Dynamic Traffic Assignment under Uncertainty: A Distributional Robust Chance-Constrained Approach

Byung Do Chung, Tao Yao, Bo Zhang

Research output: Contribution to journalArticle

21 Scopus citations

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

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Dynamic Traffic Assignment under Uncertainty: A Distributional Robust Chance-Constrained Approach'. Together they form a unique fingerprint.

  • Cite this