Vehicle Sharing Systems Research Articles

A two-stage game-theoretical framework for Pricing & Management problem of shared mobility in a competitive market

ABSTRACT With the global proliferation of shared mobility systems, the vehicle-sharing market has witnessed an influx of companies and investors, thereby intensifying competition and potentially leading to suboptimal decision-making. Consequently, there is a pressing need for a modelling framework that specifically addresses operational and strategic-level policy design for multiple vehicle-sharing companies in a competitive market. In this study, we develop a two-stage game-theoretical framework to simulate the competitive process between multiple vehicle-sharing companies, facilitating their business decisions regarding pricing and management (P&M) problem, where management mainly include service area design, vehicle deployment, redirecting, and rebalancing. In the first stage of the framework, two companies operating under the same shared mobility service form a strategic alliance to compete against another vehicle-sharing service. Subsequently, in the second stage, these two companies engage in competition to maximise their individual profits. Based on the factors such as generalised travel cost, average acceptance level and vehicle availability, the competition process of each stage is formulated as a generalised Nash equilibrium problem. A linearisation method is employed for solving the equilibrium solution. Finally, we apply our proposed models to various vehicle-sharing systems and application scenarios to demonstrate their applicability. The numerical results illustrate that our model assists companies in making rational management decisions amidst intense competition while also revealing market competition landscape of vehicle-sharing systems based on different travel distances in the shared mobility market.

Optimizing Station Placement for Free-Floating Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Beijing FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Beijing, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies.

Light electric vehicle sharing systems: Functional design of a comprehensive decision making solution

The functional design of a decision support system for a state-of-the-art vehicle sharing system (VSS) inevitably includes the selection or definition of methodologies for planning and managing the observed VSS. In this paper, our focus is on an innovative VSS that utilizes light electric vehicles. Our goal is to specify the functions of a comprehensive decision making solution for such a VSS through a qualitative and systematic literature and system analysis. To achieve this goal, we rely on the holistic VSS management framework developed in our previous research. As a result of our study, we identify possible solution methodologies or algorithms for implementing functions of the designed decision making solution, and propose the necessary adjustments for their application in the case of light electric vehicles, or note any research gap. With these findings, we provide justified guidance to practitioners in creating these solutions, with the aim of reducing development time.

Agent-based simulation of vehicle-sharing systems

ABSTRACT Agent-based simulation is a powerful tool that allows emulating the complex behaviour of individuals within a system in order to assess its global performance. In this context, this paper develops a modular agent-based simulation framework for being applied to vehicle-sharing systems. The modular structure of the simulator implies that each sub-system is treated separately, so that each module can be replaced or modified without affecting the others. In this way, the simulator can be used to analyse specific problems such as the optimal location of stations, the design of repositioning strategies, or different vehicle usage policies, by focusing only on the relevant module. The simulation framework proposed has been implemented and tested in the analysis of two different systems: a station-based bike-sharing system and a mixed (i.e., station-based and free-floating) car-sharing initiative. The paper shows some of the results obtained from such implementations as an example of its possible applications.

Optimization of bike-sharing repositioning operations: A reactive real-time approach

One of the critical issues in the operation of vehicle-sharing systems is the optimization of the fleet repositioning movements. Repositioning implies the artificial movement of vehicles from places where they accumulate to others in which they are scarce. This yields a higher vehicle availability, without over dimensioning the vehicle fleet and while increasing the vehicle utilization rates. In the particular case of bike-sharing systems, repositioning implies to deploy a fleet of small trucks or vans able to move groups of bicycles from one location to another, with the purpose of maximizing the users’ level of service while minimizing the operating agency costs. This repositioning optimization problem has been previously addressed in the operations research field through Mixed Integer Programing (MIP) and its variants, generally facing two limitations. First, its high computational cost, which prevents achieving direct solutions in realistically large systems. So, it has been necessary to develop heuristics and approximations. And second, its reliance and sensitivity to demand forecasts, with its inherent level of uncertainty. Aiming to overcome these weaknesses, this paper presents a strategy based on a real-time pairwise assignment between repositioning trucks and tasks, in order to optimize the bike-sharing repositioning operations. The proposed method is conceptually simple, less dependent on demand predictions, easily implementable in any coding language and applicable to large systems at a low computational cost. These properties make the method appealing to address the repositioning task assignment in any vehicle-sharing system. On a simulated case study, based on Bicing, the bicycle-sharing system in Barcelona, the proposed strategy has been implemented and compared to the MIP-based routing approach. Results show that the proposed real-time pairwise assignment method is able to significantly improve the performance of the repositioning operations, especially in scenarios where the demand forecast is not accurate. Being based on real-time information, the proposed strategy is flexible enough to solve unpredictable situations. So, the proposed strategy can be implemented as an alternative to MIP-based solutions, or as a complementary strategy for dynamic real-time adaptation of static long-term solutions.

