Research on Intelligent Bus Scheduling Based on GPS Signals

With the acceleration of urbanization, bus scheduling plays an increasingly prominent role in alleviating urban traffic pressure. This paper addresses the limitations of traditional static bus scheduling methods when faced with unexpected situations by proposing an intelligent bus scheduling model based on GPS signals. By introducing a rolling time horizon framework and the Lagrangian relaxation algorithm, this study aims to achieve real-time optimization of bus schedules, especially for rescheduling during peak passenger flow surges or unexpected incidents. By constructing a dynamic scheduling model that integrates passenger demand, vehicle location, and speed information, we optimized both passenger waiting times and bus operational costs. Experimental results show that the optimized scheduling scheme effectively reduces passenger waiting time, enhances the operational efficiency of the bus system, and provides an adaptive and flexible solution for bus scheduling systems.