Street Patrol Routing Optimization in Smart City Management Based on Genetic Algorithm: A Case in Zhengzhou, China.

Jiang, Yirui; Li, Hongwei; Feng, Binbin; Wu, Zekang; Zhao, Shan; Wang, Zhaohui

A series of urban law enforcement events involving city inspectors dispatched by the city management department can reflect some problems in smart city management, such as illegal advertising and unlicensed street operation. In this paper, we propose a model for the allocation of city inspectors and the optimization of patrol paths. The objective is to minimize the average response time and the number of inspectors. We also develop a priority-patrol-and-multiobjective genetic algorithm (DP-MOGA) to classify patrol segments according to the frequency of events and develop an improved genetic algorithm to achieve the aforementioned objective. We conduct numerical experiments using patrol data obtained from city inspectors in Zhengzhou, China, to clearly show that the proposed algorithm generates reasonable routes that reduce the average response time of events and the number of patrol inspectors. Furthermore, we test the algorithm for three different time scenarios (roads with different average numbers of events) and demonstrate the efficiency of the algorithm. The experimental results show that our proposed algorithm is more stable and efficient than other existing algorithms.

ZHENGZHOU Shi (China); SMART cities; GENETIC algorithms; STREETS; URBAN renewal; ROUTING algorithms; LAW enforcement

ISPRS International Journal of Geo-Information, 2022, Vol 11, Issue 3, p171

2220-9964

Academic Journal

10.3390/ijgi11030171