Practicable Solution Approaches for Differentiated Pricing of Vehicle Sharing Systems

Practicable Solution Approaches for Differentiated Pricing of Vehicle Sharing Systems

TRIMP: Three-Sided Stable Matching for Distributed Vehicle Sharing System using Stackelberg Game

TRIMP: Three-Sided Stable Matching for Distributed Vehicle Sharing System using Stackelberg Game

Fair Fares for Vehicle Sharing Systems

Fair Fares for Vehicle Sharing Systems

Customer-Centric Dynamic Pricing for Free-Floating Vehicle Sharing Systems

Free-floating vehicle sharing systems such as car or bike sharing systems offer customers the flexibility to pick up and drop off vehicles at any location within the business area and, thus, have become a popular type of urban mobility. However, this flexibility has the drawback that vehicles tend to accumulate at locations with low demand. To counter these imbalances, pricing has proven to be an effective and cost-efficient means. The fact that customers use mobile applications, combined with the fact that providers know the exact location of each vehicle in real-time, provides new opportunities for dynamic pricing. In this context of modern vehicle sharing systems, we develop a profit-maximizing dynamic pricing approach that is built on adopting the concept of customer-centricity. Customer-centric dynamic pricing here means that, whenever a customer opens the provider’s mobile application to rent a vehicle, the price optimization incorporates the customer’s location as well as disaggregated choice behavior to precisely capture the effect of price and walking distance to the available vehicles on the customer’s probability for choosing a vehicle. Two other features characterize the approach. It is origin-based, that is, prices are differentiated by location and time of rental start, which reflects the real-world situation where the rental destination is usually unknown. Further, the approach is anticipative, using a stochastic dynamic program to foresee the effect of current decisions on future vehicle locations, rentals, and profits. We propose an approximate dynamic programming-based solution approach with nonparametric value function approximation. It allows direct application in practice, because historical data can readily be used and main parameters can be precomputed such that the online pricing problem becomes tractable. Extensive numerical studies, including a case study based on Share Now data, demonstrate that our approach increases profits by up to 8% compared with existing approaches from the literature. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2021.0524 .

Simulation and optimization of dynamic-hybrid parking reservation strategies for one-way vehicle-sharing systems

ABSTRACT In this paper, we explore the regulation of one-way station-based vehicle-sharing system (OSVS) through dynamic-hybrid parking reservation policies. We first propose a dynamic-hybrid parking reservation policy. This policy only requires trips with expected travel distances shorter than a specific threshold to make a parking reservation. The distance threshold varies with time. We develop a discrete event simulation model based on the O2DES (object-oriented discrete event simulation) framework to compare the dynamic-hybrid parking reservation (DHPR) strategy with the no-reservation (NR), static-hybrid parking reservation (SHPR) and complete parking reservation (CPR) strategies. Furthermore, we propose a simulation-optimization model and an Elitism-based Genetic algorithm with the optimal computation budget allocation to determine the fleet size, station capacity, and dynamic reservation distance threshold. The analysis of case studies of a real-world system indicates that DHPR is always superior to NR, SHPR and CPR.

The Use of Artificial Intelligence in the Assessment of User Routes in Shared Mobility Systems in Smart Cities

The use of artificial intelligence in solutions used in smart cities is becoming more and more popular. An example of the use of machine learning is the improvement of the management of shared mobility systems in terms of assessing the accuracy of user journeys. Due to the fact that vehicle-sharing systems are appearing in increasing numbers in city centers and outskirts, and the way vehicles are used is not controlled by operators in real mode, there is a need to fill this research gap. The article presents a built machine learning model, which is a supplement to existing research and is updated with new data from the existing system. The developed model is used to determine and assess the accuracy of trips made by users of shared mobility systems. In addition, an application was also created showing an example of using the model in practice. The aim of the article is therefore to indicate the possibility of correct identification of journeys with vehicles from shared mobility systems. Studies have shown that the prediction efficiency of the data generated by the model reached the level of 95% agreement. In addition, the research results indicate that it is possible to automate the process of evaluating journeys made in shared mobility systems. The application of the model in practice will facilitate management and, above all, it is open to further updates. The use of many machine learning models will allow solving many problems that will occur in an increasing number of smart cities.

