Carsharing Usage Research Articles

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.

Exploring the behavioral stage transition of traveler's adoption of carsharing: An integrated choice and latent variable model

This study investigates the process of stage transition in traveler's adoption of carsharing. The carsharing adoption behaviors are classified into five stages using the transtheoretical model: precontemplation (PC), contemplation (C), preparation (PA), action (A), and maintenance (M). In Beijing, a comprehensive survey combining stated preference and revealed preference methods is conducted to collect data on travelers' adoption attitudes and related attributes, such as socioeconomic characteristics, travel features, and carsharing usage environments. Specifically, ten latent attitudinal variables are derived from the technology acceptance model and theory of planned behavior. An integrated choice and latent variable model is developed to examine the impact of these explanatory factors on the transition processes between the five adoption stages. The findings reveal that the effects of explanatory factors on stage transitions in carsharing adoption behaviors differ across stages. Factors such as education level, income, having children, car ownership, familiarity with carsharing, personal condition, and social pressures play a crucial role in the early stages of understanding before carsharing adoption, namely the PC-C and C-PA stage transitions. Conversely, in the later stages (PA-A and A-M), travelers show a greater emphasis on carsharing infrastructure, including station reachability, vehicle availability, and perception toward rental stations and shared vehicles. Additionally, factors related to travel habits and expectations are influential throughout all stages of the carsharing adoption process. Lastly, three categories of measures, namely soft, neutral, and hard measures, are derived from the results to guide policymakers and carsharing operators in formulating strategies for the operation and management of carsharing systems.

Understanding the factors associated with one-way and round-trip carsharing usage based on a hybrid operation carsharing system: A case study in Beijing

As carsharing is promoted in many cities, studying the factors related to station-level demand under multi-mode operating carsharing systems will help better develop carsharing programs. In this paper, the number of transactions is calculated to represent the carsharing demand based on the data from a one-way and round-trip hybrid operation carsharing system in Beijing. The generalized additive model is then conducted with carsharing demand as the dependent variable and five groups of independent variables: station attributes, public transit, built environment, weather, and consumption features. The main results are: (1) the one-way station has a substitution effect on the round-trip station; (2) the one-way and round-trip stations not only have considerable demand in places well served by transit but also can play different and complementary roles in areas underserved by transit; (3) the percentage of residential POIs, the POI-mixed entropy, and the number of cars per household are the most statistically significant factors; (4) weather conditions such as precipitation, high temperatures, and poor air quality play a positive role in carsharing usage; (5) personalized incentives help attract more usage, while the positive effect is minor; (6) both population density and average consumption are highly nonlinear to carsharing usage. Population densities of around 10,000 people/km2 have the largest positive effect on one-way station usage and around 6,500 people/km2 have the largest negative effect on round-trip station usage. When the average consumption exceeds 100 RMB, the usage of one-way stations decreases more rapidly compared to round-trip stations. This study can help the operators understand and refine their business strategies, and can provide insights into determining the location and mode of carsharing stations.

A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market

In the present paper, a tool is proposed to optimally schedule the charging requests of a fleet of carsharing Electric Vehicles (EVs) in an urban area, to enable their participation in the Ancillary Service Market. The centralized scheduler minimizes the imbalance of an EV fleet with respect to the power commitment declared in the Day-Ahead Market, providing also tertiary reserve and power balance control to the grid. The regulation is carried out by optimizing the initial charging time of each vehicle, according to a deadline set by the carsharing operator. To this purpose, a nature-inspired optimization is adopted, implementing innovative hybridizations of the Artificial Bee Colony algorithm. The e-mobility usage is simulated through a topology-aware stochastic model based on carsharing usage in Milan (Italy) and the Ancillary Services requests are modeled by real data from the Italian electricity market. The numerical simulations performed confirmed the effectiveness of the approach in identifying a suitable schedule for the charging requests of a large EV fleet (up to 3200 units), with acceptable computational effort. The benefits on the economic sustainability of the E-carsharing fleet given by the participation in the electricity market are also confirmed by an extensive sensitivity analysis.

