Carsharing Services Research Articles

Optimizing fleet, staff configuration and operational strategies in one-way mixed fleet carsharing systems: a Lagrangian relaxation-based approach

ABSTRACT This study explores enhancing carsharing services by integrating gasoline and electric vehicles into a one-way mixed fleet carsharing system (OMFCS). The focus is on optimizing configurations (fleet and staff size, initial deployment) and operational strategies (vehicle relocation and staff rebalancing) while considering carbon emission costs. Employing a space-time-electricity network modeling approach, we developed an integer linear programming model to tackle the configurations and operational strategies optimization problem. For solving this model, we introduce a Lagrangian relaxation-branch bound approach, which integrates subgradient, dynamic programming and greedy-based heuristics algorithm. An illustrative case and a real-world case are conducted to demonstrate the efficiency of the proposed solution method and the analysis sheds light on the configurations and operational strategies of OMFCS. The sensitive analysis results suggest that OMFCS is more profitable and balances user service quality and carbon emissions better than carsharing systems using only one type of vehicle.

Reuse Intentions for Carsharing Services: A Hierarchical Model of Perceived Benefits, Risks, and Individual Differences

Reuse Intentions for Carsharing Services: A Hierarchical Model of Perceived Benefits, Risks, and Individual Differences

A Mobility Justice Framework to prioritize areas for mobility interventions

Understanding, visualizing, and quantifying how resources are allocated and the fairness of distributions and access is significant for supporting decision-makers in incentivizing development and ensuring that future changes are widely beneficial and fair. By pointing out the need for mobility justice research to shift from state-centric to more society-centric frameworks and metrics, this study proposed a Mobility Justice Framework that integrated two important theories of justice: distribution and recognition justice. The proposed framework highlights the distribution of amenities and burdens in providing transport infrastructure. It also aims to identify those disadvantaged socio-economic groups more exposed to mobility inequalities. To make the framework applicable within the city context and guide decision-making, several metrics (variables) were identified to make principles of distributive and recognition justice operationalizable. Variables such as 1) accessibility by walking, 2) exposure to traffic negative effects, 3) frequency of use of transport modes, and 4) availability of transport infrastructure were used to identify neighborhoods and the different types of mobility resources/burdens that disadvantaged socio-economic groups are exposed to. To showcase its usability, the framework and methodology have been applied to the city of Munich to highlight neighborhoods with a higher proportion of older people and the transport disadvantages associated with these target groups. A higher negative correlation between older people and mobility resources/burdens can be observed for the following variables: accessibility by walking to activities, availability of walking and cycling infrastructure, and availability of car-sharing services. When measured against exposure to negative transport effects, a higher positive correlation was observed. The paper ends with a discussion section on the relevance and usability of the proposed framework for transport planning and policy and its limitations.

Договор поминутной аренды транспортных средств (каршеринг)

The transformation of public relations under the influence of digitalization, as well as the intensification of property turnover, contributed to the formation of an economic model of sharing, which is often referred to through the prism of the English term "sharing". A striking example of such a model is carsharing, based on the idea of "mobility on demand" and being a predictable alternative to personal transport in the future. It is assumed that in the future, car owners, giving priority to car-sharing services, will give up ownership of a car, which will significantly accelerate urban traffic, free up parking spaces, and reduce the anthropogenic burden on the environment. Despite the prevalence of carsharing, especially in large and resort cities of Russia, its federal legal regulation has not yet been presented: to date, only regional experience of such regulation is known. At the same time, there are many issues in this subject area, some of which are solved by law enforcement practice. The key issue discussed in this scientific article is the choice of a contractual structure that could become an adequate model of carsharing relations.

Alternate green and carsharing mobility options: A strategy to fight climate change in oil producing countries