Technical Note—Pricing in On-Demand and One-Way Vehicle-Sharing Networks

In this paper, we introduce a new method for evaluating the performance of static pricing in one-way vehicle-sharing systems. Our approach, based on a well-known recursive relationship, leads to a series of increasingly tight bounds on the performance of the static pricing policy. These bounds are valid for systems with multiple locations, nonzero travel times, and an arbitrary number of vehicles. They also apply to systems where the static pricing policy does not lead to a fully connected network. Our method results in a family of asymptotically optimal static pricing policies that improve upon previous results in the literature. The approach applies to the case of a single location and yields a bound that is at least as tight as the best known bound.

An equity indicator for free-floating electric vehicle-sharing systems

Sharing mobility is changing users’ behavior in urban areas, introducing a new way of movement. Electric vehicle-sharing systems are valid alternatives to the use of private modes of transport, since they allow users to cover short to medium distances with no emissions, promoting multimodality. There are two main types of vehicle-sharing systems: station-based and free-floating. The former allows for the use of a vehicle, renting and returning it from and to a fixed station. On the other hand, free-floating systems are not based on stations and a user can release a rented vehicle arbitrarily, within a predefined operating area, as close as possible to her/his destination. Although these systems are more attractive, given the freedom of parking, the spatial distribution of shared vehicles does not always allow for equitable access to the system. This research aims to propose a new way of looking at the needs of the most disadvantaged sectors of the population, evaluating the equity of free-floating electric vehicle-sharing systems. To reach this goal, a new indicator that emphasizes vertical equity is proposed. This indicator is based on the level of service intended as vehicle availability and their battery states of charge weighted by the disadvantaged population. To evaluate the effectiveness of the proposed indicator, it was applied to an electric scooter-sharing system in the city of Bari (Italy). The results obtained are interesting and show the properties of the proposal. A possible optimization model for vehicle redistribution and recharging can be based on the proposed indicator, allowing disadvantaged people to access electric vehicle-sharing systems more easily.

Optimal Control and Station Relocation of Vehicle-Sharing Systems With Distributed Dynamic Pricing

Optimal Control and Station Relocation of Vehicle-Sharing Systems With Distributed Dynamic Pricing

Compromise Multi-Criteria Selection of E-Scooters for the Vehicle Sharing System in Poland

In Poland, there is a high ratio of private transport and unfavorable patterns of daily commuting. These patterns can be changed by introducing comfortable and eco-friendly vehicles, such as e-scooters and e-bikes. At the same time, the development of the e-micromobility-based vehicle sharing services market is developing. The aim of the article is to analyze selected e-scooters available on the Polish market and to identify the most useful vehicles from two opposing perspectives, i.e., the potential customer and owner of the vehicle sharing system. The PROSA GDSS (PROMETHEE for Sustainability Assessment—Group Decision Support System) method and the graphical representation of GAIA (Geometrical Analysis for Interactive Assistance) were used to search for a compromise and balance between the needs of the indicated stakeholders. The results of the methods used were compared with the results of the PROMETHEE GDSS method, which does not take into account the balance between the stakeholders and allows for a strong compensation of the assessments of decision makers. The conducted research allowed indicating the optimal e-scooter to meet the needs of both decision makers, and it is the JEEP 2xe Urban Camou. Both the sensitivity analysis and the solution obtained with the use of the PROMETHEE GDSS method confirmed that it is the optimal alternative, the least sensitive to changes in criteria weights and changes in the decision makers’ compensation coefficients.