Behavioral factors impacting adoption and frequency of use of carsharing: A tale of two European cities

Innovative mobility services have emerged in many urban areas, causing a notable change in transport supply. Particularly, free-floating carsharing has allowed gaining short-term access to cars on an as-needed basis. In Europe, carsharing has experienced remarkable growth in the past few years and is often perceived as a shift towards sustainable mobility. While an increasing number of studies have analyzed individuals’ behavior, most authors analyze carsharing in contexts where this service is not available yet. Furthermore, little attention has been given to exploring the role played by psychosocial aspects. This study aims to fill this gap in the literature by exploring carsharing usage in two European cities (Madrid and Munich), cities with a different timespan implementation. Based on the individual-level dataset from a survey campaign conducted in 2019, we develop a Generalized Structural Equation Model (GSEM) that estimates how sociodemographic characteristics, mobility-related attributes, and psychological attitudes affect carsharing usage. The research identifies a higher use of free-floating carsharing among males, young, wealthy, well-educated individuals, and those who reside in inner and denser districts. Interestingly, the model results suggest that people with a high sharing propensity, variety-seeking lifestyle, and preference for driving have a significantly higher familiarity with carsharing services, while pro-environmental behaviors reduce carsharing usage. This research shows the importance of the background and sociodemographic context on carsharing usage by comparing the results obtained for each city. Finally, the paper provides interesting policy implications, helpful for planners and policymakers to better understand the factors impacting carsharing usage and its potential effects on travel behavior and sustainability.

A cooperative strategy for optimizing vehicle relocations and staff movements in cities where several carsharing companies operate simultaneously

Carsharing has become a popular travel mode owing to its convenience of use, easy parking, and low cost of using a car by those who only need it occasionally. However, because of the inadequate location of carsharing stations (station-based systems) or vehicles (free-floating systems), effectively requiring expensive and complex relocation strategies, a number of customers are lost, and some carsharing companies are facing bankruptcy. This study proposes a data-driven, dynamic, multi-company relocation method, which aims to reduce relocation costs and increase profit in one-way carsharing station-based systems through cooperative strategies. The method starts from the prediction of carsharing inflows and outflows at each station throughout the day using a new deep learning algorithm designated as “the attention-enhanced temporal graph convolutional network”. It adopts an encoder-decoder structure to simultaneously capture the temporal and spatial carsharing usage patterns. A two-phase integer programming model is proposed to optimize the process of vehicle relocation and staff rebalancing with cooperative relocation strategies: the sharing of relocation staff, the sharing of vehicles and stations among the different companies. An adaptive large neighborhood search based heuristic approach is implemented to solve the two-phase model. Based on the 6-month travel records from four carsharing companies operating simultaneously in Fuzhou, China, the proposed model and cooperative strategies are assessed. The results show that the total profit of the four carsharing companies can be increased by 25.49% with the cooperation of staff and vehicles. In addition, we prospect the future relocation with automated vehicles, whereby the profit can be increased by 46.69% without the need to employ the relocation staff.

Who will use new mobility technologies? Exploring demand for shared, electric, and automated vehicles in three Canadian metropolitan regions

So-called “new mobility” innovations could transform transportation systems, including the deployment of shared, electric, and automated vehicles. However, researchers are still learning about who is currently using these technologies (“realized demand”) and who is interested in future usage (“latent demand”). We explore these patterns via a representative survey including three of Canada's largest metropolitan regions: Vancouver, Toronto, and Montreal (n = 3658, June 2020). Realized demand is assessed as respondents who use or own the technology (pre-pandemic), while latent demand is assessed as respondents who report interest in using the technology (post-pandemic). For most technologies, latent demand is higher than realized demand, indicating the potential for market growth. Exceptions are ride-hailing and pooled ride-hailing in Toronto and Montreal, where latent demand is lower, likely due to concerns relating to the COVID-19 pandemic. There is regional variation in realized and latent demand for shared mobility: ride-hailing usage is highest in Toronto and car-sharing usage and interest are highest in Vancouver (each corresponding to regional availability). Further, latent demand for electric vehicles and fully automated electric vehicles are highest in Montreal. Otherwise, latent demand levels for the remining technologies are similar across regions. Regression models indicate that latent demand for each technology is associated with respondents' travel patterns, demographics, values, lifestyles, and environmental concern. Regional effects hold in descriptive and regression analyses, for example higher car-sharing latent demand among Vancouver respondents and higher electric vehicle latent demand among Montreal residents, suggesting regional context plays a role in explaining latent demand.

“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.

