No abstract

The flexible-route bus system is a type of dynamic public transit service. Routes and timetables are not fixed during the operation process, and driving routes are planned according to passengers’ reservation needs. This study develops a model that considers inter-regional travel demands. The optimal network layout is determined by minimizing an objective function that comprises operator and user costs. Then, two cases with and without loop-line buses are analyzed. In the case of the joint optimal solution, the parameter values of region side width, region angle, and cost components are compared. Results indicate that regional flexible transit is suitable for operation in areas with low demand density. Within certain ranges, increases in vehicle capacity and in the number of circle layers result in additional average total costs. Furthermore, adopting a mode with a loop is better when numerous inter-regional demands exist. The findings derived from numerical and sensitivity analyses can be used as planning guides for designing flexible-route bus systems.

To meet trip demands and avoid transit capacity waste or shortages, this study investigates the routing optimization of flexible transit with time windows. We introduce the time penalty costs to accommodate the impacts of early and late vehicle arrivals on passengers’ satisfaction. A routing optimization model is developed to minimize the system operation costs and the costs incurred by passengers' time penalties. The problem is solved by a designed adaptive genetic algorithm that adopts an adaptive mutation strategy to dynamically adjust the mutation probability and mutation operator. The numerical experiments compare the results of the mixed demand model, in which vehicles can pick up and drop off passengers simultaneously, to those of the separate pick-up and delivery modes. Finally, a sensitive analysis is conducted to explore the impact of operational factors (vehicle speed, maximum one-way travel time, and weighting ratios between operating and penalty costs) on the system's performance (total costs, per capita mileage, and average seat occupancy rate). Our results confirm the advantages of the developed adaptive genetic algorithm over traditional ones with respect to the convergence speed and optimality gap. Moreover, the numerical results indicate that the mixed demand operation mode of transit reduces total costs by an average of 2.35% compared to separate pick-up and delivery modes. The results also reveal that an increase in the weights of the operating cost can reduce the total cost. The findings of this work can provide guidance to the operation of regional flexible transit.