Bus Scheduling of Overlapping Routes Based on the Combination of All-Stop and Stop-Skipping Services

Abstract

Developing public transportation is an effective way to alleviate traffic congestion and reduce fossil fuel consumption. However, the result of ineffective public transport planning can be that some bus lines have long overlapping intervals, and share many common stops between them, which causes a significant waste of resources. The current bus scheduling method does not consider the imbalance of passenger flow between different stops or combine the overlapping interval with transit mixed scheduling, including all-stop and stop-skipping services. Therefore, based on the analysis of the characteristics of overlapping interval, this paper proposes a combined bus scheduling method, which includes both all-stop and stop-skipping bus services. The method is a deterministic method applied in the scheduling stage of transit planning. The method optimizes scheduling strategies by minimizing total passenger travel time, including the waiting time and the in-vehicle travel time, and the line frequency of each bus service is taken as the decision variable. Then a genetic algorithm is designed to find the optimal solution. The model is verified by two bus lines in Changchun city. The experimental results showed that the proposed model could reduce total passenger travel time by 7.71% compared with the existing scheduling method. The combined bus scheduling method can supplement the research on bus scheduling of overlapping routes and provide suggestions for operators.

Urban bus scheduling considering maximizing TWB under the fixed departure times: A case of Beijing

“…If the size of the group is too small, it is easy to obtain the local optimal solution. Based on experience and multiple simulation experiments, the population size is commonly determined at about 100 (Centeno-Telleria et al, 2021;Zhang et al, 2022), which can not only ensure that the population size is large enough, but also ensure that the optimal solution can be obtained within the specified time. Therefore, this study sets the number of iterations as 120.…”