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Location-allocation models for traffic police patrol vehicles on an interurban network

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Abstract

This research investigates the traffic police routine patrol vehicle (RPV) assignment problem on an interurban road network through a series of integer linear programs. The traffic police RPV’s main task, like other emergency services, is to handle calls-for-service. Emergency services allocation models are generally based on the shortest path algorithm however, the traffic police RPV also handles other roles, namely patrolling to create a presence that acts as a deterrence, and issuing tickets to offenders. The RPVs need to be located dynamically on both hazardous sections and on roads with heavy traffic in order to increase their presence and conspicuousness, in an attempt to prevent or reduce traffic offences, road accidents and traffic congestion. Due to the importance of the traffic patrol vehicle’s location with regard to their additional roles, allocation of the RPVs adheres to an exogenous, legal, time-to-arrival constraint. We develop location-allocation models and apply them to a case study of the road network in northern Israel. The results of the four models are compared to each other and in relation to the current chosen locations. The multiple formulations provide alternatives that jointly account for road safety and policing objectives which aid decision-makers in the selection of their preferred RPV assignments. The results of the models present a location-allocation configuration per RPV per shift with full call-for-service coverage whilst maximizing police presence and conspicuousness as a proxy for road safety.

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References

  • Araz, C., Selim, H., & Ozkaraham, I. (2007). A fuzzy multi-objective covering-based vehicle location model for emergency services. Computers & Operations Research, 34, 705–726.

    Article  Google Scholar 

  • Balas, E., & Jeroslow, R. (1972). Canonical cuts on the unit hypercube. SIAM Journal on Applied Mathematics, 23(1), 61–69.

    Article  Google Scholar 

  • Becker, G. S. (1968). Crime and punishment: an economic approach. Journal of Political Economy, 76(2), 169–217.

    Article  Google Scholar 

  • Beenstock, M., & Gafni, D. (2000). Globalization in road safety: explaining the downward trend in road accident rates in a single country (Israel). Accident Analysis and Prevention, 32(1), 71–84.

    Article  Google Scholar 

  • Berman, O., Drezner, Z., & Krass, D. (2010). Generalized coverage: new developments in covering location models. Computers & Operations Research, 37, 1675–1687.

    Article  Google Scholar 

  • Bester, C. J. (2001). Explaining national road fatalities. Accident Analysis and Prevention, 33(5), 663–672.

    Article  Google Scholar 

  • Birge, J. R., & Pollock, S. M. (1989). Modelling rural police patrol. Journal of the Operational Research Society, 40(1), 41–54.

    Article  Google Scholar 

  • Chaiken, J. M., & Dormont, P. (1978a). A patrol car allocation model: background. Management Science, 24(12), 1280–1290.

    Article  Google Scholar 

  • Chaiken, J. M., & Dormont, P. (1978b). A patrol car allocation model: capabilities and algorithms. Management Science, 24(12), 1291–1300.

    Article  Google Scholar 

  • Chang, L. Y., & Chen, W. C. (2005). Data mining of tree-based models to analyze freeway accident frequency. Journal of Safety Research, 36, 365–375.

    Article  Google Scholar 

  • Christensen, P., & Elvik, R. (2007). Effects on accidents of periodic motor vehicle inspection in Norway. Accident Analysis and Prevention, 39, 47–52.

    Article  Google Scholar 

  • Church, R., & Revelle, C. (1974). The maximal covering location problem. Papers in Regional Science, 32, 101–118.

    Article  Google Scholar 

  • Church, R., Sorensen, P., & Corrigan, W. (2001). Manpower deployment in emergency services. Fire Technology, 37, 219–234.

    Article  Google Scholar 

  • Coleman, T. F., & Moré, J. J. (1983). Estimation of sparse Jacobian matrices and graph coloring problems. SIAM Journal on Numerical Analysis, 20(1), 187–209.

    Article  Google Scholar 

  • Curtin, K. M., Qiu, F., Hayslett-McCall, K., & Bray, T. M. (2005). Integrating GIS and maximal covering models to determine optimal police patrol areas. In GIS and crime analysis (Chap. XIII).

  • Curtin, K. M., Hayslett-McCall, K., & Qiu, F. (2010). Determining optimal police patrol areas with maximal covering and backup covering location models. Networks and Spatial Economics, 10(1), 125–145.

    Article  Google Scholar 

  • Daskin, M. S. (1982). Application of an expected covering model to emergency medical service system design. Decision Sciences, 13(3), 416–439.

    Article  Google Scholar 

  • Daskin, M. S. (1995). Network and discrete location—models, algorithms and applications. New York: Wiley.

    Book  Google Scholar 

  • Daskin, M. S., Coullard, C. R., & Shen, Z. M. (2002). An inventory-location model: formulation, solution algorithm and computational results. Annals of Operations Research, 110, 83–106.

    Article  Google Scholar 

  • Dijkstra, E. W. (1959). A note on two problems in connection with graphs. Numerische Mathematik, 1, 269–271.

    Article  Google Scholar 

  • Elvik, R. (1997). Evaluations of road accident blackspot treatment: a case of the iron law of evaluation studies? Accident Analysis and Prevention, 29(2), 191–199.

    Article  Google Scholar 

  • Elvik, R., & Vaa, T. (2004). The handbook of road safety measurement. Oxford: Elsevier.

    Google Scholar 

  • ETSC (European Transport Safety Council) (May 1999). Police enforcement strategies to reduce traffic casualties in Europe, Brussels.

  • Fell, J. C., Ferguson, S. A., Williams, A. F., & Fields, M. (2003). Why are sobriety checkpoints not widely adopted as an enforcement strategy in the United States? Accident Analysis and Prevention, 35(6), 897–902.

