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.

Reasonable districting plays an important role in the patrolling process. In this paper, workload attributes are considered, and a mixed integer programming model is developed to solve the street patrol districting problem (SPDP). The improved spectral clustering algorithm named spectral clustering algorithm based on the road network (SCRn) and simulated annealing algorithm (SA) are combined. This results in a hybrid algorithm called SCRn-SA. The SCRn-SA algorithm is tested on small examples and real instances in Zhengzhou, China. The experimental results show that the proposed algorithm is effective for solving SPDP. It has better performance when compared to other advanced algorithms.

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Urban management is an important content of social management, which is related to people's livelihood and social harmony. With the rapid advancement of urbanization and the continuous expansion of urban scale, urban management faces greater challenges, and it is very important to complete urban management efficiently and effectively. The purpose of this study is to analyse the spatiotemporal characteristics of street patrol cases in Zhengzhou, China. A statistical approach is used for overall analysis, then analysis method of Getis-Ord Gi* and space-time cube are used. Focusing on smart city management work in the central city of Zhengzhou, the study examines street patrol cases in the categories of urban environment and street order. The main findings are as follows:The analysis by the space-time cube model shows that street order case hotspots have seasonal characteristics on monthly data. The urban environment cases have a wider distribution and higher density during winter near the Chinese New Year.