Long-term vehicle reservations in one-way free-floating carsharing systems: A variable quality of service model

Abstract

Reservations in daily services can improve user satisfaction, and give additional information about the demand patterns to the operators. However, providing reservations to carsharing clients is difficult. While carsharing is especially convenient if it is allowing one-way trips and vehicle drop-off anywhere in the service area (called free-floating), this flexibility increases management complexity because of vehicle stock imbalance. Most of the commercial providers of free-floating carsharing offer reservations under highly restrictive terms, for example only up to 30 minutes in advance. In this paper, we propose an innovative reservation enforcement technique that allows substantially longer reservation times while keeping the system profitable and achieving high service quality. A simple way to enforce reservations is locking vehicles until the departure time of a client. However, it comes at the cost of idling vehicles that could be used by other users and decreasing the revenue. Our approach, called relocations-based reservation enforcement method (R-BR) combines vehicle locking and relocation movements. It locks vehicles only a short time before the trip departure if a suitable vehicle is close enough due to the natural trip patterns. If no such vehicle is available, a car is relocated from another place. Further, we propose a variable quality of service (QoS) model in which the guaranteed radius around the user within which the reserved vehicle will be placed, and the maximum allowed reservation time before the departure depends on the zone of trip departure. A simulation-based optimization is used whereby the carsharing operation is simulated and optimized using an iterated local search (ILS) metaheuristic for adjustment of service level parameters. The proposed technique is tested on a set of artificial problem examples and a case study of a simulated working day in the Lisbon Municipality, Portugal. Results show that the proposed R-BR method is substantially better than the simple vehicle locking when the constant QoS approach is used and that the devised ILS metaheuristic can further increase the system performance, especially with high trip volumes.

Vehicle Assignment considering Battery Endurance for Electric Vehicle Carsharing Systems

“…Solutions from OR present the optimum if precise demands are known. is requires system to be long-term reservation type, or strongly assume demand can be accurately and stably predicted [35,36]. Up to now, many studies are exploring demand prediction [37,38] but still not quite accurate and robust enough to drive the optimization.…”

“…Considering that it has been reviewed that the existing one-way station-based systems are instant access [9] and the overall optimization relies on a strong assumption of long-term reservation or precise demand prediction [36], this research will focus on instant access mode and short-term reservation mode. e interaction modes are defined as follows.…”

Sustainable Mobility: A Review of Possible Actions and Policies

“…A mesoscopic approach for electric vehicles' relocation was proposed in [300] by integrating macroscopic optimization with a microscopic rule-based model. Molnar and Correia [301] proposed a joint relocation strategy considering both booked vehicles and relocation movements. Folkestad et al [302] introduced an optimal relocation model for shared electric vehicles based on genetic search.…”

Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

“…On the other hand, to make daily booking services improve user satisfaction, and to provide operators with additional information about demand patterns. Molnar and Correia [36] proposed an innovative appointment execution technique. It can keep the system profitable and achieve high service quality while achieving a longer settling time.…”