Restricted by various conditions,such as fixed vehicle capacity,conventional electric transit systems are struggling to cope with spatially and temporally uneven station demands through flexible dispatch.To overcome this bottleneck,we proposed a station-based demand responsive model for formation and scheduling optimization based on electric modular vehicle technology that enables dynamic capacity adjustment via coupling/decoupling actions.Taking a single bus route as the modeling object,the model optimizes the vehicle capacity reformation and trip sequences for electric modular vehicles to minimize the total cost,including the vehicle dispatch cost,charging cost,and other items.Considering the low battery capacity of modular vehicles,individual energy constraints and charging plans were emphasized in the scheduling model.Because the proposed model is a mixed-integer nonlinear programming problem,auxiliary variables were introduced to further transform the nonlinear part covered in the constraints into linear constraints to improve the tractability of the model.Using the parameters extracted from real operation data of electric buses in Zhengzhou City as model inputs,a variety of optimization indicators with respect to the number of vehicles employed,total system costs,penalty costs for unserved passengers,and charging costs were compared with the other two scheduling strategies.The results show that compared with the traditional electric bus,the modular vehicle scheduling strategy considering station-based demand-response can reduce the total system costs by approximately 26.6％.In particular,the dynamic capacity advantage among stations allows for a 95.4％reduction in the number of stranded passengers,increasing the accessibility of public transport services.In addition,the total cost is reduced by approximately 7.3％compared to that of the non-station demand-responsive modular vehicle scheduling mode,achieving the most significant savings in operating and charging costs.Sensitivity analysis provides a decision-making basis for actual operations regarding vehicle-type selection,battery capacity configuration,and supply/demand balance between passenger services and vehicle scheduling.
维普
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