Optimization Model for Allocating Resources for Highway Safety Improvement at Urban Intersections
Publication: Journal of Transportation Engineering
Volume 138, Issue 5
Abstract
The authors present a procedure for allocating resources for implementing safety improvement alternatives at urban intersections over a multiyear planning horizon. The procedure, on the basis of optimization techniques, attempts to maximize benefits measured in dollars saved by reducing crashes of different severity categories subject to budgetary and other constraints. It is presented in two parts: (1) a base case including the objective function and a set of mandatory constraints; and (2) additional policy constraints/special features that can be separately incorporated to the base case. Demonstration of the procedure is presented on intersections in the Detroit metropolitan region, in which economic losses resulting from traffic crashes at intersections are estimated to exceed $4 billion annually. The proposed model can allocate resources for safety improvement alternatives over a planning horizon, given a number of independent locations and a number of mutually exclusive alternatives at each location. The policy constraints provide the analyst the flexibility of adding equity, urgency, and other features to the base case. An integer programming technique is applied to solve the demonstration problem.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgements
The authors greatly appreciate the suggestion from the reviewers and their viewpoints have substantially improved the presentation of the research. The authors are thankful to the Michigan Department of Transportation (MDOT) for supporting the alternative “Safety Improvements for Urban Arterials” in 2005–2006 at Wayne State University (WSU), which served as the foundation of this research. The authors would like to thank a number of individuals at WSU for providing valuable information for this research. Subrat Swain and Hassan Sobh, former graduate students of WSU, deserve special mention for their help in the collection and organization of data. The opinions and viewpoints expressed are entirely those of the authors and do not necessarily represent policies and programs of the agencies mentioned in the paper.
References
Banihashemi, M. (2007). “Optimization of highway safety and operation by using crash prediction models with accident modification factors.” Transportation Research Record 2019, Transportation Research Board, Washington, DC, 108–118.
Bierman, H., Bonini, C. P., and Hausman, W. H. (1997). Quantitative analysis for business decisions, Richard Irwin, Homewood, IL.
Chowdhury, M. A., Garber, N. J., and Li, D. (2000). “Multiobjective methodology for highway safety resource allocation.” J. Infrastruct. Syst., 6(4), 138–144.
Cook, W. D., Kazakov, A., and Persaud, B. N. (2001). “Prioritizing highway accident sites: A data employment analysis model.” J. Oper. Res. Soc., 52(3), 303–309.
Craig, L., Brent, G., Felix, W. W. K., and Persaud, B. (2007). “Comparison of alternative methods for identifying sites with high proportion of specific accident types.” Transportation Research Record No. 2019, Transportation Research Board, Washington, DC, 65–73.
Deacon, J. A., Zegeer, C. V., and Deen, R. C. (1975). “Identification of hazardous rural highway locations.” Transportation Research Record No. 543, Transportation Research Board, Washington, DC, 16–33.
Federal Highway Administration (FHWA). (2007). “Desktop reference for crash reduction factors.” Rep. No. FHWA-SA-07-015, Washington, DC.
Goodell-Grivas. (1981). “Highway safety improvement program (HSIP).” FHWA Rep. No.TS-81-218 prepared for Federal Highway Administration, Bloomfield Hills, MI.
Grile, C., Hunter-Zaworski, K. M., and Monsere, C. M. (2005). “Programming safety improvements on pavement resurfacing, restoration and rehabilitation alternatives.” Transportation Research Record No. 1922, Transportation Research Boards, Washington, DC, 73–78.
Harwood, D. W., Burrani, E. R. K., Richard, K. R., McGee, H. W., and Gittings, G. L. (2003a). NCHRP Rep. 486: Systemwide impact of safety and traffic operations design decisions 3R alternatives, Transportation Research Board National Research Council, Washington, DC.
Harwood, D. W., Rabbani, E. R. K., and Richard, K. R. (2003b). “Systemwide optimization of safety improvements for resurfacing, restoration, or rehabilitation alternatives.” Transportation Research Record No. 1840, Transportation Research Board, Washington, DC, 148–157.
Hauer, E. (1996).“Identification of sites with promise.” Transportation Research Record No. 1542, Transportation Research Board, Washington, DC, 54–60.
Hillier, F. S., and Libermann, G. J. (2005). Introduction to operations research, McGraw-Hill Science, New York.
