One-way Carsharing Services Research Articles

A Methodology for Designing One-Way Station-Based Carsharing Services in a GIS Environment: A Case Study in Palermo

One-way carsharing is recognized as one of the most popular transportation services in urban areas, being an alternative option to private cars. Over the last decades, a vast amount of literature on the design of specific aspects of this service (fleet size, stations’ locations, fare, balancing operations) has formed. However, a holistic approach for designing carsharing services seems not to be developed. This paper proposes a new approach for designing one-way station-based carsharing services, presenting a five-step method, entirely developed in a GIS environment. The first three steps (suitability analysis, site selection analysis, and walkability analysis) allow finding the candidate locations for carsharing stations. After the assessment of the capacity of the potential stations, a location-allocation analysis allows for assessing the fleet size, the number of stations that maximize the coverage of carsharing demand, and their optimal locations. This paper presents a case study: a new one-way carsharing service was designed in Palermo (Italy) and compared to the existing carsharing service operating in the city. The results highlight that the current carsharing supply is undersized, having about 45% fewer stations and about half the cars compared to those resulting from the model, leaving some POIs unserved.

Optimal Dispatcher Number for One-Way Carsharing Services Considering Break Requirement

Optimal Dispatcher Number for One-Way Carsharing Services Considering Break Requirement

Daily Commuting and Parking Pattern under the Fully Station-Based One-Way Carsharing Vehicle Environment

This study is the first in the literature to investigate the joint equilibrium of departure time and parking location choices in the station-based one-way carsharing system. Time-varying road tolls are optimized to eliminate queueing deadweight loss, minimize schedule delay cost, and induce an economically efficient parking pattern. The round-trip commuting pattern under the fully privately owned vehicle environment was also studied, and the aggregate social cost of the two travel modes are compared with each other to verify the potential benefit gained from implementing the one-way carsharing service. We then investigated demand elasticity with respect to the user’s daily travel cost in a one-way carsharing-only system, and an externality-based charging mechanism is proposed to achieve the optimal demand-supply level. In the one-way carsharing-only travel mode, the following results were obtained: (1) the evening commute pattern is not a simple mirror image of the morning one when spatial parking is considered in both scenarios with and without pricing; (2) when taking account of elastic demand, in optimal time-varying pricing regimes, the charging scheme automatically leads to the socially optimal demand level, but in free-pricing regime, additional user anonymous tax must be charged to achieve the optimal demand level; and (3) the station-based one-way carsharing service benefits the public in aggregate social cost saving compared with the mode of driving a self-owned car.

Incentivized user-based relocation strategies for moderating supply–demand dynamics in one-way car-sharing services

One-way car-sharing services have drawn much attention in recent years in transportation research and practice. The flexible operating scheme stimulates patronage but causes unbalanced distributions of the shared cars and consequently low utilization of the services. In light of the empirical evidence that users are willing to cooperate with the operators for relocations, this paper formulates the supply–demand dynamics of shared cars under four user-based relocation strategies, including a naive one without incentive and three alternatives with incentives. We formulate the triggering conditions and travel disutilities responding to the incentive-based strategies based on the concept of load-space–time paths. To systematically assess the effects of the strategies, we propose an incentive-based boundedly rational dynamic user equilibrium (IBR-DUE) model in a multimodal transportation system. A cluster-based column generation algorithm is developed to solve the IBR-DUE model without path enumeration in a space–time supernetwork. Numerical examples demonstrate that the alternative drop-off incentive outperforms other strategies and can improve the utilization and service rates about 2 times on average.

One-way carsharing service design under demand uncertainty: A service reliability-based two-stage stochastic program approach

This study proposes a two-stage stochastic program approach to jointly optimize the planning and operational decisions for one-way carsharing systems accounting for demand uncertainty. The two-stage stochastic model is constructed in the way that stage-one determines the strategic deployments, including the number of vehicles, personnel and parking lots at each carsharing station, which is referred to as the regular service. Upon the realization of the random demand, stage-two solves the ad hoc service and optimizes vehicle relocation and personnel assignment at the operational level. The ad hoc service is integrated into the model, which serves the excess demand that cannot be covered by the regular service. For computational efficiency, we introduce the concept of service reliability (SR) to decompose the original two-stage stochastic formulation into separable sub-problems. A SR-based gradient solution procedure is then applied to efficiently solve the problem. The performance of the proposed model and algorithm is demonstrated with numerical experiments on a toy and a realistic network. Sensitivity analysis on demand variations and ad hoc service costs is further conducted to evaluate impacts of these key factors on the system performance. Results show that up to 20.49% improvement in the profit can be achieved by the proposed stochastic approach compared to the solution obtained from the historical average model.

