Design and implementation of public transportation emergency command and assistance platform from the perspective of smart policing
DOI:
https://doi.org/10.65455/9m8wac27Keywords:
Smart Policing, Public Transport, Emergency Command, Vehicle Scheduling, Public Security Prevention and Control, Microservices ArchitectureAbstract
In view of the pain points faced by the current urban public transportation public security management, such as high security pressure of large-scale activities, lagging police response, and insufficient integration of multi-source data, this paper designs and implements a public transportation emergency command and assistance platform. The system adopts Spring Boot + Vue.js front-end and back-end separation architecture, integrating five core modules: vehicle scheduling, route optimization, passenger feedback, real-time monitoring and data analysis. On this basis, from the perspective of smart policing, the adaptation design of the system in scenarios such as security emergency vehicle scheduling for large-scale activities, public security risk investigation of passenger feedback data, real-time monitoring and linkage with public security command center is studied. At the same time, ideas for expanding police functions such as early warning of key personnel and one-click alarm linkage are proposed. Practice shows that the platform can effectively improve the informatization and intelligence level of public transportation security work, and provide technical support for the construction of an integrated police mechanism of "emotion, guidance, and action".
References
[1]Shanghai Municipal Transportation Commission. Top-level Design Scheme of Smart Bus in Shanghai[Z/OL]. https://jtw.sh.gov.cn/cmsres/7e/7e4bcedd3e1545319b38cdc558e459d6/7ed1c1cab43047bfda36f0d64deb8f5f.pdf.
[2]LI X J, YUAN J, QIN Y H. Joint Optimization of Customized Passenger Motion Boarding and Disembarking Points and Vehicle Routes. Transportation System Engineering and Information Technology, 2026, 26(01): 228-238.
[3]ZHANG A L, JIA S P. Research on Optimization of Cross-regional Demand-Responsive Bus Dynamic Scheduling Based on Modular Vehicles. Shandong Science, 2026, 39(01): 77-87.
[4]LI X Y, ZHAO T, SUN Q. Optimization of dynamic demand responsive bus routes considering reservation cancellation behavior. Journal of Wuhan University of Technology, 2025, 47(09): 55-62+79.
[5]CHENG H. Research on Dynamic Bus Coordination and Scheduling Based on Vehicle-Road Coordination. Dalian: Dalian Jiaotong University, 2025.
[6]XIAO Y Y. Research on bus passenger satisfaction in Foshan City. Wuhan: Central China Normal University, 2020.
[7]KOU W B. Optimization modeling of bus passenger travel time reliability improvement strategy. Beijing: Beijing Jiaotong University, 2019.
[8]CHEN M Y, CAO C S. Design and Implementation of BRT Intelligent Scheduling System. Electronic Design Engineering, 2017, 25(19): 93-97.
[9]ZHANG J, ZHAO A, TANG C. Integrated optimization of mixed bus fleet replacement and scheduling with emission and budget constraints. Computers & Industrial Engineering, 2026, 215: 111908.
[10]KOUREPINIS V, ILIOPOULOU C, TASSOPOULOS X I, et al. An Improved Particle Swarm Optimization Algorithm for the Urban Transit Routing Problem. Electronics, 2023, 12(15): 3358.
[11]RYU S, PARK B B, TAWAB E S. WiFi Sensing System for Monitoring Public Transportation Ridership: A Case Study. KSCE Journal of Civil Engineering, 2020, 24(10): 1-13.
[12]CASTELLANOS C J, FRUETT F. Embedded system to evaluate the passenger comfort in public transportation based on dynamical vehicle behavior with user's feedback. Measurement, 2014, 47: 442-451.
[13]Ministry of Public Security. Technical Specifications for the Construction of Public Security Video Surveillance System[S/OL]. 2017. https://zjjcmspublic.oss-cn-hangzhou-zwynet-d01-a.internet.cloud.zj.gov.cn/jcms_files/jcms1/web3233/site/attach/0/dfd244b5f4f04f4fa43cbf79797857ad.pdf
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP

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