Maintenance Scheduling Optimization of Urban Non-intrusive Road Intelligent Transportation System Equipment under the Autonomous Driving Environment

<div class="section abstract"><div class="htmlview paragraph">The increasing traveling demands are putting higher pressure on urban networks, where the efficient driving modes highly depend on various non-intrusive ITS equipment for interaction, which asks for higher maintenance scheduling plans minimizing network loss. Current studies have researched methodologies with the aspects of deterministic methods and metaheuristic algorithms under different scenarios, but lack the simulation considering maintenance work type, urban traffic characteristics as well as the ITS equipment. This study aims to optimize the maintenance scheduling plan of urban ITS systems by using the genetic algorithm (GA) and Dijkstra algorithm, as well as other judgmental algorithms to minimize traffic delays caused by maintenance activities, and presents a novel method to assess economic losses. A mixed integer programming model is established simulating the real urban network while considering multiple constraints, including the route selection principle, network updating, network updating principle, etc. Then a complex urban network is randomly assumed for the case study. Through case verification, the effectiveness of the proposed model and algorithm in reducing the delay of the entire road network is proved and reached a 19.5% loss avoidance compared to the traditional GA under the case scenario. This study provides a theoretical basis and practical guidance for the future maintenance and scheduling of intelligent transportation systems in the environment of automatic driving, with advantages of expandability, editability, and relatively high efficiency, but leaves shortcomings of the possibility of falling into the local optima and traffic assignment principle, which could be further studied in the algorithm and the help with other advanced traffic assignment models.</div></div>