In Kuwait, the number of private cars is significantly high. Hence, reducing private car ownership is one of the main necessary approaches to minimize greenhouse gas emissions. Globally, cities are countering the increase in private car ownership by providing a reliable public transportation system. In Kuwait, public transport consists of a bus system, which is not widely accepted by users since it does not provide connectivity to desired locations. However, providing carsharing services and ride-sourcing can be an alternative mode. In 2019, a service called Careem was launched in Kuwait which is considered a leading ride-sourcing company in the region. Meanwhile, carsharing is not introduced yet to Kuwait. This study aims to evaluate people acceptance of carsharing and evaluate the available transport modes in Kuwait which are Careem, Taxi, Private car and Bus. 373 people participated in this study through an online questionnaire, 40% of them were willing to use carsharing when it is introduced. Additionally, Chi-square test verified that males are more likely to use taxi and Careem services. Moreover, car availability had the biggest influence on not using public transport modes. To evaluate the current transport modes and the accessibility of transport modes, 5 trips with 5 different origins and destinations were selected. The results indicated that private car is the quickest mode followed by Careem. However, public transport showed bad results with no access in Al-Jahra area, with walking time exceeding 30 min from and to the station. The required carsharing vehicles was estimated using the questionnaire data, referring to the number of carsharing in Dubai. It was concluded that there should be at least a single car sharing in 785,398 m2. In order to increase the acceptance of public transport modes, it is highly recommended to extend its tours to cover more regions and reallocate bus stations to reduce walking time for users.

Personalization of the Car-Sharing Fleet Selected for Commuting to Work or for Educational Purposes—An Opportunity to Increase the Attractiveness of Systems in Smart Cities

Car-sharing services, which provide short-term vehicle rentals in urban centers, are rapidly expanding globally but also face numerous challenges. A significant challenge is the effective management of fleet selection to meet user expectations. Addressing this challenge, as well as methodological and literature gaps, the objective of this article is to present an original methodology that supports the evaluation of the suitability of vehicle fleets used in car-sharing systems and to identify the vehicle features preferred by users necessary for specific types of travel. The proposed methodology, which incorporates elements of transportation system modeling and concurrent analysis, was tested using a real-world case study involving a car-sharing service operator. The research focused on the commuting needs of car-sharing users for work or educational purposes. The study was conducted for a German car-sharing operator in Berlin. The research was carried out from 1 January to 30 June 2022. The findings indicate that the best vehicles for the respondents are large cars representing classes D or E, equipped with a combustion engine with a power of 63 to 149 kW, at least parking sensors, navigation, hands-free, lane assistant, heated seats, and high safety standards as indicated by Euro NCAP ratings, offered at the lowest possible rental price. The results align with market trends in Germany, which focus on the sale of at least medium-sized vehicles. This suggests a limitation of small cars in car-sharing systems, which were ideologically supposed to be a key fleet in those kinds of services. The developed methodology supports both system operators in verifying whether their fleet meets user needs and urban policymakers in effectively managing policies towards car-sharing services, including fleet composition, pricing regulations, and vehicle equipment standards. This work represents a significant step towards enhancing the efficiency of car-sharing services in the context of smart cities, where personalization and optimizing transport are crucial for sustainable development.

Carsharing in China: Impact of system and urban factors on usage and efficiency

The carsharing service has experienced significant growth over the past few years in China, yet few studies have scrutinized the multi-city variations of this service. Using carsharing data from 61 cities in China, we analyzed the usage and efficiency of each city and investigated the impact of system and urban factors on the service performance. The study reveals vast differences in carsharing supply and demand across Chinese cities. Our results show the parking station density and the parking lot to vehicle ratio of the carsharing system are positively related to the usage. Urban factors such as public transportation availability, educational attainment levels, and vehicle restriction policies, are found to have significantly positive associations with the carsharing usage. However, no urban factors demonstrate significant associations with the efficiency measured by vehicle utilization rate. Moreover, the presence of other competing carsharing services within a city exhibits a positive impact on the performance of carsharing systems. This study also examined nonlinear effects of the factors. It provides valuable insights into the management of carsharing services in China, which can inform policy-making and operational strategies for sustainable development of carsharing.

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.

Dynamics in residential relocation, car ownership, and carsharing adoption in neighborhoods with a high prevalence of carsharing

Many studies have looked into the private car ownership and residential environment factors that affect current carsharing adoption from a cross-sectional perspective, while very few have focused on dynamic variables related to home relocation and changes in vehicle access. Using survey data collected in eight typical neighborhoods with a high level of carsharing in Utrecht, the Netherlands, this study explored how dynamics in car ownership, residential relocation, and current carsharing membership are related, and how this relationship shapes intention for future carsharing membership. The explanatory variables were the changes in residential location, and past and expected changes in private car ownership. Results of our binary logistic regression and ordinary least squares regression modeling show that residential relocation has a limited effect on carsharing adoption: Newcomers from lower urbanization level neighborhoods need some time to get used to carsharing and adopt it. For car ownership dynamics, future intentions to change private car ownership are more likely to affect current and future carsharing adoption than previous changes: Previous and future changes in car ownership and carsharing adoption have different effects on actual and expected behaviors. Our study shows that different functional goals of carsharing services for different types of potential users should be distinguished.

