Free-floating Carsharing Service Research Articles

Is access enough? A spatial and demographic analysis of one-way carsharing policies and practice

For low-income individuals, carsharing services that provide short-term, on-demand access to a fleet of shared vehicles may be a viable, low-cost alternative to personal vehicle ownership, yet the demographics of carsharing users often reflect higher income groups. Our objectives in this research study are: 1) quantify the benefits of carsharing for low-income users; 2) evaluate the effectiveness of policies targeting spatial access (e.g., parking policies, designation of service areas); and 3) review other social equity initiatives taken by carsharing operators (e.g., low-income discounts, educational training) and impacts on user demographics. We use two one-way, free-floating carsharing services as case studies: a survey of GIG (Get In and Go) Car Share users in Oakland, CA from 2018 (n = 218), and a survey of car2go users (now called ShareNow) in five North American cities from 2015 (n = 9497). We analyze these surveys using descriptive statistics, hypothesis testing, and geographic information systems (GIS) mapping.We find significantly more low-income respondents in Oakland do not have a personal vehicle (70%) compared to high-income respondents (44%). Meanwhile, 62% of our sample was Caucasian, and 41% earned an annual household income of more than $100,000, similar to results of the car2go survey from 2015, indicating that the demographics of one-way carsharing users have not changed much over time.We find that policies expanding carsharing service areas into equity priority zones can potentially attract new members. Washington, D.C. and Oakland included equity service area requirements in carsharing permits and had a higher percent of users residing in equity priority zones (21% and 33%, respectively), compared to Calgary and Vancouver that did not (12% and 4%, respectively). However, the demographics of carsharing users still do not reflect the demographics of equity zones. Policies to increase spatial access of carsharing are insufficient on their own to improve social equity in carsharing. Based on findings from previous carsharing equity pilots, policy initiatives such as discounted memberships for low-income users and hands-on educational outreach are needed to advance social equity.

Forecasting the carsharing service demand using uni and multivariable models

Carsharing is ana lternative to urban mobility that has been widely adopted recently. This service presents three main business models: two of these models base their services on stations while the remainder, the free-floating service, is free of fixed stations. Despite the notable advantages of carsharing, this service is prone to several problems, such as fleet imbalance due to the variance of the daily demand in large urban centers. Forecasting the demand for the service is a key task to deal with this issue. In this sense, in this work, we analyze the use of well-known techniques to forecast a carsharing service demand. More in deep, we evaluate the use of the Long Short-Term Memory (LSTM) and Prophet techniques to predict the demand of three real carsharing services. Moreover, we also evaluate seven state-of-the-art forecasting models on a given free-floating carsharing service, highlighting the potentials of each technique. In addition to historical carsharing service data, we have also used climatic series to enhance the forecasting. Indeed, the results of our analysis have shown that the addition of meteorological data improved the models’ performance. In this case, the mean absolute error of LSTM may fall by half, when using the climate data. When considering the free-floating carsharing service, and prediction for the short-term (i.e., 12 hours), the boosting algorithms (e.g. XGBoost, Catboost, and LightGBM) present superior performance, with less than 20% of mean absolute error when compared to the next best-ranked model (Prophet). On the other hand, Prophet performed better for predictions conducted on long-term periods.

A multi-faceted characterization of free-floating car sharing service usage

During the last decade, car sharing systems appeared in many cities and gained popularity. The research community has analyzed their current utilization trends in different contexts, their growth perspectives, and their gradual shift towards more sustainable technologies. Through the large and heterogeneous amount of car sharing usage data that is now available, researchers have been able to gain new insights into these services. In this paper, we provide an extensive characterization of the Free-Floating Car Sharing (FFCS) service usage in 23 cities in Europe and North America over a 14-month period. From our data about FFCS services, we detail fleet size, operating area, and characteristics of the car bookings and rentals. We also identify temporal patterns that are peculiar to specific cities and countries. We further highlight urban zones with high attractiveness or with a high rental generation rate. Finally, we compare the systems relying on internal combustion engine cars with those based on electric vehicles in terms of various indicators, including the influence on car refueling. The results show that car utilization patterns are rather variable across cities with the highest per-car utilization rate in Madrid. The majority of the cities show negative or stable usage trends due to either the reduced appeal of the service or the presence of inefficiencies in the service provision. These data-driven insights may help system managers assess the provided services’ profitability and sustainability from multiple perspectives.

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.

Does free-floating carsharing reduce private vehicle ownership? The case of SHARE NOW in European cities.