A unified activity-based framework for one-way car-sharing services in multi-modal transportation networks

We propose in this paper a novel activity-based bi-level mathematical model to study the interaction between one-way car-sharing services and travel demand as well as their impacts on a multi-modal network performance. At the upper level, car-sharing operators decide the price of service and the plan of relocating shared vehicles among the depots. At the lower level, driven by their own activities, the decision of travelers is made bases on the equilibrium choice of routes and associated transport modes that include private cars, public transport and shared cars. Furthermore, we develop a scalable link-based method that is capable of solving large-scale network with capacitated link flow. We show via numerical examples the impact of the one-way car-sharing services on both route choices and network performance, and demonstrate the significant improvements in the obtained solution both in terms of accuracy and computational complexity.

Incentivized self-rebalancing fleet in electric vehicle sharing

With the rising need for efficient and flexible short-distance urban transportation, more vehicle sharing companies are offering one-way car-sharing services. Electrified vehicle sharing systems are even more effective in terms of reducing fuel consumption and carbon emission. In this article, we investigate a dynamic fleet management problem for an Electric Vehicle (EV) sharing system that faces time-varying random demand and electricity price. Demand is elastic in each time period, reacting to the announced price. To maximize the revenue, the EV fleet optimizes trip pricing and EV dispatching decisions dynamically. We develop a new value function approximation with input convex neural networks to generate high-quality solutions. Through a New York City case study, we compare it with standard dynamic programming methods and develop insights regarding the interaction between the EV fleet and the power grid.

Analysis of first-come-first-served mechanisms in one-way car-sharing services

The principle of “first-come-first-served” (FCFS) has been widely adopted in the deployment of car-sharing services (CSS) to manage service requests for the sake of equity. Most studies of CSS do not explicitly model the supply-demand interactions of shared cars, especially when supply insufficiency arises. This study formulates the supply-demand dynamics of one-way CSS under different FCFS mechanisms and embeds them in a boundedly rational dynamic user equilibrium (BR-DUE) problem. Two disaggregate FCFS mechanisms are suggested to improve the utilization of shared cars given the same CSS supplies in the discrete-time domain. To accurately capture the choice of CSS in space and time, a path expansion strategy is proposed to cope with different waiting times under the disaggregate FCFS mechanisms. The path expansion strategy congruently bridges the aggregate-disaggregate analyses and is incorporated in an adaptive column generation algorithm to solve the BR-DUE problem in a bi-modal supernetwork. Numerical examples demonstrate that the FCFS mechanisms have a significant influence on the supply-demand dynamics and choice of CSS.

Day-to-day dynamics of traveler learning behavior and the incentivization scheme of the operator for one-way carsharing services

In station-based one-way carsharing system, the asymmetric demand-supply issue represents a toughing challenge. This problem affects the carsharing system’s level of service as well as the financial viability, and requires the engagement of a large amount of resources in redistributing the sharing cars to meet travelers’ need. Firstly, this paper proposes an approach involves the day-to-day dynamics of traveler’s ’learning behavior’ together with an adaptive incentivization scheme of the carsharing operator. Secondly, on each day, carsharing travelers make the route choice decisions according to their perceived travel costs, which can be affected by the past experience and the incentivization scheme of the carsharing operator. More specifically, the adaptive scheme does not require specific information about travelers’ behavior traits, is adopted by the operator so as to motivate travelers to rent their car from an over supplied station and/or return it to an under supplied station, thereby reducing the expected cost of relocating the cars using dedicated staff. What is more, travelers tend to discount the value of the incentive, making it less effective in relocations. Then, the equilibrium state and stability of the evolution model is examined. Finally, numerical experiments are conducted to illustrate the application of the approach.

