Routing Optimization of Regional Flexible Transit Under the Mixed Demand Mode

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\u27 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\u27s 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