A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing Systems

This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse demand in a network, FFEVSS requires relocation of electrical vehicles (EVs) to charging stations and demander nodes, which is typically done by a group of drivers. A shuttle is used to pick up and drop off drivers throughout the network. The objective of this study is to solve the shuttle routing problem to finish the rebalancing work in minimal time. We consider a reinforcement learning framework for the problem, in which a central controller determines the routing policies of a fleet of multiple shuttles. We deploy a policy gradient method for training recurrent neural networks and compare the obtained policy results with heuristic solutions. Our numerical studies show that unlike the existing solutions in the literature, the proposed methods allow solving the general version of the problem with no restrictions on the urban EV network structure and charging requirements of EVs. Moreover, the learned policies offer a wide range of flexibility, resulting in a significant reduction in the time needed to rebalance the network.

Smart Recommendations for Renting Bikes in Bike-Sharing Systems

Vehicle-sharing systems—such as bike-, car-, or motorcycle-sharing systems—have become increasingly popular in big cities in recent years. On the one hand, they provide a cheaper and environmentally friendlier means of transportation than private cars, and on the other hand, they satisfy the individual mobility demands of citizens better than traditional public transport systems. One of their advantages in this regard is their availability, e.g., the possibility of taking (or leaving) a vehicle almost anywhere in a city. This availability obviously depends on different strategic and operational management decisions and policies, such as the dimension of the fleet or the (re)distribution of vehicles. Agglutination problems—where, due to usage patterns, available vehicles are concentrated in certain areas, whereas no vehicles are available in others—are quite common in such systems, and need to be dealt with. Research has been dedicated to this problem, specifying different techniques to reduce imbalanced situations. In this paper, we present and compare strategies for recommending stations to users who wish to rent or return bikes in station-based bike-sharing systems. Our first contribution is a novel recommendation strategy based on queuing theory that recommends stations based on their utility to the user in terms of lower distance and higher probability of finding a bike or slot. Then, we go one step further, defining a strategy that recommends stations by combining the utility of a particular user with the utility of the global system, measured in terms of the improvement in the distribution of bikes and slots with respect to the expected future demand, with the aim of implicitly avoiding or alleviating balancing problems. We present several experiments to evaluate our proposal with real data from the bike sharing system BiciMAD in Madrid.

Dynamic Redeployment to Counter Congestion or Starvation in Vehicle Sharing Systems

Vehicle sharing (ex: bike sharing, car sharing) systems, an attractive alternative of private transportation, are widely adopted in major cities around the world. In vehicle-sharing systems, base stations (ex: docking stations for bikes) are strategically placed throughout a city and each of the base stations contain a pre-determined number of vehicles at the beginning of each day. Due to the stochastic and individualistic movement of customers, there is typically either congestion (more than required) or starvation (fewer than required) of vehicles at certain base stations, which causes a significant loss in demand. We propose to dynamically redeploy idle vehicles using carriers so as to minimize lost demand or alternatively maximize revenue for the vehicle sharing company. To that end, we contribute an optimization formulation to jointly address the redeployment (of vehicles) and routing (of carriers) problems and provide two approaches that rely on decomposability and abstraction of problem domains to reduce the computation time significantly.

Fleet Repositioning for Vehicle Sharing Systems: the Optimality of Balanced Myopic Policy

We consider the fleet repositioning problem for a free-floating vehicle sharing system. The objective is to decide on the vehicle distribution dynamically in the network in order to maximize the long-run average social welfare, i.e., to match the vehicle supply and travel demand at the least total cost of repositioning and lost sales. We formulate the problem as a Markov decision process by considering an ex ante vehicle distribution decision in each period. Interestingly, we show that a balanced myopic policy is optimal, i.e., it is optimal to maintain the vehicle distribution which best matches the vehicle supply and trip demand. We also extend our results to systems with seasonal demand. The optimality of the (generalized) balanced myopic policy overcomes the curse of dimensionality. Our results suggest a simple and effective solution procedure for fleet repositioning, and shed light on how to design effective heuristics. We also quantify the operational value of the balanced myopic policy through a case study of a real-world vehicle sharing system.

Vehicle sharing systems: A review and a holistic management framework

Abstract Although different vehicle sharing systems (VSSs) use different vehicle types, the management challenges and optimization problems to be solved are similar or even the same. This observation led us to create a generalized and holistic VSS management framework, which aims to be applicable to the system using any vehicle type. The framework components, their mutual relationships, and framework tasks have been identified through a thorough and systematic literature review. Furthermore, the literature is positioned in the line with the framework. Finally, the framework and systematic literature review allowed us to identify gaps in the literature, and interesting research avenues.