Station-based Carsharing Research Articles

Carsharing adoption dynamics considering service type and area expansions with insights from a Montreal case study

Carsharing operators (CSOs) are adapting their service over time to meet changing demands and grow their market share. Service areas are enlarged and, in some cities, “dual-mode settings” evolve, incorporating free-floating carsharing (FFcs) as a new service alongside existing station-based carsharing (SBcs). This paper proposes a methodology to discuss adoption dynamics in such a context, specifically focusing on the impact of existing services and service extensions on the adoption of the new service. We propose a framework, comprising of two parts: a potential market assessment and an adoption model. The potential market assessment focuses on establishing the relationships between the local population, carsharing memberships and Points of Interest (POIs) within the given service area. The adoption model then describes the likelihood of consumers adopting the FFcs service. By combining these two models, the effects of service extensions can be assessed. We evaluate the framework using a nearly six year dataset from Communauto, Montreal. The first 35 months of data are set as training data, while the subsequent 33 months are used for validation of predictive performance. Results demonstrate that the proposed model accurately predicts adoption dynamics. Prior experience of SBcs and initial information spread are found to be key parameters for demand prediction determining early adoption peaks and, due to follower effects, also impact long-term demand. Additionally, we quantify the importance of covering residential areas and points of interests in the service area, highlighting the synergy effects of service area expansions.

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.

Can Electric Vehicle Carsharing Bridge the Green Divide? A Study of BlueLA’s Environmental Impacts among Underserved Communities and the Broader Population

This study aims to evaluate the potential of electric vehicle (EV) carsharing services to address social and environmental disparities in urban transportation through an evaluation of BlueLA, a one-way station-based carsharing service in Los Angeles, California. BlueLA provides a clean and affordable mobility option in underserved communities that face significant air quality burdens and have historically been excluded from environmental benefits. By incorporating BlueLA trip activity data from January 2021 to December 2022 (n = 59,112 trips) and an online user survey implemented in early December 2022 (n = 215 responses), we estimate the impacts of BlueLA on personal vehicle ownership patterns, vehicle miles traveled (VMT), and associated greenhouse gas (GHG) emissions. The results show an overall net reduction in VMT and GHG emissions of 463,845 miles and 656 metric tons, respectively, among the BlueLA user population (3074 registered users). When disaggregating impacts by BlueLA member type, our findings show a net reduction of 234 and 371 metric tons in GHG emissions for Standard (general population) and Community (low-income qualified) members, respectively. Additionally, our socio-demographic analysis highlights clear disparities between these two member groups, with Community members typically having lower incomes (i.e., 74% earning below USD 50,000 annually); lower educational attainment (i.e., 46% with at most an associate’s degree); and larger households (i.e., 23% living in households of four or more) compared to Standard members (i.e., 19% earning below USD 50,000, 24% with at most an associate’s degree, and 9% in households of four or more). Moreover, when comparing the VMT and associated GHG emissions due to BlueLA, we find that the presence of BlueLA reduces VMT and GHG emissions by 34% and 48% respectively, and each BlueLA vehicle replaces 16 personally owned vehicles (shed and postponed purchases). Last, when comparing the emissions produced by the electric fleet of BlueLA to those of a comparable fleet of internal combustion engine vehicles, we find that the use of an EV fleet reduces GHG emissions by 43% in comparison. The BlueLA carsharing service has led to notable net reductions in VMT and thus GHG emissions, with a major share of these reductions observed among Community members.

Operating Policies With Temporally and Spatially Varied Parameters in Station-Based Carsharing Services

This study investigates two operating policies with temporally and spatially varied parameters in station-based carsharing services, which are becoming a popular traffic mode. One is the so-called free-reservation policy, in which a user can reserve a car freely before his/her trip. The other is the extra-parking policy, in which a user can return a car to a station without valid parking slots but with a fixed extra-parking fee paid. A scheme with temporally and spatially varied parameters is proposed to maximize the total operational profit, where key service parameters in the two policies are varied with stations and periods. The strategic behavior of users is considered which involves the choices of both transportation modes and carsharing stations. A simulation-based optimization solution method is developed to optimize the key parameters in the policies, where the solution space is reduced. Extensive numerical experiments validate the solution method and the scheme with varied parameters. The results indicate that the scheme with varied parameters increases the total profit by approximately 20% compared to the scheme with unified parameters in the configuration of experiments. Several managerial implications are also proposed to help carsharing platforms utilize resources efficiently.

