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This study chose Nanjing as a case study, comprehensively analysed the spatio-temporal distribution characteristics of traffic flow, and constructed an optimisation model based on the theory of vehicle queues. The study profoundly explored signal control strategies and developed an adaptive traffic signal control system. This system integrates real-time data acquisition, dynamic data processing, and intelligent decision-making support modules, thereby forming a highly automated and intelligent traffic management platform. The study verified the effectiveness of the optimisation strategy through simulation experiments utilising microscopic traffic simulation software VISSIM and SUMO. The results indicated that the average delay was decreased by 25%, and the emissions of CO and NOx were reduced by 15% and 20% respectively. The study put forward a multi-objective optimisation model based on mixed integer linear programming (MILP), which combines multi-objective optimisation algorithms with the requirements of modern traffic management and aims to achieve the dual goals of minimising traffic delay and reducing exhaust emissions. This comprehensive strategy holds significant practical significance and application value for addressing urban traffic congestion problems.

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Design and Implementation of Traffic Congestion Relief Strategies Based on Multi-objective Optimization Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Jun Zhao

摘要

This study chose Nanjing as a case study, comprehensively analysed the spatio-temporal distribution characteristics of traffic flow, and constructed an optimisation model based on the theory of vehicle queues. The study profoundly explored signal control strategies and developed an adaptive traffic signal control system. This system integrates real-time data acquisition, dynamic data processing, and intelligent decision-making support modules, thereby forming a highly automated and intelligent traffic management platform. The study verified the effectiveness of the optimisation strategy through simulation experiments utilising microscopic traffic simulation software VISSIM and SUMO. The results indicated that the average delay was decreased by 25%, and the emissions of CO and NOx were reduced by 15% and 20% respectively. The study put forward a multi-objective optimisation model based on mixed integer linear programming (MILP), which combines multi-objective optimisation algorithms with the requirements of modern traffic management and aims to achieve the dual goals of minimising traffic delay and reducing exhaust emissions. This comprehensive strategy holds significant practical significance and application value for addressing urban traffic congestion problems.