考虑乘客偏好的需求响应定制公交线路优化

To enhance the appeal of demand-responsive customized bus systems, this paper addresses a two-stage demand-responsive customized bus route optimization problem that incorporates passenger preferences. In the static stage, passenger preferences for vehicle service types are identified with a spatio-temporal clustering algorithm. These preferences are then characterized as exogenous parameters for each stop and integrated into a demand-responsive customized bus route optimization model. The objective of this model is to minimize the sum of the vehicle fixed cost, vehicle variable cost, operating time cost, and penalty costs for unserved passengers. It simultaneously optimizes vehicle routes, arrival times at stops, and passenger loads after serving each stop. An Adaptive Large Neighborhood Search (ALNS) algorithm is designed to generate initial solutions for this static stage. In the dynamic stage, the problem is approached from the perspective of en-route passengers who makes autonomous decisions about accommodating new ride requests. A group decision-making function is introduced to determine whether a vehicle should accept a dynamic request. A dynamic request assignment algorithm is then designed to update the initial routes generated in the static stage, subject to passenger preferences, time window constraints, and vehicle capacity constraints. Finally, a case study using public transit data from Beijing is presented to validate the proposed method. The results indicate that, compared to the single-stage static model, the two-stage demand-responsive customized bus network design model increases the number of boarding passengers by 2.78%; compared to a standard spatio-tamporal clustering algorithm, the strategy that incorporates passenger preference clustering increases total vehicle operating costs by 17.23% while successfully satisfying passenger preferences in the static stage. Furthermore, compared to a scenario without group decision-making, the group decision-making framework leads to a 33.33% reduction in detour distance for the demand-responsive customized bus. This method can provide a decision support for the route planning of demand-responsive customized bus systems.