational impact of network bottlenecks requires reliable and realistic tools that account for stochasticity in prebreakdown flow rates and queue discharge rates. Focusing on methodological and analytic enhancements to existing dynamic traffic assignment models, this paper presents a method to seamlessly incorporate stochastic capacity models at freeway bottlenecks and signalized intersections and develops adaptive day-to-day traveler learning and route choice behavioral models under the travel time variability introduced by random capacity variations. To account for different levels of information availability and cognitive limitations of individual travelers, a set of bounded rationality rules are adapted to describe route choice rules for a traffic system with inherent process noise and different information provision strategies. A case study based on a real-world Portland, Oregon, subarea network is presented to illustrate the capabilities of the enhanced simulator and highlight the advantage of modeling stochastic capacity in a dynamic mesoscopic traffic simulator as compared with conventional tools that assume deterministic road capacity.",
author = "Anxi Jia and Xuesong Zhou and Mingxin Li and Rouphail, {Nagui M.} and Williams, {Billy M.}",
year = "2011",
month = "12",
day = "1",
doi = "10.3141/2254-12",
language = "English (US)",
pages = "112--121",
journal = "Transportation Research Record",
issn = "0361-1981",
publisher = "US National Research Council",
number = "2254",

}

TY - JOUR

T1 - Incorporating stochastic road capacity into day-to-day traffic simulation and traveler learning framework

T2 - Model development and case study

AU - Jia, Anxi

AU - Zhou, Xuesong

AU - Li, Mingxin

AU - Rouphail, Nagui M.

AU - Williams, Billy M.

PY - 2011/12/1

Y1 - 2011/12/1

N2 - A key foundation for developing strategies aimed at improving the efficiency and reliability of an urban transportation network is identifying the locations and impact of system bottlenecks. Although free-flow capacity and queue discharge rates at system bottlenecks have traditionally been modeled as fixed values, they are in fact random variables. Therefore, assessing the operational impact of network bottlenecks requires reliable and realistic tools that account for stochasticity in prebreakdown flow rates and queue discharge rates. Focusing on methodological and analytic enhancements to existing dynamic traffic assignment models, this paper presents a method to seamlessly incorporate stochastic capacity models at freeway bottlenecks and signalized intersections and develops adaptive day-to-day traveler learning and route choice behavioral models under the travel time variability introduced by random capacity variations. To account for different levels of information availability and cognitive limitations of individual travelers, a set of bounded rationality rules are adapted to describe route choice rules for a traffic system with inherent process noise and different information provision strategies. A case study based on a real-world Portland, Oregon, subarea network is presented to illustrate the capabilities of the enhanced simulator and highlight the advantage of modeling stochastic capacity in a dynamic mesoscopic traffic simulator as compared with conventional tools that assume deterministic road capacity.

AB - A key foundation for developing strategies aimed at improving the efficiency and reliability of an urban transportation network is identifying the locations and impact of system bottlenecks. Although free-flow capacity and queue discharge rates at system bottlenecks have traditionally been modeled as fixed values, they are in fact random variables. Therefore, assessing the operational impact of network bottlenecks requires reliable and realistic tools that account for stochasticity in prebreakdown flow rates and queue discharge rates. Focusing on methodological and analytic enhancements to existing dynamic traffic assignment models, this paper presents a method to seamlessly incorporate stochastic capacity models at freeway bottlenecks and signalized intersections and develops adaptive day-to-day traveler learning and route choice behavioral models under the travel time variability introduced by random capacity variations. To account for different levels of information availability and cognitive limitations of individual travelers, a set of bounded rationality rules are adapted to describe route choice rules for a traffic system with inherent process noise and different information provision strategies. A case study based on a real-world Portland, Oregon, subarea network is presented to illustrate the capabilities of the enhanced simulator and highlight the advantage of modeling stochastic capacity in a dynamic mesoscopic traffic simulator as compared with conventional tools that assume deterministic road capacity.

UR - http://www.scopus.com/inward/record.url?scp=84863275292&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84863275292&partnerID=8YFLogxK

U2 - 10.3141/2254-12

DO - 10.3141/2254-12

M3 - Article

AN - SCOPUS:84863275292

SP - 112

EP - 121

JO - Transportation Research Record

JF - Transportation Research Record

SN - 0361-1981

IS - 2254

ER -