Genetic Algorithms Based Traffic Signal Optimization at a Congested Intersection

Ping Wang; Qun Yang

doi:10.4028/www.scientific.net/AMM.209-211.814

Paper Titles

Building Hongqiao Integrated Transport Hub to Serve Regional Economy
p.792

Study on Prior to Develop Public Transportation Measures in Dalian
p.799

Research and Development of Coping Disposal System for Incident Suddenly Occurring on Freeway Based on GIS
p.803

A Hierarchical Approach to the Capacitated Single Allocation p-Hub Median Problem with Direct Shipment
p.807

Genetic Algorithms Based Traffic Signal Optimization at a Congested Intersection
p.814

The Efficient Applicability of Highway Alignment Optimization Models
p.818

Analysis of Influence Factors and Countermeasures on the Safety of Hazardous Goods Transportation on Road
p.822

Research on the Equilibrium of Train Crew Scheduling Plan of Motor Train Set
p.827

Analysis on Capacity of Unsignalized Intersection on Urban Arterial Road in Slight Snowy Weather
p.833

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 209-211Genetic Algorithms Based Traffic Signal...

Genetic Algorithms Based Traffic Signal Optimization at a Congested Intersection

Abstract:

The objective of this paper is to investigate genetic algorithms (GA) on traffic signal timing at a congested isolated intersection. The objective function for GA modeling was established on the strategy of minimizing average delay and GA was applied to search for the optimal signal timing. Then microsimulation is used to compare the optimized timings produced by the GA with those obtained for the same intersection using Synchro. Results indicated that applying GA results in lower values of average delay and average number of stops in congested condition than applying Synchro.

You might also be interested in these eBooks

Sustainable Cities Development and Environment

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 209-211)

Pages:

814-817

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.209-211.814

Citation:

Cite this paper

Online since:

October 2012

Authors:

Ping Wang, Qun Yang

Keywords:

Average Delay, Congested, Genetic Algorithm (GA), Intersection, Traffic Signal Optimization

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Schrank, D., Lomax, T. 2009 Urban Mobility Report. Texas Transportation Institute of the Texas A & M University System. (2009).

Google Scholar

[2] Mark D. Foy, Rahim F. Benekohal, and David E. Goldberg. Signal Timing Determination Using Genetic Algorithms. In transportation Research Record 1365, TRB, National Research Council, Washington D.C., pp.108-115. (1992).

Google Scholar

[3] Jinwoo Lee, Baher Abdulhai, Amer Shalaby, and Eui-hwan Chung. Real-Time Optimization for Adaptive Traffic Signal Control Using Genetic Algorithms. Journal of Intelligent Transportation System, pp.111-122. (2005).

DOI: 10.1080/15472450500183649

Google Scholar

[4] Byungkyu Park, Carroll J. Messer, and Thomas Urbanik II. Traffic Signal Optimization under Congested Conditons Genetic Algorithms Approach. In transportation Research Record 1683, TRB, National Research Council, Washington D.C., pp.134-142. (1999).

DOI: 10.3141/1683-17

Google Scholar

[5] Byungkyu Brian, Park, Carroll J. Messer, and Thommas Urbanik II. Enhanced Genetic Algorithms for Signal-Timing Optimization of Congested Intersections. In transportation Research Record 1727, TRB, National Research Council, Washington D.C., pp.32-40. (2000).

DOI: 10.3141/1727-05

Google Scholar

[6] ROESS R P, PRASSAS E S, MCSHANE W R. Traffic Engineering. Third Edition [M]. New Jersey: Prentice Hall, (2004).

Google Scholar

[7] Trafficware. Synchro 6 User Guide. (2003).

Google Scholar

[8] U.S. Department of Transportation, Traffic Analysis Toolbox Volume III: Guidelines for Applying Traffic Microsimulation Modeling Software [M]. Federal Highway Administration, (2004).

Google Scholar