    Article  Google Scholar 

  • Francis, R. L., Lowe, T. J., Rayco, B., & Tamir, A. (2005). Aggregation error for location models: survey and analysis. Working paper, Department of Industrial and Systems Engineering, University of Florida, Gainesville. http://www.ise.ufl.edu/francis/download/Survey.pdf.

  • Garey, M. R., & Johnson, D. S. (1979). Computers and intractability: a guide to the theory of NP-completeness. New York: Freeman.

    Google Scholar 

  • Green, L. V., & Kolesar, P. J. (1989). Testing the validity of a queuing model of police patrol. Management Science, 35(2), 127–148.

    Article  Google Scholar 

  • Green, L. V., & Kolesar, P. J. (2004). Improving emergency responsiveness with management science. Management Science, 50(8), 1001–1014.

    Article  Google Scholar 

  • Hakkert, A. S., Yelinek, A., & Efrat, E. (1990). Police surveillance methods and police resource allocation models. In The international road safety symposium, Denmark.

  • Hakkert, A. S., Gitelman, V., Cohen, A., Doveh, E., & Umansky, T. (2001). The evaluation of effects on driver behavior and accidents of concentrated general enforcement on interurban roads in Israel. Accident Analysis and Prevention, 33, 43–63.

    Article  Google Scholar 

  • Hauer, E. (2005). Fishing for safety information in murky waters. Journal of Transportation Engineering, 131(5), 340–344.

    Article  Google Scholar 

  • Hurley, W. J., Brimberg, J., & Pavlov, A. (2009). Optimal thresholds for fining speeders for a stationary speed-check operation when the traffic intensity is low. Journal of the Operational Research Society, 60, 1154–1159.

    Article  Google Scholar 

  • Israel Central Bureau of Statistics (2006). Traffic volumes.

  • Larson, R. C. (1974). A hypercube queuing modeling for facility location and redistricting in urban emergency services. Computers & Operations Research, 50(1), 135–145.

    Google Scholar 

  • Larson, R. C., & McKnew, M. A. (1982). Police patrol-initiated activities within a systems queueing model. Management Science, 28(7), 759–774.

    Article  Google Scholar 

  • Ma, L. (2003). Integrating GIS and combinatorial optimization to determine police patrol areas. Master, GIS, supervised By Dr. Curtin Kevin, University of Texas at Dallas.

  • Newstead, S. V., Cameron, M. H., & Leggett, L. M. W. (2001). The crash reduction effectiveness of a network-wide traffic police deployment system. Accident Analysis and Prevention, 33, 393–406.

    Article  Google Scholar 

  • OECD (Organisation for Economic Co-operation and Development) (1974). Research on traffic law enforcement. Paris, France.

  • Owen, S. H. & Daskin, M. S. (1998). Strategic facility location: a review. European Journal of Operational Research, 111, 423–447.

    Article  Google Scholar 

  • Peleg, K. (2000). The effectiveness of Israel’s pre-hospital emergency medical services organization. PhD thesis, Ben-Gurion University, Beer-Sheba, Israel.

  • Plastria, F., & Vanhaverbeke, L. (2007). Aggregation without loss of optimality in competitive location models. Networks and Spatial Economics, 7, 3–18.

    Article  Google Scholar 

  • ReVelle, C. S., & Eiselt, H. A. (2005). Location analysis: a synthesis and survey. European Journal of Operational Research, 165, 1–19.

    Article  Google Scholar 

  • Sacks, S. R. (2000). Optimal spatial deployment of police patrol cars. Social Science Computer Review, 18(1), 40–55.

    Article  Google Scholar 

  • Schrijver, A. (1998). Theory of linear and integer programming. New York: Wiley (Chap. 19, pp. 266–281).

    Google Scholar 

  • Simpson, N. C., & Hancock, P. G. (2009). Fifty years of operational research and emergency response. Journal of the Operational Research Society, 60, 126–139.

    Article  Google Scholar 

  • Tillyer, R., Engel, R. S., & Cherkauskas, J. C. (2010). Best practices in vehicle stop data collection and analysis. Policing: An International Journal of Police Strategies & Management, 33(1), 69–92.

    Article  Google Scholar 

  • Toregas, C., & ReVelle, C. (1973). Binary logic solutions to a class of location problem. Geographical Analysis, 5(2), 145–155.

    Article  Google Scholar 

  • Toregas, C., Swain, R., ReVelle, C., & Bergman, L. (1971). The location of emergency service facilities. Operations Research, 19, 1363–1373.

    Article  Google Scholar 

  • Tsai, J. F., Lin, M. H., & Hu, Y. C. (2008). Finding multiple solutions to general integer linear programs. European Journal of Operational Research, 184, 802–809.

    Article  Google Scholar 

  • Vanlaar, W. (2008). Less is more: the influence of traffic count on drinking and driving behaviour. Accident Analysis and Prevention, 40, 1018–1022.

    Article  Google Scholar 

  • Walker, W., Chaiken, J., & Ignall, E. (1979). Fire department deployment analysis. The rand fire project. New York: Elsevier/North-Holland.

    Google Scholar 

  • Wright, P. D., Liberatore, M. J., & Nydick, R. L. (2006). A survey of operations research models and applications in homeland security. Interfaces, 36(6), 514–529.

    Article  Google Scholar 

  • Yin, Y. (2006). Optimal fleet allocation of freeway service patrols. Networks and Spatial Economics, 6, 221–234.

    Article  Google Scholar 

Download references

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Correspondence to Mali Sher.

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Adler, N., Hakkert, A.S., Kornbluth, J. et al. Location-allocation models for traffic police patrol vehicles on an interurban network. Ann Oper Res 221, 9–31 (2014). https://doi.org/10.1007/s10479-012-1275-2

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