During the last decade, the use of free-floating carsharing systems has grown rapidly in urban areas. However, little is known on the effects free-floating carsharing offerings have on car ownership in general. Also the main drivers why free-floating users sell their cars are still rarely analysed. To shed some light on these issues, we carried out an online survey among free-floating carsharing users in 11 European cities and based our analysis on a sample of more than 10,000 survey participants. Our results show that one carsharing car replaces several private cars - in optimistic scenarios up to 20 cars. In Copenhagen (followed by Rome, Hamburg, and London) one carsharing car replaces about two times more private cars than in Madrid, the city with the lowest number. The main non-city specific influencing factor of shedding a private car due to the availability of the free-floating carsharing services seems to be the usage frequency of the service. The more kilometres users drive with these cars, the more likely it becomes that they sell a private car (or they sell their car and, therefore, use this service more often). Further memberships of bikesharing and other carsharing services, users that live in larger buildings as well as users that own several cars are more likely to reduce their number of cars, too. Finally, our findings are highly valuable for carsharing operators and (transport) policy makers when introducing free-floating carsharing systems in further cities. According to our results, all 11 cities show a reduced private car fleet due to members' access to free-floating carsharing.

Carsharing in the Brussels region

The aim of this fact sheet is to highlight the differences and commonalities between the user profiles of station-based and free-floating carsharing services based on the analysis of a survey of 5 375 car sharing users in Brussels. The analysis shows that free-floating users have a higher rate of car ownership, higher share of males and they are slightly younger. Both types of services cater to individuals with a high level of education and are used approximately 1 to 3 times a month. The two services clearly serve different purposes, with a greater variety of travel motives stated by the users of free-floating services and the rates of car ownership much higher. In addition, users reportedly reduce their car use once they become members, while the impact on public transport use remains unclear, with free-floating users replacing public transport trips partly with carsharing.

Predicting Car Availability in Free Floating Car Sharing Systems: Leveraging Machine Learning in Challenging Contexts

Free-Floating Car Sharing (FFCS) services are currently available in tens of cities and countries spread all over the worlds. Depending on citizens’ habits, service policies, and road conditions, car usage profiles are rather variable and often hardly predictable. Even within the same city, different usage trends emerge in different districts and in various time slots and weekdays. Therefore, modeling car availability in FFCS systems is particularly challenging. For these reasons, the research community has started to investigate the applicability of Machine Learning models to analyze FFCS usage data. This paper addresses the problem of predicting the short-term level of availability of the FFCS service in the short term. Specifically, it investigates the applicability of Machine Learning models to forecast the number of available car within a restricted urban area. It seeks the spatial and temporal contexts in which nonlinear ML models, trained on past usage data, are necessary to accurately predict car availability. Leveraging ML has shown to be particularly effective while considering highly dynamic urban contexts, where FFCS service usage is likely to suddenly and unexpectedly change. To tailor predictive models to the real FFCS data, we study also the influence of ML algorithm, prediction horizon, and characteristics of the neighborhood of the target area. The empirical outcomes allow us to provide system managers with practical guidelines to setup and tune ML models.

Identifying user classes for shared and automated mobility services

New forms of shared mobility such as free-floating car-sharing services and shared automated vehicles have the potential to change urban travel behaviour. In this paper, we identify potential user classes for these new modes. For this, a stated choice experiment on mode choice among a sample of the Dutch urban population has been conducted, which features free-floating car-sharing and shared automated vehicles next to private vehicles, bus, and taxi. The experimental design allows disentangling the effects of vehicle ownership, vehicle sharing and vehicle automation on the perceived utility of these modes. Further contributions lie in the identification of user classes for shared and automated mobility services and their potential migration from their current modes to the these services. Latent class choice models were estimated to capture the heterogeneity in these preferences among the respondents. The most explanatory mode choice model is obtained by estimating a 3-class nested logit model capturing the impact of vehicle ownership. The results show that higher educated and more time-sensitive respondents are more inclined than others to favour the (automated) car-sharing options. By simulating a scenario that directly compares car with free-floating car-sharing and taxi with shared automated vehicles, a migration analysis has been performed. This analysis shows that the preferences towards shared automated vehicles and free-floating car-sharing is highest for those currently combining car and public transport for their commute. Commuters using the car showed a high preference towards free-floating car-sharing, in particular as for the latter no parking fees are issued. Respondents currently commuting by public transport showed the lowest preference for the new modes.

Understanding the spatial distribution of free-floating carsharing in cities: Analysis of the new Madrid experience through a web-based platform

In recent years the sharing economy has become established in different modes in the urban transport system, and claims to be reducing the number of cars and contributing to lower traffic pollution. Free-floating carsharing (FFCS) is a new and more flexible type of carsharing that is driving the growth of carsharing markets around the world. While there is a very extensive literature on traditional carsharing, more research needs to be done on the new FFCS trip profile in order to estimate its spillover effects on the urban transportation system. As FFCS systems are based on ICTs, new web-based methodologies (instead of traditional surveys) are the best approach to analyse them.This paper contributes to the existing literature with a spatial evaluation of the FFCS trip profile, obtaining a temporal distribution of the main flows throughout the FFCS service area. The added value of this research is that it provides the first spatial analysis of a FFCS system in Spain using rental data collected from the operators' websites. The results clearly show the prevalence of the short-distance FFCS trip that is faster than available public transport and whose origin and destination are closely dependent on parking availability.