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

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

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.

Analyzing Car-Sharing Parking Time to Determine who Benefits from Autonomous Car-Sharing Services

Analyzing Car-Sharing Parking Time to Determine who Benefits from Autonomous Car-Sharing Services

Real-time vehicle relocation and staff rebalancing problem for electric and shared vehicle systems

ABSTRACT This study addresses the challenging real-time vehicle relocation and staff rebalancing (RT-VR&SR) problem in electric carsharing services. The complexity arises from ad-hoc demand, charging requirements of electric vehicles (EVs), and staff scheduling constraints. The problem aims to maximize the profit of carsharing operators by determining strategies for vehicle relocation, vehicle charging, and staff rebalancing in real-time. It considers the uncertainty of demand and the practical nonlinear charging profile of EVs. We formulate this problem as a Markov Decision Process (MDP), and propose an efficient concurrent-scheduler-based policy. Numerical experiments are conducted to demonstrate the effectiveness of the proposed policy and methodology. The results show that the proposed policy significantly improves service level and profitability compared to a benchmark policy. It is also found that ignoring staff rebalancing in decision making can lead to overestimation of service level and profitability. In conclusion, this study presents a real-time solution for vehicle relocation and staff rebalancing in one-way electric carsharing services. The proposed policy and methodology improve performance and highlight the importance of considering staff rebalancing in decision making.

Spatially-aware station based car-sharing demand prediction

In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spatial analysis are sparse in the literature. We propose to analyze the average monthly demand in a station-based car-sharing service with spatially-aware learning algorithms that offer high predictive performance as well as interpretability. Our study utilizes a rich set of socio-demographic, location-based (e.g., POIs), and car-sharing-specific features as input, extracted from a large proprietary car-sharing dataset and publicly available datasets. We first compare the performance of different modeling approaches and find that a global Random Forest with geo-coordinates as part of input features achieves the highest predictive performance with an R-squared score of 0.87 on test data. While a local linear model, Geographically Weighted Regression, performs almost on par in terms of out-of-sample prediction accuracy. We further leverage the models to identify spatial and socio-demographic drivers of car-sharing demand. An analysis of the Random Forest via SHAP values, as well as the coefficients of GWR and MGWR models, reveals that besides population density and the car-sharing supply, other spatial features such as surrounding POIs play a major role. In addition, MGWR yields exciting insights into the multiscale heterogeneous spatial distributions of factors influencing car-sharing behaviour. Together, our study offers insights for selecting effective and interpretable methods for diagnosing and planning the placement of car-sharing stations.

Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand

Uncertain demand may exacerbate the imbalance of the supply–demand for a one-way carsharing system and complicate vehicle relocation decisions. To consider the effect of the uncertainty, this study proposes a two-stage stochastic nonlinear programming model, integrating long-term and short-term decisions and maximizing the profit of the carsharing companies. Specifically, in the first stage, tactical decisions of fleet sizing and initial vehicle distribution are determined before the realization of the uncertain demand. Operational decisions of both operator-based and user-based relocation are optimized in the second stage. Moreover, this paper first studies the user-based relocation incentives, which affect the distribution of uncertain demand with an endogenous relationship. A learning-embedded optimization method is introduced to learn such a distribution, enabling the decision-making optimization model to achieve higher performance under the guidance of the demand uncertainty. Second, we envision an equitable relocation issue that considers an uneven distribution of the unsatisfied demand with two different equity criteria measured from the aspects of stations and OD pairs, respectively. Third, the large problem scale, the nonlinear objective function and constraints, and the endogenous demand uncertainty constitute the nontrivial challenges to the feasible solution. For solving the problem efficiently, we linearize the nonlinear terms and develop a dedicated two-phase solution algorithm with a learning-embedded trust-region method in phase I to solve the continuous relaxation problem and a mixed-integer linear programming guided iterative rounding in phase II to obtain the integer solutions of carsharing operations. The solution algorithm adaptively bridges the learning and optimization process via the trust-region method with flexible sample generation. Finally, we conduct numerical experiments based on a real-world one-way carsharing system in Beijing to demonstrate the effectiveness and applicability of the proposed method and reveal some insights for the carsharing service.

