Real-time information-based combined control method for bus delay - Hu, Baoyu; Song, Xinlei; Fu, Yangyang (Transport, 2025)
As environmental pollution and energy consumption become increasingly serious concerns, more cities are opting for electric buses over traditional fuel buses. However, the stability and reliability of electric buses during operation are challenged by the unpredictability of traffic flow and passenger demand. Factors such as traffic congestion, weather conditions, and fluctuations in passenger numbers can compromise the punctuality of electric bus services, often resulting in delays. To address these challenges, a real-time dual-objective bus control model for mixed traffic scenarios has been proposed. This model aims to minimize both passenger time costs and company operational costs. Factors such as intersections and traffic flow are also considered. A combined control strategy, including speed control and a backup bus replacement strategy, has been proposed. Speed control is specifically aimed at managing intersection delays, allowing buses to adjust their speeds to pass through intersections optimally between queue dissipation and the end of the green-light period. The backup bus replacement strategy, on the other hand, is implemented at bus terminals, where a backup bus replaces a delayed one to maintain the schedule. A heuristic algorithm based on Particle Swarm Optimization (PSO) is incorporated into the model, enhancing its effectiveness by iteratively updating the positions and velocities of particles in the search space. Harbin City Road 96 was selected as a case study for model validation. In the off-peak case scenario, schedule deviation was reduced by 89% through the implementation of the proposed speed control strategy. Additionally, passenger waiting time was reduced by 8%, and passenger travel time was reduced by 14%. In the peak case scenario, the proposed control strategy effectively eliminated bus departure delays originating within the bus system. These results demonstrate the potential of the proposed model to significantly enhance the reliability and stability of public transportation systems, thereby improving the overall quality of public transport services.