Pricing and Scheduling for Hybrid Demand Responsive Transit System With Mixed Services and Passenger Preference

Demand‐responsive transit (DRT) offers promising mobility solutions when designed to balance individual convenience with collective efficiency. Hybrid DRT systems integrating many‐to‐one and many‐to‐many service patterns can enhance passenger sharing and vehicle occupancy, thereby improving operational efficiency and system sustainability. Furthermore, understanding passenger preferences is critical for effective service design in multimodal environments. To address these issues, this paper studies the scheduling and pricing problem for a hybrid DRT system that integrates feeder and direct services by combining two different DRT modes, namely, demand‐responsive connector and route deviation modes. To capture passenger heterogeneity in valuing service attributes, we introduce a user classification‐based mode choice model. This choice model, coupled with a hybrid distance‐and‐service‐quality‐based pricing strategy, is integrated into a profit‐maximising mixed‐integer programming model for hybrid DRT scheduling and pricing. To solve this complex optimisation problem, we develop a multi‐start variable neighbourhood simulated annealing (MSVNSA) algorithm tailored to the inherent problem characteristics. Finally, a case study based on the actual road network in Guangzhou, China, is conducted to evaluate system performance and derive managerial insights. Experimental results demonstrate that the proposed hybrid fare structure effectively balances cost recovery and service accessibility, while the MSVNSA algorithm exhibits superior solution quality and convergence stability compared to benchmark metaheuristics. Sensitivity analyses further reveal how passenger time valuation and preference heterogeneity influence optimal pricing strategies and demand composition.