Carsharing in the Czech Republic: Understanding why users chose this mode of travel for different purposes

Carsharing is a relatively new and growing mobility service in many cities. Although there are numerous carsharing initiatives in operation around the world, it is still not clear how they will affect overall travel behavior. Understanding the effects of carsharing when combined with other existing travel modes is an important prerequisite for decision-makers for them to be able to positively utilise the benefits of carsharing services. Such new mobility services need to be incorporated into existing modelling tools to reliably evaluate the impact of specific measures and policies.The presented study provides an insight into one aspect of such models: the use of carsharing for various trip purposes by users with different socio-demographic characteristics and attitudes. We identify variables that should be used for computing the utility function for users with different backgrounds, perceptions, and preferences.In this study, existing users of a carsharing company operating in various Czech cities participated in a survey and their responses were paired with records of their real carsharing usage patterns. To reduce the number of variables, principal component analysis was used, mainly with attitudinal and perception questions. The principal components based on survey respondents’ attitudes towards carsharing and socio-demographic and other variables were used in logistic regression to identify parameters that most significantly influence various purposes for trips.The results confirm the hypothesis that trip purposes differ significantly among carsharing users and need to be incorporated into macroscopic carsharing usage models. These findings can assist municipality representatives and transport companies in considering carsharing as a valid and sustainable travel mode in traffic models and studies.

User characteristics of shared-mobility: a comparative analysis of car-sharing and ride-hailing services

ABSTRACT Over the past 20 years, shared-mobility services have become important transportation options, as they provide on-demand, door-to-door mobility without requiring vehicle ownership. Although low-income communities may benefit especially from the services due to their lower vehicle ownership and high dependency on public transit, scant research has been conducted exploring how frequently these individuals utilize the shared-mobility programmes. This study develops a mathematical model based on Zero Inflated Negative Binomial Regression to understand the effects of individuals’ sociodemographic characteristics, financial status, and travel behaviours on car-sharing and ride-hailing usage. The model outcomes indicate that the individuals experiencing financial burden are more likely to use car-sharing services while those with a higher income tend to use ride-hailing. Ride-hailing tends to serve those who have lower miles driven or those who use public transit. Results show that car-sharing and ride-hailing could provide create synergetic impacts to attract more riders to the shared-mobility services.

The Impact of the Covid-19 Pandemic on Consumers' Intention to Use Shared-Mobility Services in German Cities.

One sector that severely suffers from the outbreak of the coronavirus is carsharing (i.e., short-term car access). The downswing of the carsharing industry may not only experience negative economic consequences but also ecological ones. Carsharing has the potential to reduce emissions, occupied space, and congestion and hence can actively contribute to mitigating climate change. As Bill Gates strikingly states: “Covid-19 is awful. Climate change could be worse.” For this reason, it is important to understand which underlying mechanisms drive carsharing usage during the Covid-19 pandemic. The current research has the overall objective to provide deeper insights into the mediating mechanisms that explain carsharing usage intention during the Covid-19 pandemic. In particular, we draw on signaling theory to explore how different claims (environmental claims, safety claims) that prompt two different opposing underlying processes (perceived ecological benefits, perceived physical risk) influence carsharing usage intention. An online experiment employing a 3 (environmental claim vs. safety claim vs. no claim) × 2 (high information diagnosticity vs. low information diagnosticity) between-subjects design with participants acquired by the online panel platform Clickworker was conducted in April 2020. Fictitious labels and fictitious advertisements served as stimulus material and constituted the five experimental conditions. The data were analyzed by a multicategorial moderated mediation analysis and a multivariate analysis of covariance. Results reveal that environmental claims can stimulate perceived ecological benefits, which, in turn, positively affect carsharing usage intention. Interestingly, our research demonstrates that safety claims cannot decrease perceived physical risk in the context of Covid-19 and carsharing. Nevertheless, perceived physical risk has a (marginal) negative influence on carsharing usage intention and hence should not be discarded altogether. The findings of this article offer new insights into the mental processes that guide consumer decision-making during the coronavirus crisis and also offer important policy implications by highlighting the relevance of environmental claims during the Covid-19 pandemic. Furthermore, the negative influence of perceived physical risk on carsharing usage intention points to the need for alternative measures to reduce users' risk perceptions.

