Randomized trial demonstrates effective pricing and scheduling in hybrid demand-responsive transit systems, suggesting improved passenger access and system efficiency.
Key Points
This research aims to optimize pricing and scheduling for a hybrid demand-responsive transit (DRT) system that balances individual preferences with collective efficiency.
Developed a user classification-based mode choice model to capture passenger preferences.
Implemented a hybrid distance-and-service-quality-based pricing strategy within a mixed-integer programming model.
Applied a multi-start variable neighbourhood simulated annealing algorithm to solve the optimization problem.
The hybrid fare structure improved cost recovery while enhancing service accessibility for users.
The MSVNSA algorithm outperformed benchmark metaheuristics in solution quality and convergence stability.
Sensitivity analyses indicated that variations in passenger time valuation significantly impact optimal pricing strategies.