Hossain, M., and Muromachi, Y. (2011). “A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways.” Accid. Anal. Prev., 45, 373–378.
Interactive Highway Safety Design Model (IHSDM) [Computer software]. Federal Highway Administration, Washington, DC.
Kar, K., and Datta, T. K. (2004). “Development of a safety resource allocation model in Michigan.” Transportation Research Record No. 1865, Transportation Research Board, Washington, DC, 64–71.
Karp, R. M. (2010).“Reducibility among combinatorial problems.” 50 years of integer programming, 1958-2008: From the early years to the state-of-the-art, Jünger, M., Liebling, Th. M., Naddef, D., Nemhauser, G. L., Pulleyblank, W. R., Reinelt, G., Rinaldi, G., and Wolsey, L. A., A., L., eds., Springer, New York.
Khasnabis, S., Safi, C., and Mishra, S. (2006a). “Safety improvements for urban arterials.” A Rep. Prepared for Michigan Dept. of Transportation (MDOT), Wayne State Univ., Detroit.
Khasnabis, S., Safi, C., and Mishra, S. (2006b). “Toolbox for safety improvements for urban arterials.” A Rep. Prepared for Michigan Dept. of Transportation (MDOT), Wayne State Univ., Detroit.
Lambert, J. H., Baker, J. A., and Petersen, K. D. (2003). “Decision aid for allocation of transportation funds to guardrails.” Accid. Anal. Prev.AAPVB5, 351(1), 47–57.
Martin, A. (2001). “General mixed integer programming: Computational issues for branch-and-cut algorithms.” Computational combinatorial optimization: Optimal or provably near-optimal solutions, Jünger, M. and Naddef, D., eds., 2241, Springer, Berlin, 1–25.
Mathew, T. V., Khasnabis, S., and Mishra, S. (2010). “Optimal resource allocation among transit agencies for fleet management.” Transp. Res. Part A: Policy Pract., 44(6), 418–432.
Melachrinoudis, E., and Kozanidis, G. (2002). “A mixed integer knapsack model for allocating funds to highway safety improvements.” Transp. Res. Part A: Policy Pract., 36(9), 789–803.
National Highway Traffic Safety Administration (NHTSA). (2010). “Traffic safety facts.” U.S. Department of Transportation. 〈http://www-nrd.nhtsa.dot.gov/Pubs/811402EE.pdf〉 (Jan. 15, 2011).
National Safety Council (NSC). (2008). “Estimating cost of unintentional injuries.” 〈http://www.nsc.org/news_resources/injury_and_death_statistics/Pages/EstimatingtheCostsofUnintentionalInjuries.aspx〉 (Jan. 5, 2010).
Pal, R., and Sinha, K. C. (1998). “Optimization approach to highway safety programming.” Transportation Research Record No. 1640, Transportation Research Board, Washington, DC, 1–9.
Persaud, B. N., and Kazakov, A. (1994). “A procedure for allocating safety resources a safety improvement budget among treatment types.” Accid. Anal. Prev.AAPVB5, 26(1), 121–126.
Premium Solver Platform (PSP) [Computer software]. Frontline Systems, Incline Village, NV.
Rau, S. (1996). Engineering optimization. Wiley Interscience, New York.
Shohet, I. M., and Perelstein, E. (2004). “Decision support model for the allocation of resources in rehabilitation alternatives.” J. Constr. Eng. Manage.JCEMD4, 130(2), 249–257.
Southeast Michigan Council of Goverments (SEMCOG). (2008). “Traffic crashes.” 〈http://semcog.org/Data/Apps/crash.cfm?mcd=8999〉 (June 5, 2008).
Tarko, A. P., and Kanodia, M. (2004). “Effective and fair identification of hazardous locations.” Transportation Research Record No. 1897, Transportation Research Board, Washington, DC, 64–70.
Wolsey, L. A., and Nemhauser, G. L. (1999). Integer and combinatorial optimization, Wiley-Interscience, New York.
Zhu, W., and Lin, G. (2011). “A dynamic convexized method for nonconvex mixed integer term nonlinear programming.” Comput. Oper. Res., 38(12), 1792–1804.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 138 • Issue 5 • May 2012
Pages: 535 - 547
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Feb 18, 2011
Accepted: Sep 26, 2011
Published online: Sep 28, 2011
Published in print: May 1, 2012
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.