Shared travel demand forecasting and multi-phase vehicle relocation optimization for electric carsharing systems

ABSTRACT Electric shared mobility is flourishing in urban transportation. However, the problem of uneven vehicle distribution and untimely vehicle charging hampers user trip experience and system operation efficiency. To overcome these challenges, this study proposed a multi-phase vehicle relocation optimization approach for one-way station-based carsharing systems. In phase one, a micro-level shared travel demand forecasting model was developed to capture the number of orders in the short-term future. In phase two, stations were divided into different categories based on the results of user travel demand forecast. In phase three, the minimization of driving mileage and carbon emissions was taken as the optimization objective, and a solution method combining Gurobi solver and charging priority ranking was designed. Finally, the effectiveness and advantages of the proposed model and algorithm were comprehensively validated through a case study using real passenger orders and geographic data from the city of Shanghai, China.

Mobility transition or just another business? Temporal and spatial analysis of the diffusion of station-based carsharing services in five German municipalities

Station-based carsharing systems are regularly seen as key building blocks for a successful sustainability transition of municipal mobility systems. While numerous studies deal with the optimal distribution of carsharing stations from a business perspective, none explores which spatial and temporal patterns shape the development of carsharing station networks, even though sustainability transitions and their patterns are perceived as strictly place-specific. This study uses a comparative cross-case analysis of five southwest German cities to reveal similarities in spatial and temporal patterns. The analysis differentiates between ‘efficient causes’, described by structural and social demographic characteristics of cities, and ‘final causes’, described by the causal pathway development of municipal carsharing policy. Results reveal that despite the different city characteristics and causal pathways, the spatial evolution of station-based carsharing systems follow similar growth paths, tending towards consolidation of carsharing patterns regardless of municipal support. The study adds to our understanding of mobility transitions and casts doubt on the ability of current station-based carsharing systems to shift municipal mobility systems towards ecological sustainability.

Station-Based or Free-Floating? An Integrated Carsharing System with Dual Service Types

A specific service type is applied by most one-way carsharing operators, either station-based or free-floating, which depends on whether users are required to pick up and return vehicles in fixed parking lots. However, both of them have inherent limitations. The station-based carsharing system encounters the weakness of low parking-space utilization, whereas the free-floating system needs to face high parking and relocation costs. To overcome the limitations of single-type carsharing systems, this paper proposes an integrated system to apply the two types in different parts of the service area. A mixed-integer linear-programming model is established to maximize the operator profit, considering both strategic decisions of service type and operational decisions of relocation and trip selection. Despite the fact that the model can be solved by the CPLEX solver, it is limited by excessive memory usage and too long solving time in large-scale cases. To enhance the solving feasibility of the proposed model, a heuristic algorithm is proposed to effectively iterate feasible strategic options and solve the operational subproblem. The numerical results of the case study show that the proposed algorithm can achieve comparable objectives with the CPLEX solver in a much shorter computational time. Through system comparison, the integrated system achieves a larger profit and higher demand-fulfillment rate, compared with the single station-based or free-floating system. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services.

Dynamic pricing, vehicle relocation and staff rebalancing for station-based one-way electric carsharing systems considering nonlinear charging profile

ABSTRACT This study focuses on the imbalance of vehicles in one-way station-based electric carsharing systems (OSECSs). A simulation-based optimization framework is proposed to determine optimal OSECS operation strategies, including dynamic pricing, vehicle relocation, and staff rebalancing. In this framework, the simulation module considers the nonlinear charging profiles for electric vehicles (EVs), EV-dependent road congestion, and price-dependent elastic demands to evaluate the performance of operation strategies. The optimization module triggered by the simulation model provides the optimized configuration of operation strategies based on the genetic algorithm and simultaneous perturbation stochastic approximationTo eliminate the decision variable combinatorial explosion for the optimization problem, we apply the K-means clustering algorithm to cluster stations into small groups of cluster zones, reducing the dimensions of decision variables. A case study in Chengdu demonstrates the proposed simulation-optimization framework’s efficiency and further analyzes the impact of different operation strategies, nonlinear charging mode, and EV-dependent road congestion for the OSECSs.

Governance and business policy impact on carsharing diffusion in European cities

Congestion and pollution are common urban challenges. Among many policy measures, carsharing has been advanced as an instrument to mitigate adverse consequences of urban mobility. Municipal authorities have responded differently to its promises: some actively promote carsharing and public–private collaboration, others leave this to the market. Likewise, carsharing operators have revealed varying preferences for cities: in some they operate many cars and different business models, in other they are completely absent. Aim of the present study is to evaluate the impact of governance support and business models on urban carsharing diffusion. Based on data about 170 cities in 30 European countries, we find that governance factors, like awareness campaigns and collaboration between the public transport and carsharing operators, favorably contribute to carsharing diffusion. Also, operators’ choices regarding free-floating or station-based carsharing affect diffusion. In all, these findings contribute to the development of urban policies aimed at promoting carsharing diffusion.

