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

Byung Do Chung; Tao Yao; Bo Zhang

doi:10.1007/s11067-011-9157-8

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

Byung Do Chung, Tao Yao, Bo Zhang

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

24 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 language	English
Pages (from-to)	167-181
Number of pages	15
Journal	Networks and Spatial Economics
Volume	12
Issue number	1
DOIs	https://doi.org/10.1007/s11067-011-9157-8
Publication status	Published - 2012 Mar

Bibliographical note

Funding Information:
Acknowledgment This work was partially supported by the grant awards CMMI-0824640 and CMMI-0900040 from the National Science Foundation and the grant awards from the Mid-Atlantic Universities Transportation Center (MAUTC).

All Science Journal Classification (ASJC) codes

Software
Computer Networks and Communications
Artificial Intelligence

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

Abstract

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