Simulating Ride-Pooling Services with Pre-Booking and On-Demand Customers (2210.06972v1)

Abstract: If private vehicle trips can be replaced, ride-pooling services can decrease parking space needed by higher vehicle utilization and increase traffic efficiency by increasing vehicle occupancy. Nevertheless, substantial benefits can only be achieved if a certain market penetration is passed to find enough shareable rides for pooling to take place. Additionally, because of their highly dynamic and stochastic nature on-demand ride-pooling services cannot always guarantee that a request is served. Allowing customers to pre-book their trip in advance could provide benefits for both aspects. Additional knowledge helps an operator to better plan vehicle schedules to improve service efficiency while an accepted trip or a rejection can be communicated early on to the customer. This study presents a simulation framework where a ride-pooling provider offers a service in mixed operation: Customers can either use the service on-demand or pre-book trips. A graph-based batch optimization formulation is proposed to create offline schedules for pre-booking customers. Using two rolling horizons, this offline solution is forwarded to an online optimization for on-demand and pre-booking customers simultaneously. The framework is tested in a case study for Manhattan, NYC. That the graph-based batch optimization is superior to a basic insertion method in terms of solution quality and run-time. Due to additional knowledge, the ride-pooling operator can improve the solution quality significantly by serving more customers while pooling efficiency can be increased. Additionally, customers have shorter waiting and detour times the more customers book a trip in advance.