Revealing influences on carsharing users’ trip distance in small urban areas

Carsharing is an essential part of the transformation towards sustainable mobility in smaller urban areas. To expand their services and the positive social and environmental benefits, carsharing operators must understand their users' travel behavior. To accelerate this understanding, we analyze usage data of a station-based carsharing service from a small city in Germany with machine learning and explainable artificial intelligence to reveal influencing factors on the trip distance. The resulting four overarching groups are personal characteristics, time-related, car-related, and environmental features. We further analyze the driving distance of several subgroups split by personal and time-related features. Our findings highlight the importance of time-related features for the trip distance of carsharing users in all subgroups. We also discuss the influence of non-time-related features on the user groups. With these results, we derive valuable insights for research and carsharing operators by understanding patterns in individual user behavior in smaller urban areas.

Optimization of personalized price discounting scheme for one-way station-based carsharing systems

As a promising pattern of shared mobility, one-way carsharing systems provide users with flexible car-rental services and have enjoyed rapid development in the past few years. Almost all previous relevant optimization studies assume that the objective of the pricing or discounting scheme is to balance the fleet from the standpoint of the carsharing companies. However, the users’ preferences for various levels of service have not been explored sufficiently. This paper introduces a carsharing personalized price discounting scheme (CSPPDS) problem for one-way reservation-based11It is assumed that users make reservations for their trips one day ahead of set-off in this work. services, considering user acceptance of the discounted price and various levels of service that the system provides to the users. To maximize the system’s profit, the presented CSPPDS problem decides the discounting scheme and whether to accept the requests. Meanwhile, the actual requests travel plans that can be different from the users’ original travel requests in pick-up locations and start times are optimized. A real-world case study based on a one-way carsharing system in Beijing is conducted to demonstrate the applicability of the presented model.

Collaborative strategic and tactical planning for one-way station-based carsharing systems with trip selection and vehicle relocation

ABSTRACT Carsharing emerges as a flexible and sustainable transportation mode to deal with severe urban challenges. This paper addresses a joint design problem of carsharing systems by an integer linear programming (ILP) model. Strategic planning of stations’ number, location and parking capacities and tactical planning of the fleet size and initial vehicle distribution are collaboratively optimized by considering all-day vehicle relocation and dynamic trip selection at the operational level. For the first time, vehicle relocation and demand selection are integrated with the joint optimization of strategic and tactical planning. We present a greedy heuristic algorithm embedding ILP to solve the problem. The case study of Chengdu demonstrates that the algorithm is computationally efficient when dealing with large-scale problems. The results identify how vehicle relocation and trip selection work in different scenes of travel time and demands and we conclude operators should target at medium-long trips by discount or promotion.

“Is Sharing Really Caring?”: The Role of Environmental Concern and Trust Reflecting Usage Intention of “Station-Based” and “Free-Floating”—Carsharing Business Models

Global use of carsharing has increased. The dominant model is station-based carsharing, but free-floating providers are continuously increasing their businesses volumes. Carsharing customers have been described as environmentally conscious with a high preference for mobility providers who show responsibility and trustworthiness. This generalization of usage determinants appears to be questionable given the background of current market developments. Existing research in the area is lacking a context-sensitive view of the antecedents of potential carsharing usage. Do environmental concerns and trust have similar effects on usage intention of carsharing, when free-floating providers implement business models that are more flexible, digital, and sophisticated? Using a standardized online survey, this study paper applied a framework adapted from the Theory of Planned Behavior (ToPB) that included the constructs of trust and environmental concern. The focus was on Share Now and Stadtmobil, which are the largest free-floating and station-based providers, respectively, in Germany. Using structural equation modeling, the study explored potential perception differences between both business models among German consumers. Although not significant, results indicate slightly higher total effects of environmental concern and initial trust on the usage intention of station-based compared to free-floating carsharing. Depending on the type of carsharing, different priorities should be set in the respective business model.

Analysis and Prediction of Carsharing Demand Based on Data Mining Methods

With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application.