Sharing mode selection and optimal pricing for an electric vehicle manufacturer

This paper studies the sharing mode selection and optimal pricing for an electric vehicle manufacturer (EVM). We evaluate three sharing modes, i.e. Mode N, car sales business only, Mode S, car sales business and car-sharing service provided by a self-built sharing platform, and Mode T, car sales business and car-sharing service provided by a third-party sharing platform. Further, we consider two types of government subsidy policies: purchase and production subsidies. We find that for purchase subsidies, the EVM should select Mode S if the transaction cost per sharing service is low, and select Mode T otherwise; for production subsidies, the EVM should select Mode S. Regardless of the subsidy policy, the optimal sales prices of EVs under Mode N and Mode S are the same, but both are lower than that under Mode T. The optimal rental price of EVs under Mode S is lower than that under Mode T.

Impacts of Covid-19 on Preferences for Airbnb and Car-Sharing Services and Marketing Implications

The COVID-19 pandemic has devastated the ecosystem of tourism businesses. This study aimed to assess the effect of the pandemic on consumers’ intentions to choose sharing economy products and services. A series of five experimental designs was conducted using online panel data. Consumers were found to be less likely to choose sharing economy products under pandemic conditions. During the pandemic, cleanliness is considered more important than location when people choose Airbnb accommodation. The results of this study contribute to a better understanding of the underlying psychological mechanisms that are driving consumers’ decisions in the current environment of market uncertainty and offer evidence of the effects of the global health crisis on people's decisions to use sharing economy products in the tourism industry.

What drives the joint demand for ride-hailing and carsharing services? Understanding consumers’ behaviors, attitudes, & concerns

Driven by the emerging collaborative consumption trends, new shared ownership-based business models provide more flexible and accessible on-demand mobility options. This study simultaneously analyzes factors correlated with the consumers’ use of two interrelated disruptive on-demand mobility services, including ride-hailing (RH) and carsharing (CS). A comprehensive behavioral framework is presented to explicitly address important methodological concerns regarding the complex stochastic dependence between the use of CS and RH programs and the underlying behavioral heterogeneity in latent factors influencing the use of the two shared mobility options. Using unique data from the California Vehicle Survey, a rigorous elliptical and Archimedean copula-based finite mixture bivariate ordered probit (BOP) modeling methodology is used to understand behavioral, attitudes, and concern-related correlates of households’ participation levels in CS and RH programs. Characterized by a clayton-copula based finite mixture BOP model, participation levels in CS and RH programs exhibited complex (both synergistic and competing) non-linear stochastic dependence patterns. With a stronger left tail dependence, majority of the households having lower levels of participation in RH also had lower participation levels in CS programs. Contrarily, of the households with higher levels of participation in RH programs, the majority had lower levels of participation in CS programs (revealing weaker right tail dependence). Taste heterogeneity was observed in the unobserved determinants of CS use. Results show that current users of CS and/or RH programs tend to be those with greater awareness, owners of plug-in electric vehicles, those making greater transit trips, workers with greater commute distances, and those lacking free parking at their residences. Compared to ride-hailing, the negative effect of more frequent drivers in a household on CS use was less pronounced. Analysis of marginal and joint marginal effects provided deeper insights into the (interactive) effects of other behavioral and socio-demographic correlates. Treatment effects were simulated and discussed to further demonstrate the policy implications of our results. The new empirical insights provide a more granular understanding of the use patterns of on-demand shared mobility services and can inform more context-sensitive travel forecasts for planning and programming purposes.

Vulnerability analysis of an electric vehicle fleet for car-sharing service under cyber attacks

The booming development of the car-sharing industry and the continuous increase in the market share of electric vehicles demand effective dispatching strategies that consider collaborative optimization of route selection and charging management. Furthermore, in recent years, network attacks have posed significant threats to traffic management centers. Therefore, this paper presents a pioneering work aimed at investigating the vulnerability of electric vehicle sharing services in traffic management systems under network attacks. Firstly, we develop a bilevel optimization model to capture the game between network attackers and electric vehicle fleet operators. The upper-level problem represents the behavior of network attackers, who aim to maximize the total cost by manipulating traffic control signals and sensor data. The lower-level problem determines the system operator's decisions on optimizing route selection and charging scheduling to minimize costs. Secondly, a nested column generation algorithm is employed to solve the bilevel optimization model. Finally, several sensitivity studies are conducted using real-world traffic networks to examine the effectiveness of the proposed model and the influence of various parameters on system performance.