You'll never share alone: Analyzing carsharing user group behavior

The rapidly developing concept of carsharing is an essential and scalable part of sustainable, multimodal mobility in urban environments. There is a clear need for carsharing operators to understand their users and how they use different transportation modes to intensify the development of carsharing and its positive impacts on the environment and urban cohabitation. We foster this understanding by analyzing usage data of carsharing in a medium-sized German city. We compare user groups based on individual characteristics and their carsharing usage behavior. We focus on a station-based two-way carsharing scheme and its relation to free-floating carsharing. Based on different clustering and segmentation approaches, we defined 20 particularly interesting user groups among the carsharing users and analyzed noticeable usage patterns. Additionally, we examined these partially overlapping user groups in the spatial dimension. With these results, we support research and operators in understanding carsharing customers and assessing users’ individual behavior.

Latent stage model for carsharing usage frequency estimation with Montréal case study

In order to predict the monthly usage frequency of members of a car-sharing scheme by analysing the gradual change of behaviour over time, a new model is proposed based on the Markov Chains model with latent stages. The model accounts for changing patterns of frequency from soon after signing up to later stages by including five latent user ‘life stages’. In applying the model to panel data from Montreal’s free-floating carsharing service the authors calculate each user’s ’lifetime’ applied to ‘system operation time’, the time period since the start of the scheme. Three-fold validation reveals effective performance of the model for both lifetime and system operation time dimensions. The model is further applied to illustrate how previous carsharing experience and the extension of the scheme to a larger area can affect usage frequency changes. We conclude that this approach is effective for usage prediction for novel transport schemes.

MaaS 도입 가능성과 결정요인 분석 : 송도국제도시를 중심으로

Mobility as a Service (MaaS), which emerged as an innovative concept, is expected to significantly change the paradigm of urban mobility in the near future. In recent years, various studies related to MaaS have been conducted in academia and industry, but research on the knowledge of the introduction possibility and determinants of MaaS has been limited. This study analyzes the introduction possibility and determinants of MaaS based on the survey results of residents in Songdo City, South Korea. We built an ordinal logistic model on the possibility of MaaS, assuming that the higher the rate of willingness to pay for using MaaS, the greater the likelihood of being positive in MaaS. The analysis results indicate that public transportation has a substitutive relationship with MaaS. The greater the convenience of public transportation, the lower the willingness to pay for MaaS. This finding indicates that MaaS would provide mobility benefits in some areas with a lower level of public transportation services. On the other hand, this study shows that the convenience of bicycle mobility has a complementary relationship with MaaS. This finding indicates that MaaS should consider solid connections with the bicycle mobility system. In addition, this study shows that, while respondents with higher taxi usage are more likely to pay for a higher fare of MaaS, those with higher car-sharing usage are less likely to pay for a higher fare for MaaS. Finally, this study suggests policy implications for the successful introduction of MaaS in Songdo City.

Characterising Annual Behaviour of Carsharing Users in Montreal

Carsharing is one of the emerging modes of transportation in the recent years, as a pointer to the fact that owning a private car is not all we need for the sake of travelling. Using k-means clustering and Principal Component Analysis, the purpose of this paper is to study the behaviour of the Communauto regular-service carsharing users in Montreal over a year (2014) and find the usage patterns in each cluster. Also, by having the Communauto customer features available, the characteristics of the customers in each cluster will be defined, which eventually ends in customer profiles. The k-means clustering results show that the Communauto regular-service carsharing users are divided into nine different clusters. Some of the clusters patterns are similar during the weeks and some are similar during a year, but none of them are similar during week and year. So, each cluster has a unique usage pattern over the weeks and the year. Furthermore, the resulting clusters are ordered from highly intensive users to the occasional ones based on the frequency of carsharing usage over one year.

Understanding the antecedents of car sharing usage: an empirical study in Italy

PurposeSustainable mobility and collaborative consumption are debated issues in the literature. In this field, car sharing (CS) represents a growing tendency that attracts interest by academicians for its potential positive impact on sustainability. This study aims to understand the main drivers of CS usage, unveiling the role of service quality and the possible inhibitors.Design/methodology/approachBased on theoretical assumptions in the literature, the authors developed a theoretical framework that aims at understanding the main antecedents of CS usage. An empirical investigation involving the city of Rome (Italy) was performed. The study presents a qualitative and quantitative survey, while the proposed theoretical model has been tested through structural equation modelling statistical techniques.FindingsThe findings show that usefulness positively affects the intention to use CS services. Moreover, the green attitude and expected service quality indirectly influence the intention to use CS services impacting perceived usefulness. The results show that the expected service quality has a strong influence on usefulness, whereas green attitude has a weaker influence on usefulness. The intention to use CS services is directly influenced by the usefulness and inhibitors that affect it negatively.Originality/valueThe originality of this paper lies in the fact that this study discloses the main antecedents of the intention to use CS services, emphasizing the role of service quality. This study provides fruitful insights to policy and decision-makers to understand how to improve the CS usage in the urban transport system of the city of Rome. It suggests developing a higher level of service quality in the vehicles, suitable technological applications and effective communication as well as on overcoming its main inhibitors.

