Location-coverage models for preventing attacks on interurban transportation networks
- 325 Downloads
Interurban roads are constantly used by transient vehicles. In some places, however, network users are subject to attacks, resulting in assaults to drivers and cargo theft. In an attempt to solve this problem, a binary integer programming model is developed, whose objective is to maximize the expected vehicle coverage across the network. The model dynamically locates patrol units through a fixed time horizon, subject to movement and location constraints, considering a probability of not being able to attend to an attack, due to a distance factor. A chronological decomposition heuristic is developed, and achieves an optimality gap of less than 1 %, in less than 5 min. The problem is also solved by developing a geographical decomposition heuristic. To introduce a measure of equity, two sets of constraints are proposed. Three measures are considered: total vehicle coverage, inequity and network coverage. A trade-off between these three measures is observed and discussed. Finally, scalability of the model is explored, using decomposition in terms of patrolling units, until we obtain subproblems of equal size as the original instance. All of these features are applied to a case study in Northern Israel. In the last section, some adaptations and additions to the model that can be made in further research are discussed.
KeywordsLocation Patrolling Coverage Transportation Traffic police Routing
The authors would like to thank the Associate Editor and anonymous referees for their comments on earlier versions of this paper. This proved to be of great help, specially for improving many explanations in this work.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Anderson, B. (2007). Securing the supply chain-prevent cargo theft. Security, 44(5), 56–59.Google Scholar
- Barth, S., & White, M. (1998). Hazardous cargo. World, 11(11), 46–48.Google Scholar
- Chapman, S., & White, J. (1974). Probabilistic formulations of emergency service facilities location problems. In ORSA/TIMS conference, San Juan, Puerto Rico.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 F. Wang (Ed.), Geographic information systems and crime analysis (Chap. 13, pp. 214–235). Hershey: IDEA Group Publishing.Google Scholar
- International Road Transport Union. (2008). Attacks on drivers of international heavy goods vehicles. Technical report, 3, rue de Varembé B.P. 44 CH-1211 Geneva 20 Switzerland. http://www.iru.org/cms-filesystem-action?file=webnews2008/Attack
- Marshall, B., Kaza, S., Xu, J., Atabakhsh, H., Petersen, T., Violette, C., & Chen, H. (2004). Cross-jurisdictional criminal activity networks to support border and transportation security. In The 7th international IEEE conference on intelligent transportation systems, 2004. Proceedings (pp. 100–105). IEEE.Google Scholar
- Secretariado Ejecutivo del Sistema Nacional de Seguridad Pública. (2015). Incidencia delictiva del fuero común 2014. Technical report, Gral. Mariano Escobedo 456, Anzures, Miguel Hidalgo, 11590 Ciudad de México, D.F., Mexico. http://www.secretariadoejecutivo.gob.mx/docs/pdfs/estadisticas.
- Van den Engel, A., & Prummel, E. (2007). Organised theft of commercial vehicles and their loads in the european union. European Parliament. Directorate General Internal Policies of the Union. Policy Department Structural and Cohesion Policies. Transport and Tourism, Brussels.Google Scholar