Bi-objective bus scheduling optimization with passenger perception in mind

Abstract

With the development of big traffic data, bus schedules should be changed from the traditional "empirical" rough scheduling to "responsive" accurate scheduling to meet the travel needs of passengers. Based on passenger flow distribution, considering passengers' feelings of congestion and waiting time at the station, we establish a dual-cost bus scheduling optimization model (DCBSOM) with the optimization objectives of minimizing bus operation and passenger travel costs and improving the classical genetic algorithm (GA) by adaptively determining the crossover probability and variance probability of the algorithm. We use an improved double probability adaptive genetic algorithm (IDPAGA) to solve the model and consider the constraint conditions of the model in the iterative process of the IDPAGA. By solving the arithmetic example, we get: (1) the optimized departure plan is more inclined to the passenger benefits and more in line with passenger travel pattern; (2) the optimal solution can reduce the overall objective function value by 4.22%, improve the bus operation cost by 4.9%, and reduce the passenger travel cost by 13.4%. The conclusions show that although the DCBSOM built by the research increases the bus sector's operating costs to a certain extent, it can better meet the passenger travel demand, improve passenger travel satisfaction, and reduce the passenger travel cost and waiting for cost.