Optimization of smart bus scheduling based on dynamic speed limit of load rate

Abstract

Aiming at the problem that fixed speed limit and static regular scheduling of single-type bus cannot meet the actual passenger flow demand with uneven distribution of time and space. By designing a dynamic speed limit rule of full load rate, based on the integrated scheduling of multiple types of bus models, taking the total passenger cost and bus scheduling cost as the overall optimization objective, and considering various constraints comprehensively, OBLESGA (Opposition-based learning elite strategy genetic algorithm) was designed to solve the problem, and the data of Beijing 563 route downlink was taken as an example for simulation experiment. The results show that OBLESGA can improve the convergence rate and solution accuracy of GA. At the same time, the optimized scheduling can reduce the cost of passenger congestion by 86.61 percent and the cost of passenger waiting times by 40.65 percent compared to traditional scheduling, while only increasing operating costs by 26.57 percent.