Travel demand and distance analysis for free-floating car sharing based on deep learning method.

In order to address the time pattern problems in free-floating car sharing, in this paper, the authors offer a comprehensive time-series method based on deep learning theory. According to car2go booking record data in Seattle area. Firstly, influence of time and location on the free-floating car-sharing usage pattern is analyzed, which reveals an apparent doublet pattern for time and dependence usage amount on population. Then, on the basis of the long-short-term memory recurrent neural network (LSTM-RNN), hourly variation in short-term traffic characteristics including travel demand and travel distance are modeled. The results were also compared with other different statistical models, such as support vector regression (SVR), Autoregressive Integrated Moving Average model (ARIMA), single and second exponential smoothing. It showed that (LSTM-RNN) shows better performance in terms of statistical analysis and tendency precision based on limited data sample.

Carsharing: Another gender issue? Drivers of carsharing usage among women and relationship to perceived value

The sharing economy is competing with traditional companies in many different industries and markets nowadays. One of first and most strongly affected industries is transportation. Efforts have been directed at finding more sustainable ways of moving from place to place and reducing deficiencies in public infrastructure. Carsharing has been promoted as a relatively sustainable means of travel. However, new means of travel may increase gender gaps or create new ones. A survey of 228 women was carried out to investigate use of both car clubs and peer-to-peer carsharing, analysing the carsharing business model and personal motivations. We found that women use carsharing when they perceive business and personal value in doing so. Nevertheless, there were significant differences in perceptions of car club and peer-to-peer carsharing. Implications for management, policy and research are presented to promote efforts to eliminate gender gaps in transportation.

Factors associated with round-trip carsharing frequency and driving-mileage impacts in London

This study draws on respondents from Greater London within the 2016/17 wave (n = 2640) of Britain’s Annual Survey of carsharing users, which we enrich with external data from the 2011 England and Wales Census and small-area income estimates. Focusing on round-trip carsharing users, we present multivariate analyses of frequency-of-usage of carsharing vehicles and impacts on annual vehicle miles traveled (VMT). Published attribute effects from other geographic and contextual circumstances are compiled and compared (where direct comparison is possible) with the specific attribute effects that we report in this paper. We demonstrate a statistically significant link between customer satisfaction with proximity of carsharing vehicles and VMT impacts. Car ownership (both current, and changes upon joining a carsharing service) is shown to have intuitive structural impacts. We find that frequent usage is associated ceteris paribus with increasing VMT after joining a carsharing service, and that subscribing to multiple types of carsharing is associated with frequent carsharing usage and a reduction in VMT. Interestingly, we did not find any significant effect of household income on either frequency-of-usage or VMT impacts.

Locating Station of One-Way Carsharing Based on Spatial Demand Characteristics

This study aims to explore factors that affect carsharing demand characteristics in different time periods based on EVCARD transaction data, which is the largest station-based one-way carsharing program in Shanghai, China. Monthly usage intensity and degree of usage imbalance are used as proxies of demand. This study uses three groups of independent variables: carsharing station attributes, built environment (density, diversity, design, and destination accessibility), and transportation facilities. The adaptive elastic net regression is developed to identify factors that influence carsharing usage intensity and degree of usage imbalance after factor selection using extra-randomized-tree algorithm. Finally, a station layout is proposed according to both usage intensity and degree of imbalance. The main results of this study are presented as follows: (1) different effects of built environment and transportation factors cause dynamic demand across different time periods; (2) factors with positive and negative effect on carsharing demand are divided clearly for guidance of the carsharing station layout; (3) public parking space leads to more personal vehicle trip compared to a carsharing trip; and (4) as public transportation, the relationship of the metro and carsharing is complementary. However, the bus stop and carsharing have a competitive relationship. This study provides a carsharing layout method based on both usage intensity and degree of imbalance. Furthermore, several policies concerning carsharing are proposed.