Dynamic Traffic Assignment under Uncertainty

A Distributional Robust Chance-Constrained Approach

Byung Do Chung, Tao Yao, Bo Zhang

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.

Original languageEnglish (US)
Pages (from-to)167-181
Number of pages15
JournalNetworks and Spatial Economics
Volume12
Issue number1
DOIs
StatePublished - Mar 1 2012

Fingerprint

Traffic control
Distribution functions
Sampling
Planning
Experiments
Uncertainty

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

@article{a7f54ac454cb49388a4124d4ee6cf62a,
title = "Dynamic Traffic Assignment under Uncertainty: A Distributional Robust Chance-Constrained Approach",
abstract = "This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.",
author = "Chung, {Byung Do} and Tao Yao and Bo Zhang",
year = "2012",
month = "3",
day = "1",
doi = "10.1007/s11067-011-9157-8",
language = "English (US)",
volume = "12",
pages = "167--181",
journal = "Networks and Spatial Economics",
issn = "1566-113X",
publisher = "Kluwer Academic Publishers",
number = "1",

}

Dynamic Traffic Assignment under Uncertainty : A Distributional Robust Chance-Constrained Approach. / Chung, Byung Do; Yao, Tao; Zhang, Bo.

In: Networks and Spatial Economics, Vol. 12, No. 1, 01.03.2012, p. 167-181.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Dynamic Traffic Assignment under Uncertainty

T2 - A Distributional Robust Chance-Constrained Approach

AU - Chung, Byung Do

AU - Yao, Tao

AU - Zhang, Bo

PY - 2012/3/1

Y1 - 2012/3/1

N2 - This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.

AB - This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.

UR - http://www.scopus.com/inward/record.url?scp=84858700896&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84858700896&partnerID=8YFLogxK

U2 - 10.1007/s11067-011-9157-8

DO - 10.1007/s11067-011-9157-8

M3 - Article

VL - 12

SP - 167

EP - 181

JO - Networks and Spatial Economics

JF - Networks and Spatial Economics

SN - 1566-113X

IS - 1

ER -