A diffusion model for estimating adoption patterns of a one-way carsharing system in its initial years

Abstract Oneway carsharing service operators must assess the importance of each station relative to overall demand if resources are limited. We propose a variation of an innovation diffusion model designed to estimate new “hesitant” and “fast” adopters for different stations of a one-way carsharing system to understand system adoption dynamics better over time and to derive policy implications. We forecast the number of monthly new adopters and potential market of stations considering their synergistic effects. We further reflect the spatially diverse adoption dynamics during the initial years of a carsharing service. Stations are classified into four groups based on their location and demand pattern. The models are estimated using data from the Ha:mo RIDE carsharing system in Toyota, Japan. We observe two peaks in the new user curve that our model can explain. We propose that the initial peak is caused by information diffusion, whereas the later peak is due to market saturation. Policy relevant implications are that we observe a low degree of follower effect and that new stations in strategic locations are essential for continued demand growth. More specifically, we suggest that carsharing stations in residential areas experience demand stagnation fast, whereas continued demand growth can be expected and quantified for carsharing stations around transit hubs and public facilities. Therefore carsharing operators need to be aware that assessing the importance of a station for the overall system requires time as well as consideration of synergy effect with other service points.

Fleet sizing for one-way electric carsharing services considering dynamic vehicle relocation and nonlinear charging profile

This study aims to determine the electric vehicle fleet size for one-way carsharing services by maximizing the profit of carsharing operators while taking into account the vehicle relocation operations and nonlinear electric vehicle charging profile. We formulate a set partitioning model for the considered problem. A tailored branch-and-price (B&P) approach is proposed to find the exact optimal solution of the model. In particular, an effective multi-label correcting method is developed to solve the pricing problem (i.e., generate columns) within the B&P approach. A novel non-dominated charging strategy is put up to avoid the exponential growth of labels caused by the allowance of partial charging with a nonlinear charging profile. In addition to the B&P approach, two heuristic methods are put forward for solving the large-scale problems or reinforcing the B&P approach. Numerical experiments with randomly generated instances and a case study based on a one-way carsharing operator in Singapore are conducted to further assess the efficiency and applicability of the proposed solution methods. The effects of several key parameters, i.e., the fixed cost of EV, relocation cost, electricity cost, service charge, EV driving range, the charging efficiency, and the number of rentals on the performance of a one-way electric carsharing system are also examined.

Analysis, Control, and Evaluation of Mobility-on-Demand Systems: A Queueing-Theoretical Approach

This paper presents a queueing-theoretical approach to the analysis, control, and evaluation of mobility-on-demand (MoD) systems for urban personal transportation. A MoD system consists of a fleet of vehicles providing one-way car-sharing service and a team of drivers to rebalance such vehicles. The drivers then rebalance themselves by driving select customers similar to a taxi service. We model the MoD system as two coupled closed Jackson networks with passenger loss. We show that the system can be approximately balanced by solving two decoupled linear programs and exactly balanced through nonlinear optimization. The rebalancing techniques are applied to a system sizing example using taxi data in three neighborhoods of Manhattan. Finally, we formulate a real-time closed-loop rebalancing policy for drivers and perform case studies of two hypothetical MoD systems in Manhattan and Hangzhou, China. We show that the taxi demand in Manhattan can be met with the same number of vehicles in a MoD system, but only requires 1/3 to 1/4 the number of drivers; in Hangzhou, where customer demand is highly unbalanced, higher driver-to-vehicle ratios are required to achieve good quality of service.

Electric vehicle fleet size and trip pricing for one-way carsharing services considering vehicle relocation and personnel assignment

This study proposes an interesting electric vehicle fleet size and trip pricing (EVFS&TP) problem for one-way carsharing services by taking into account the necessary practical requirements of vehicle relocation and personnel assignment. The EVFS&TP problem aims to maximize the profit of one-way carsharing operators by determining the electric vehicle fleet size, trip pricing, and strategies of vehicle relocation and personnel assignment subject to the elastic demand for the one-way carsharing services. A mixed-integer nonlinear and nonconvex programming model is first built for the EVFS&TP problem. By exploiting the unique structure of the original built model, a mixed-integer convex programming model is subsequently developed. An effective global optimization method with several outer-approximation schemes is put up to find the global optimal or ε-optimal solution to the EVFS&TP problem. A case study based on a one-way carsharing operator in Singapore is conducted to demonstrate the efficiency of the proposed model and solution method and further analyse the impact of demand, the degree of demand variation, the fixed operational cost of the vehicles as well as payment for personnel on the performance of the one-way carsharing services.

Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing

A carsharing service can be seen as a transport alternative between private and public transport that enables a group of people to share vehicles based at certain stations. The advanced carsharing service, one-way carsharing, enables customers to return the car to another station. However, one-way implementation generates an imbalanced distribution of cars in each station. Thus, this paper proposes forecasting relocation to solve car distribution imbalances for one-way carsharing services. A discrete event simulation model was developed to help evaluate the proposed model performance. A real case dataset was used to find the best simulation result. The results provide a clear insight into the impact of forecasting relocation on high system utilization and the reservation acceptance ratio compared to traditional relocation methods.

Good practices for advancing urban mobility innovation: A case study of one-way carsharing

Transforming urban mobility requires integrating public with private services into a single transportation system. Local governments and private companies face the challenge of how to coordinate themselves. An emblematic example is one-way carsharing (shared use of a fleet of vehicles that are typically free-floating throughout an urban area).Surprisingly, good practices for public and private players innovating together remain relatively undocumented. This paper proposes a systematic and balanced public-private approach to foster transportation innovation management. We review both public policy and business management literature and build a framework to help governments and companies collaborate (organizational structures, project management processes, and profitability assessment tools).We use this framework to examine both public and private experiences through a case study analysis with five one-way carsharing services in Europe (Paris, Munich) and the United-States (San Francisco, Portland, Seattle). For each we conducted expert interviews with the government and the private operator. This paper provides recommendations for both sectors. First, public and private players should have specific organizations, separated from the core business. Second, they should co-manage innovation since pilot projects lack certainty and require risk management. Third, a new approach to value emphasizing the role of project learning and capability building is necessary.

The Optimization of One-way Car-Sharing Service by Dynamic Relocation : Based on PSO Algorithm

The Optimization of One-way Car-Sharing Service by Dynamic Relocation : Based on PSO Algorithm

Carsharing Demand Estimation

Worldwide carsharing has grown significantly in recent years. The traditional round-trip model is no longer the only carsharing model offered. It is now being accompanied by more flexible options such as one-way station-based, free-floating and peer-to-peer carsharing. Moreover, it has become important to have tools that can estimate both the spatial and temporal demand for carsharing services, providing operators with a good instrument for planning their services. The work presented in this paper makes use of the multiagent simulation tool (MATSIM) to investigate the effects of supply on the demand of the existing round-trip service in the Zurich area of Switzerland. Additionally, the results provide guidance for the possible optimization of the carsharing service. Also presented is an implementation of a one-way station-based model as a part of the MATSIM framework as well as an investigation of the potentials of one-way carsharing service in the study area. Results show that there is still untapped potential of round-trip carsharing, but that service might need optimization. Furthermore, because of greater convenience, one-way carsharing would generate slightly less than three times more trips compared with the round-trip option.

Assessing the viability of enabling a round-trip carsharing system to accept one-way trips: Application to Logan Airport in Boston

Although one-way carsharing is suitable for more trip purposes than round-trip carsharing, many companies in the world operate only in the round-trip market. In this paper, we develop a method that optimizes the design of a one-way carsharing service between selected origin–destination pairs of an existing round-trip carsharing system. The goal is to supplement the established round-trip services with new one-way services and increase profitability. We develop an integer programming model to select the set of new one-way services and apply it to the case study of Boston, USA, considering only trips with one endpoint at a station in the round-trip Zipcar service network and the other endpoint at Logan Airport. The airport was chosen as a necessary endpoint for a one-way service because it is a very significant trip generator for which the round-trip carsharing is not suitable. Results show that these supplemental one-way services could be profitable. Enabling relocation operations between the existing round-trip stations and the Airport greatly improves the demand effectively satisfied, leads to an acceptable airport station size (in terms of the number of parking spots required), and is profitable; however, these benefits come with the need to manage relocation operations.