The demand-responsive public transport system with multi-vehicles has the potential to efficiently meet real-time and high-volume transportation needs through effective scheduling. This paper focuses on studying the real-time vehicle scheduling problem, which involves dispatching and controlling different model vehicles uniformly based on generated vehicle number tasks at a given point in time. By considering the immediacy of real-time itinerary tasks, this paper optimizes the vehicle scheduling problem at a single time point. The objective function is to minimize the total operating cost of the system while satisfying constraints such as passenger capacity and vehicle transfer time. To achieve this, a vehicle scheduling optimization model is constructed, and a solution approach is proposed by integrating bipartite graph optimal matching theory and the Kuhn–Munkres algorithm. The effectiveness of the proposed approach is demonstrated by comparing it with a traditional greedy algorithm using the same calculation example. The results show that the optimization method has higher solution efficiency and can generate a scheduling scheme that effectively reduces operating costs, improves transportation efficiency, and optimizes the operation organization process for demand-responsive buses.

There is an inherent coupling relationship between the time when buses arrive at the station and the time when they arrive the intersection, and it is essential to study the relationship as a whole to maximize the benefits of company operations and passenger services. In this study, a coordinated control method of signal priority and speed regulation in the stop-skipping mode at peak hours is proposed. First, the decision result of stop-skipping is obtained based on the historical passenger flow data. On this basis, the signal-priority decision is made for each vehicle in combination with the signal period and the arrival time of the intersection, and coordinated control is carried out in combination with the speed adjustment. The result of the genetic algorithm shows that cooperative control and prevention can minimize the passenger delay time and enterprise operation cost. The conclusions obtained in this research lay a theoretical foundation for company operation and signal-priority triggering mechanism.

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