Novel Method for Optimizing Traffic Police Manpower by Standby and Patrol

  • yanqun yang Fuzhou University, Fuzhou, Fujian
  • Mingyue Miao School of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing, 100070, China.
  • Said Easa Department of Civil Engineering, Ryerson University, Toronto, Ontario M5B 2K3, Canada.
  • Jianying Chen College of Civil Engineering, Fuzhou University, Fuzhou, Fujian, 350108, China.
  • Jun Zhang School of Information, Capital University of Economics and Business, Beijing, 100070, China.
  • Qiang Li Traffic Police Detachment, Capital International Airport, Beijing, 100621, China.
Keywords: Traffic police, Police resources, Queueing model, Poisson Distribution model

Abstract

The purpose of this research paper is to find out a quantitative method that could assist traffic police department with determining minimum patrol and standby staffing and optimizing shift schedule to meet current performance benchmarks. The average events rate reflects the overall traffic situation over a certain period of time. By Fault Tree Analysis (FTA) method, average events occurrence rate could be expressed by a linear equation, which combines the number of traffic accidents, congestion events, and serious violation events with their key importance coefficients. Next, the number of patrol team and the sum of the minimum number of patrol and standby police could be obtained by applying Queueing model and Poisson Distribution model, based on comparing and analyzing both models by average events occurrence rate. These variables mentioned will be used to establish constraint equations in Integer Programming (IP) model. Finally, it is provided to the traffic police detachments or stations, especially those consisted of patrol police and clerical police, to a relatively simple and quantitative way to optimize police resources based on traffic conditions of their precinct.

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References

Adler, N., Hakkert, A. S., Raviv, T., & Sher, M. (2014). The traffic police location and schedule assignment problem. Journal of Multi‐Criteria Decision Analysis, 21(5-6), 315-333.
Alfares, H. K. (2007). Operator staffing and scheduling for an IT-help call centre. European Journal of Industrial Engineering, 1(4), 414-430.
Bassett, M. (2000). Assigning projects to optimize the utilization of employees' time and expertise. Computers & Chemical Engineering, 24(2-7), 1013-1021.
Basu, M., & Ghosh, D. (1997). Nonlinear Goal Programming Model for the Development of Metropolitan Police Patrol Units. Opsearch, 34(1), 27-42.
Chaiken, J. M., & Dormont, P. (1978). A patrol car allocation model: Background. Management Science, 24(12), 1280-1290.
D'Amico, S. J., Wang, S. J., Batta, R., & Rump, C. M. (2002). A simulated annealing approach to police district design. Computers & Operations Research, 29(6), 667-684.
Green, L. (1984). A multiple dispatch queueing model of police patrol operations. Management Science, 30(6), 653-664.
Green, L., & Kolesar, P. (1984). The feasibility of one-officer patrol in New York City. Management science, 30(8), 964-981.
Green, L., & Kolesar, P. (1989). Testing the validity of a queueing model of police patrol. Management Science, 35(2), 127-148.
Hickman, M. J., Fricas, J., Strom, K. J., & Pope, M. W. (2011). Mapping police stress. Police Quarterly, 14(3), 227-250.
Keskin, B. B., Li, S. R., Steil, D., & Spiller, S. (2012). Analysis of an integrated maximum covering and patrol routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 215-232.
Kolesar, P. J., Rider, K. L., Crabill, T. B., & Walker, W. E. (1975). A queuing-linear programming approach to scheduling police patrol cars. Operations Research, 23(6), 1045-1062.
Kou, C. K. C., & Liu, G. C. L. G. C. (1996, October). An adaptive police duty scheduling system based on machine learning. In 1996 30th Annual International Carnahan Conference on Security Technology (pp. 212-219). IEEE.
Lou, Y., Yin, Y., & Lawphongpanich, S. (2011). Freeway service patrol deployment planning for incident management and congestion mitigation. Transportation Research Part C: Emerging Technologies, 19(2), 283-295.
Mustapar, W. E., Nasir, D. S. M., Nor, N. A. M., & Abas, S. F. S. (2017, November). Goal programming for cyclical auxiliary police scheduling at UiTM Cawangan Perlis. In AIP Conference Proceedings (Vol. 1905, No. 1, p. 040021). AIP Publishing.
Nag, B. (2014). A MIP model for scheduling India’s General elections and police movement. Opsearch, 51(4), 562-576.
Ozbay, K., Iyigun, C., Baykal-Gursoy, M., & Xiao, W. (2013). Probabilistic programming models for traffic incident management operations planning. Annals of Operations Research, 203(1), 389-406.
Pal, B. B., Kumar, M., & Sen, S. (2009, December). A linear fuzzy goal programming approach for solving patrol manpower deployment planning problems—A case study. In 2009 International Conference on Industrial and Information Systems (ICIIS) (pp. 244-249). IEEE.
Pal, B. B., Chakraborti, D., Biswas, P., & Mukhopadhyay, A. (2012). An application of genetic algorithm method for solving patrol manpower deployment problems through fuzzy goal programming in traffic management system: a case study. International Journal of Bio-Inspired Computation, 4(1), 47-60.
Sharma, D. K., Ghosh, D., & Gaur, A. (2007). Lexicographic goal programming model for police patrol cars deployment in metropolitan cities. International journal of information and management sciences, 18(2), 173.
Todovic, D., Makajic-Nikolic, D., Kostic-Stankovic, M., & Martic, M. (2015). Police officer scheduling using goal programming. Policing: An International Journal of Police Strategies & Management, 38(2), 295-313.
Van den Bergh, J., Beliën, J., De Bruecker, P., Demeulemeester, E., & De Boeck, L. (2013). Personnel scheduling: A literature review. European Journal of Operational Research, 226(3), 367-385.
Vila, B. (2006). Impact of long work hours on police officers and the communities they serve. American journal of industrial medicine, 49(11), 972-980.
Vila, B., Morrison, G. B., & Kenney, D. J. (2002). Improving shift schedule and work-hour policies and practices to increase police officer performance, health, and safety. Police quarterly, 5(1), 4-24.
Violanti, J. M., Fekedulegn, D., Andrew, M. E., Charles, L. E., Hartley, T. A., Vila, B., & Burchfiel, C. M. (2012). Shift work and the incidence of injury among police officers. American journal of industrial medicine, 55(3), 217-227.
Wu, J. S., & Lou, T. C. (2014). Improving highway accident management through patrol beat scheduling. Policing: An International Journal of Police Strategies & Management, 37(1), 108-125.
Wu, M. C., & Sun, S. H. (2006). A project scheduling and staff assignment model considering learning effect. The International Journal of Advanced Manufacturing Technology, 28(11-12), 1190-1195.
Yanan, D. , & Huayu, F. . (2012). Genetic annealing algorithm for police officer scheduling problem. Computer Engineering and Applications, 48(28), 225-228.
Yin, Y. (2008). A scenario-based model for fleet allocation of freeway service patrols. Networks and Spatial Economics, 8(4), 407-417.
Zhang, Y., & Brown, D. (2014, April). Simulation optimization of police patrol district design using an adjusted simulated annealing approach. In Proceedings of the Symposium on Theory of Modeling & Simulation-DEVS Integrative (p. 18). Society for Computer Simulation International.
Published
2021-05-25
How to Cite
yang, yanqun, Miao, M., Easa, S., Chen, J., Zhang, J., & Li, Q. (2021). Novel Method for Optimizing Traffic Police Manpower by Standby and Patrol. IJRDO - Journal of Applied Management Science, 7(5), 01-15. https://doi.org/10.53555/ams.v7i5.3655