Alleviating road network congestion: Traffic pattern optimization using Markov chain traffic assignment

ORCID Identifiers

0000-0002-4072-4500

Document Type

Article

Source of Publication

Computers and Operations Research

Publication Date

11-1-2018

Abstract

© 2018 Elsevier Ltd Exacerbated urban road congestion is a real concern for transportation authorities around the world. Although agent-based simulation and iterative design approaches are typically used to provide solutions that reduce congestion, they fall short of meeting planners’ need for an intelligent network design system. Since Markov chains are remarkably capable of modeling complex, dynamic, and large-scale networks, this paper leverages their theory and proposes a mathematical model based on Markov chain traffic assignment (MCTA) to optimize traffic and alleviate congestion through targeted direction conversions, i.e. two-way to one-way flow conversions. The approach offers an intelligent traffic pattern design system, one which can analyze an existing complex network and suggest solutions taking into consideration network-wide interdependencies. Specifically, the paper presents a binary nonlinear mathematical model to optimize road network traffic patterns using maximum vehicle density. The model is then solved using Genetic Algorithm (GA) optimization methodology, and a fine-tuning search algorithm is proposed to improve upon GA results in terms of solution's practicality and fitness. The approach is applied to a city setting and experimental results are reported. Finally, an application in time-sensitive decision-making is discussed.

ISSN

0305-0548

Publisher

Elsevier Ltd

Volume

99

First Page

191

Last Page

205

Disciplines

Computer Sciences

Keywords

Genetic algorithm, Markov chains, Network design, Operations research, Traffic optimization

Scopus ID

85049731174

Indexed in Scopus

yes

Open Access

no

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