Highway traffic management in incidents of national significance

Burak Eksioglu, PhD; Mingzhou Jin, PhD; Ismail Capar, PhD; Zhuoxiu Zhang, BS; Sandra D. Eksioglu, PhD

doi:10.5055/jem.2008.0002

Authors

Burak Eksioglu, PhD
Mingzhou Jin, PhD
Ismail Capar, PhD
Zhuoxiu Zhang, BS
Sandra D. Eksioglu, PhD

DOI:

https://doi.org/10.5055/jem.2008.0002

Keywords:

traffic management, routing, linear programming, disaster management

Abstract

A framework is proposed to help federal and state agencies in responding to disasters by effectively routing vehicles around a disaster area. The proposed framework includes an information center that uses prediction and optimization models and heuristic algorithms to generate alternative routes for those vehicles that are not able to follow their planned routes because of a disaster. The prediction model determines the routes that will be taken by the vehicles that do not have any communication means. For those vehicles that can communicate with the information center, alternative routes are generated by an optimization model. When a disaster strikes, the information center is immediately informed about the damage and the current traffic conditions in and around the disaster area. The information gathered is used by the optimization model to find alternative routes. The proposed framework is tested using a simulation model on a hypothetical terrorist attack that takes place in Mississippi. The simulation model is executed to compare the system-wide average mobility and speed for three different cases. The first case represents the traffic situation under normal conditions prior to any disaster. The second case shows the affect of setting up simple detours to reroute the traffic after a disaster. The third case shows the traffic conditions if the proposed framework is implemented. The results indicate that the proposed framework improves both system mobility and average speed.

Author Biographies

Burak Eksioglu, PhD

Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, Mississippi.

Mingzhou Jin, PhD

Department of Industrial and Systems Engineering, Mississippi State University.

Ismail Capar, PhD

Department of Engineering Technology and Industrial Distribution, Texas A&M University.

Zhuoxiu Zhang, BS

Department of Industrial and Systems Engineering, Mississippi State University.

Sandra D. Eksioglu, PhD

Department of Industrial and Systems Engineering, Mississippi State University.

References

Scanlon J: Transportation in emergencies: An often neglected story. Disast Prev Manag. 2003; 12(5): 428-437.

Winston WL: Operations Research: Applications and Algorithms. Belmont, CA: Thompson, 2004.

Blumstein A: Task force report: Science and technology, a report to the President’s Commission on Law Enforcement and Administration of Justice. The 1967 President’s Crime Commission Report: Its Impact 25 Years Later. Cincinnati, OH: Anderson, 1994, 145-157.

Larson RC: Urban Police Patrol Analysis. Cambridge, MA: MIT Press, 1972.

Larson RC: A hypercube queuing modeling for facility location and redistricting in urban emergency services. J Comput Oper Res. 1974; 1(1): 67-95.

Walker W, Chaiken J, Ignall E (eds.): Fire Department Deployment Analysis: A Public Policy Analysis Case Study: The RAND Fire Project. New York: North-Holland, 1979.

Green LV, Kolesar PJ: Improving emergency responsiveness with management science. Manag Sci. 2004; 50(8): 1001-1014.

Kaplan EH, Craft DL,Wein LM: Emergency response to a smallpox attack: the case for mass vaccination. Proc Natl Acad Sci. 2002: 99(16): 10935-10940.

Yu G, Arguello M, Song M, et al.: A new era for crew recovery at continental airlines. Interfaces. 2003; 33(1): 5-22.

Urbanik T: Evacuation time estimate for nuclear power plant. J Hazard Mater. 2000; 75(2): 165-180.

Southworth F: Regional Evacuation Modeling: A State-of-theart Review. Oak Ridge, TN: Center for Transportation Analysis, Oak Ridge National Laboratory, March 1991.

Francis RL: A Simple Graphical Procedure to Estimate the Minimum Time to Evacuate a Building. Technical report. Society of Fire Protection Engineers, Boston, MA, 1979, 5-14.

Francis RL: A uniformity principle for evacuation route allocation. J Res Natl Bur Stand. 1981; 86(55): 509-513.

Cova TJ, Church RL: Modelling community evacuation vulnerability using GIS. Int J Geograph Inform Sci. 1997; 11(8): 763-784.

Sayyady F, Eksioglu SD: Emergency evacuation. In Encyclopedia of Optimization (in press).

Husdal J: Fastest path problems in dynamic transportation networks. University of Leicester, UK. Available at www.husdal.com/mscgis/research.htm. Accessed January 22, 2006.

Zhao Y: Vehicle Location and Navigation Systems. Norwood, MA: Artech House, 1997.

Dijkstra EW:A note on two problems in connection with graphs. Numerische Math. 1959; 1: 269-271.

Ford LR, Fulkerson DR: Flows in Network. Princeton, NJ: Princeton University Press, 1962.

Bellman R: On a routing problem. Q Appl Math. 1958; 16: 87-90.

Nilsson NJ: Principles of Artificial Intelligence. San Francisco, CA: Tioga, 1980.

Fu L, Rilett LR: Expected shortest paths in dynamic and stochastic traffic networks. Transport Res B Method. 1996; 32(7): 499-516.

Chabini I: Discrete dynamic shortest path problems in transportation applications. Transport Res Rec. 1998; 1645: 170-175.

Chabini I, Shan L: Adaptations of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks. IEEE Trans Intell Transport Syst. 2002; 3(1): 60-74.

Mohajer K, Mutapcic A, Emami M: Estimation-Pruning (EP) algorithm for point-to-point travel cost minimization in a non- FIFO dynamic network. Proc IEEE 6th Int Conf Intelligent Transportation Systems. 2003; 2: 1257-1262.

Kim S, Lewis ME: Optimal vehicle routing with real-time information. IEEE Transactions Intell Transport Syst. 2005; 6(2): 178-188.

Wiley RB, Keyser TK: Discrete event simulation experiments and geographic information systems in congestion management planning. Proc 30th Conf Winter Simulation. 1998, 1087-1094.

Zhou J, Golledge R: A GPS-based analysis of household travel behavior. In WRSA Annual Meeting. Kauai, Hawaii: 2000.

Barceló J, Casas J: Dynamic network simulation with AIMSUM. In International Symposium on Transport Simulation. Yokohama: 2002.

Owen LE, Zhang Y, Rao L: Traffic flow simulation using CORSIM. Proc 2000 Winter Simulation Conf. 2000, 1143-1147.

ERSI. ArcLogistics Route: A Complete Routing and Scheduling Solution. October 2004.

OmniTrans International. Manuals for OmniTrans v4.0. July 2004.

Caliper Corporation. TRANSCAD User’s Guide. 2000.

Mahmassani HS, Sbayti H, Zhou X: DYNASMART-P Version 1.0 User’s Guide. Maryland Transportation Initiative. April 2004.

Jin M, Eksioglu B, Zhang Z, et al.: Optimally Routing Vehicles with Communication Capabilities in Disasters.Working paper. Department of Industrial and Systems Engineering, Mississippi State University, Starkville,MS, January 2007.

Tan AC, Bowden RO: The Virtual Intermodal Transportation System (VITS) Final Report. Department of Industrial Engineering, Mississippi State University, Starkville,MS,May 2004.