Simulation and optimization of one-way car-sharing systems with variant relocation policies

Car-sharing is a transportation service consisting of vehicles distributed over an urban area that any driver registered to the system can use. This paper focuses on one-way electric car-sharing systems. The success of such systems relies strongly on operations management and attractive rental conditions. Immediate availability and possibility of reservation in advance are key points. This induces strong constraints for the operator especially when some stations attract more trips as a destination than as an origin and vice versa. These imbalances must be corrected by performing vehicle relocations in a smart way to maximize vehicle availability and minimize operator’s costs. In order to understand the demand patterns and explore relocation possibilities, an event-based simulator is built in C#. We develop a new relocation strategy to minimize the demand loss due to vehicle unavailability. Implemented in parallel to rentals, it relies on the regular update of the relocation plans based on an optimization framework which utilizes the current state of the system and partial knowledge of near-future demand. This strategy is compared to three other strategies on a case study based on real data from Nice, France. We show that it maximizes the number of served demand and succeeds in keeping the system in a balanced state contrary to the other strategies considered

An event-based simulation for optimizing one-way car-sharing systems

Car-sharing systems allow registered users to use cars spread throughout an urban area: vehicles are at their disposal anytime they need one against some amount of money per minute rental. The customer avoids some issues linked to the ownership of a car such as insurance fees, maintenance or parking. Such a system is beneficial for the society in terms of environmental, energetic impacts and congestion. It completes the urban transportation service by allying the efficiency of public transportation and the flexibility of owning a vehicle. Car-sharing systems can be classified in different families depending on the rental conditions. For instance, free-floating systems allow people to park the vehicles anywhere in city area whereas non-free floating impose to users to park them inside stations with limited number of allowed spots. In this last family, another differentiating feature is the "one-way/two-way" characteristic: two-way systems force the user to return the car to the location where it was picked-up whereas one-way systems allow drop-off at any station. We focus in this research mainly on non-free-floating one-way electric systems. The system operations naturally induce imbalances in the distribution of vehicles that need to be corrected by performing relocations. Our aim is to model and simulate those operations to first analyze the way the system evolves with time and then to test different management policies for operations and especially relocations in order to both maximize customers' satisfaction and make the operation of the system sustainable for the operator

Predictive dynamic relocations in carsharing systems implementing complete journey reservations

We study the operations of station-based one-way carsharing systems that enforce a complete journey reservation policy. Under such regulation, users are required to reserve both a vehicle at the origin station and a parking spot at the destination station whenever they wish to make a trip. Reservations can be made up to one hour in advance and users do not have to specify in advance the exact pick-up and drop-off times. These attractive customer-oriented rental conditions guarantee the availability of vehicles and parking spots at the start and end of the customers’ journeys but may result in an inefficient use of resources. Notwithstanding, reserved vehicles/parking spots provide information about resources that are about to become available. In this work, we develop a Markovian model for a single station that explicitly considers journey reservation information and estimates the expected near future demand loss using historical data. The output of the model is integrated in a new proactive dynamic staff-based relocation decision algorithm. The proposed algorithm was tested in the field on the Grenoble car-sharing system and compared to other dynamic and static approaches. Real-world results are reinforced by an extensive simulation experiment using real transaction data obtained from the same system

Dynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservations

In this paper, we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic staff-based relocation policies. Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. This policy is compared to a state-of-the art staff-based relocation policy and a centralistic relocation model assuming full knowledge of the demand. Numerical results from a real-world implementation and a simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy

On-line proactive relocation strategies in station-based one-way car-sharing systems

In this work, we study the integration of relocation activities and system regulations in the operation of one-way car-sharing systems. Specifically, we consider the on-line proactive planning of relocations in a one-way station-based car-sharing system that implements a complete journey reservation policy. Under such policy, a user’s request is accepted only if at the booking time, a vehicle is available at the origin station and a parking spot is available at the destination station. If a request is accepted, the vehicle is reserved until the user arrives at the vehicle and the spot is reserved until the user returns the vehicle. Each parking spot may be in one of the following states: empty free spot, empty reserved spot, available vehicle and reserved vehicle. The reserved vehicles/spots provide additional information regarding spots/vehicles that are about to become available. We thus propose utilizing this information in order to plan relocation activities and implement impactful demand shifting strategies. We devise two relocation policies and two demand shifting strategies that are based on the evaluation of the near future states of the system. Using a purpose-built event based simulation, we compare these polices to a state-of-the-art inventory rebalancing policy. An extensive numerical experiment is performed in order to demonstrate the effectiveness of the proposed policies under various system configurations

On-line proactive relocation and regulation strategies for one-way station-based car-sharing systems

This study examines the on-line proactive planning of relocations in a one-way station-based electric car-sharing system that implements a complete parking reservation policy. A Markovian model that utilizes reservation information is formulated in order to estimate the expected near-future shortages of vehicles and parking spots at each station. The outcome of the model is used in algorithms for staff-based and user-based relocations. The proposed algorithms are tested in a simulation environment using data derived from a real-world car-sharing system. In addition, in collaboration with a car-sharing operator, the algorithms are test in the field