Freeway Work Zone Traffic Delay and Cost Optimization Model
Publication: Journal of Transportation Engineering
Volume 129, Issue 3
Abstract
A new freeway work zone traffic delay and cost optimization model is presented in terms of two variables: the length of the work zone segment, and the starting time of the work zone using average hourly traffic data. The total work zone cost defined as the sum of user delay, accident, and maintenance costs is minimized and the number of lane closures, darkness factor, and seasonal variation travel demand normally ignored in prior research are included. To find the global optimum solution, a Boltzmann-simulated annealing neural network is developed to solve the resulting mixed real variable–integer cost optimization problem for short-term work zones. The new model can be used as an intelligent decision support system to (1) find the optimum work zone segment length and optimum starting time; (2) study the impact of various factors such as number of lane closures and darkness; and (3) quickly observe the relation between the total work zone cost and the work zone segment length and starting time in a quantitative and rational way.
Get full access to this article
View all available purchase options and get full access to this article.
References
Ackley, D. H., Hinton, G. E., and Sejnowski, T. J.(1985). “A learning algorithm for Boltzmann machines.” Cogn. Sci., 9, 147–169.
Adeli, H.(2001). “Neural networks in civil engineering: 1989–2000.” Comput.-Aided Civ. Infrastruct. Eng., 16(2), 126–142.
Adeli, H., and Cheng, N.-T.(1993). “Integrated genetic algorithm for optimization of space structures.” ASCE, J. Aerosp. Eng., 6(4), 315–328.
Adeli, H., and Hung, S. L. (1995). Machine learning–Neural networks, genetic algorithms, and fuzzy sets, Wiley, New York.
Adeli, H., and Karim, A. (2001). Construction scheduling, cost optimization, and management—A new model based on neurocomputing and object technologies, E&FN Spon, London.
Adeli, H., and Park, H. S. (1998). Neurocomputing for design automation, CRC, Boca Raton, Fla.
Aleksander, I., and Morton, H. (1991). An introduction to neural computing, Chapman and Hall, London.
Chien, S., and Schonfeld, P.(2001). “Optimal work zone lengths for four-lane highways.” J. Transp. Eng., 127(2), 124–131.
Hagan, M. T., Demuth, H. B., and Beale, M. (1996). Neural network design, PWS, Boston.
Highway Capacity Manual (HCM). (1985). Special Rep. 209, Transportation Research Record, National Research Council, Washington, D.C.
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P.(1983). “Optimization by simulated annealing.” Science, 220, 671–680.
McCoy, P. T., and Mennenga, D. J. (1998). “Optimum length of single-lane closures in work zones on rural four-lane freeways,” Transportation Research Record 1650, Transportation Research Board, National Research Council, Washington, D.C., 55–61.
Mehrotra, K., Mohan, C. K., and Ranka, S. (1997). Elements of artificial neural networks, MIT Press, Cambridge, Mass.
Mitretek. (2000). QuickZone delay estimation program-user guide, Prepared for Federal Highway Administration, Mitretek Systems Inc.
Pham, D. T., and Karaboga, D. (2000). Intelligent optimization techniques—Genetic algorithms, Tabu search, simulated annealing and neural networks, Springer, London.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 129 • Issue 3 • May 2003
Pages: 230 - 241
Copyright
Copyright © 2003 American Society of Civil Engineers.
History
Received: Oct 30, 2001
Accepted: May 16, 2002
Published online: Apr 15, 2003
Published in print: May 2003
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.