Measuring Changes in Multimodal Travel Behavior Resulting from Transport Supply Improvement

Despite the desired transition toward sustainable and multimodal mobility, few tools have been developed either to quantify mode use diversity or to assess the effects of transportation system enhancements on multimodal travel behaviors. This paper attempts to fill this gap by proposing a methodology to appraise the causal impact of transport supply improvement on the evolution of multimodality levels between 2013 and 2018 in Montreal (Quebec, Canada). First, the participants of two household travel surveys were clustered into types of people (PeTys) to overcome the cross-sectional nature of the data. This allowed changes in travel behavior per type over a five-year period to be evaluated. A variant of the Dalton index was then applied on a series of aggregated (weighted) intensities of use of several modes to measure multimodality. Various sensitivity analyses were carried out to determine the parameters of this indicator (sensitivity to the least used modes, intensity metric, and mode independency). Finally, a difference-in-differences causal inference approach was explored to model the influence of the improvement of three alternative transport services (transit, bikesharing, and station-based carsharing) on the evolution of modal variability by type of people. The results revealed that, after controlling for different socio-demographic and spatial attributes, increasing transport supply had a significant and positive impact on multimodality. This outcome is therefore good news for the mobility of the future as alternative modes of transport emerge.

Optimizing the Strategic Decisions for One-Way Station-Based Carsharing Systems: A Mean-CVaR Approach

The study focuses on the strategic decisions including on the location and capacity of stations and the fleet size for designing the one-way station-based carsharing systems. Under demand uncertainty, we introduce a two-stage risk-averse stochastic model to maximize the mean return and minimize the risk, where the conditional value-at-risk (CVaR) is specified as the risk measure. To solve the problem efficiently, a branch-and-cut algorithm and a scenario decomposition algorithm are developed. We conduct computational experiments based on historical use data and generate efficient frontiers so that the system operator can make a trade-off between return and risk. We then utilize an evaluation method to analyze the necessity of introducing risk. Finally, the efficiency of the proposed algorithms is elaborated through comparative experiments. Both branch-and-cut algorithm and scenario decomposition algorithm can tackle the small- and medium-scale problems well. For large-scale problems that cannot be solved by using an optimization solver or the branch-and-cut algorithm, scenario decomposition method can provide favorable solutions within a reasonable time.

Performance of one-way carsharing systems under combined strategy of pricing and relocations

ABSTRACT A bilevel nonlinear mathematical programing model is formulated to determine the optimal pricing and operator-based relocations in a one-way station-based carsharing system in competition with private cars. In the upper level, the carsharing operator determines the vehicle fleet, prices, and relocation operations with the objective of maximizing profits, considering the potential reaction of travelers. In the lower level, travelers choose travel modes from a cost-minimization perspective. Travel utilities are calculated through a logit model. The Karush–Kuhn–Tucker conditions are used to transform the bilevel model into a single-level model and then a genetic algorithm is proposed to solve it. Computational tests in four different scenarios show the combined strategy is the best one. The four scenarios are base, relocations, dynamic pricing, and a combination of relocations and pricing separately. The combined strategy can make the best trade-offs between the operator’s profit and the travelers’ cost.

Application of German State Policy Practice Towards Carsharing in Russia

Carsharing is one of the major recent trends in urban mobility in many countries of Asia, Europe and North America. Russia is one of the leading countries in the industry in terms of growth rates and fleet volume in certain market segments. The purpose of this study is to analyze the key features of German carsharing market and policies regarding carsharing and highlight the possibilities of their application in Russia. As a result of the study a number of recommendations for carsharing development in Russia were outlined. According to several studies, stationbased schemes seem to be environmentally more beneficial compared to free-float carsharing. Station-based carsharing stimulation should be considered as a means to facilitate the development of the industry. A new metamobility policy is required, which implies carsharing, public transport, micromobility and private cars all become part of a single multimodal transport system. A transparent regulatory framework is required. New billing and payment methods, combined tariffs and a single mobile application for all means of public transport and carsharing should be considered as viable measures to stimulate the development of carsharing industry and transport system in general. Parking fees for carsharing should be reduced or waived. Leasing subsidies and preferential business conditions should be introduced to support carsharing operators. Additional taxes for personal vehicles as well as fees for parking and entering the city center using private transport should also stimulate carsharing development.

Electric Free-Floating Carsharing for Sustainable Cities: Characterization of Frequent Trip Profiles Using Acquired Rental Data

Free-floating carsharing systems (FFCS) have become a new type of urban sustainable mobility, much more flexible than the previous station-based carsharing but limited by on-street parking availability and managed by municipal administrations. Literature on FFCS until now mostly relies on survey-based methodologies and simulations, and little research on FFCS has been devoted to the scientific analysis of real flows using revealed web-based data. This paper contributes to the existing literature with an analysis of FFCS trips using rental data collected directly from operators’ websites, paying special attention to the most frequent trips. The added value of this research is that it provides the first analysis of the more FFCS demanding districts in the city of Madrid. The results showed that the main origin and destinations points were concentrated in low populated and high-income districts that also had good parking availability and connectivity to the public transportation network.

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.