Environmental sustainability with free-floating carsharing services: An on-demand refueling recommendation system for Car2go in Seattle

Free-floating carsharing has become an increasing concern in recent years. It is a key contributor to the sharing economy, which is beneficial to environmental sustainability through energy conservation, pollution reduction, and low carbon emissions, ultimately improving people's quality of life. This study proposed an on-demand refueling recommendation system to enhance the service performance of free-floating carsharing businesses for better mobility and environmental sustainability. In this system, a method based on data analytics for identifying the appropriate refueling level is designed. One of the most popular free-floating carsharing services, Car2go, is taken as a case study. Based on relevant data from Car2go in Seattle, the on-demand refueling recommendation system is developed and evaluated. This system could be adapted by other free-floating carsharing companies into similar recommendation systems to improve the companies’ operations. It would provide further support for sustainable mobility and have an even greater impact on people's quality of life.

Modelling the determinants of car-sharing adoption intentions among young adults: the role of attitude, perceived benefits, travel expectations and socio-demographic factors

Major cities in developing countries are increasingly becoming motorized. Thus, effective solutions to address the negative impacts that come with rising car-ownership are needed as part of an overall travel demand management strategy. In developed and emerging economies, shared-mobility in the form of car-sharing is becoming popular as potentially low-cost and environmentally sustainable alternative to car-ownership. Yet, our understanding of car-sharing adoption and diffusion factors in developing countries is limited. In this study, we fill this gap by examining car-sharing adoption intentions among young adults aged between 18 and 35 years in Ghana, Sub-Saharan Africa. Using structural equation modelling, we model car-sharing adoption intentions based on a framework that integrates individuals’ perception of the benefits of car-sharing, attitudes towards the environment and technology, trust of stewardship in car-sharing, perception of innovativeness of car-sharing, travel expectations and socio-demographic factors. We found that pro-technology and pro-environmental attitudes correlate positively with perceived benefits of car-sharing. Perceived benefits of car-sharing, in turn, has the largest predictive effect on intentions to car-share. Other factors, including individuals’ previous experience using Uber on-demand taxi services, gender, education, driver’s licensure and expectation of comfortable and fast travel options, all predict car-sharing adoption intentions. While there exists an interest in both station-based and free-floating car-sharing services, more of the would-be users favour the latter than the former. Also, majority of the potential adopters (62%) would join a car-sharing service within the first 1 year of its introduction. An important finding is that dissatisfaction with existing public transit services underpins car-sharing intentions, implying that relying on car-sharing alone to meet travel needs, without a holistic strategy of providing quality and affordable public transit services, could lead to unsustainable outcomes.

Traveller preferences for free-floating carsharing vehicle allocation mechanisms

Free-floating carsharing (FFCS) fleets are inherently volatile spatio-temporally, which presents both a logistical challenge for operators and a service reliability issue for customers. In this study we present a stated-choice survey to investigate the attractiveness to customers of two mechanisms for managing fleet volatility: Virtual Queuing (VQ) and Guaranteed Advance Reservation (GAR). We investigate socio-demographic features and “Big Five” personality traits that are associated ceteris paribus with choosing to use the existing FFCS service model, willingness-to-pay (WTP) for VQ and GAR, and risky-choice behaviour under the uncertainty of FFCS systems. Data (n = 289; 232 employed in analysis) are sourced from existing users of a FFCS service in London, UK. Within the survey context, we found that customers are on average not willing to pay for VQ (i.e. negative WTP), however have £0.54 per journey WTP for GAR, with low-frequency FFCS users and users scoring highly on the Big Five “Conscientiousness” dimension having larger WTP for GAR. When analysing the two dimensions of uncertainty, we found that respondents exhibit risk-seeking behaviour towards price and weaker and insignificant risk-aversion towards walking time. This pattern holds across the three standard model types of nonlinear risky-choice behaviour that we investigated. The results are intended to be useful both to policymakers and carsharing operators who are likely, as the industry matures, to seek mechanisms to differentiate their service offers to better serve individual market segments with distinctive characteristics.

Exploring Service Usage and Activity Space Evolution in a Free-Floating Carsharing Service

This paper proposes to empirically investigate the members’ behaviors over time in a free-floating carsharing system. With a continuously evolving service in terms of service area and fleet size, member usage intensity and activity space are explored with passive data streams. Members are labeled according to their usage intensity for various periods of analysis. Results show an increase in higher usage intensity classes and a change in demographic composition of new members adopting the service over time: new members are younger and a parity between both genders is reached. Activity space investigation shows that members perform a fair share of their trips to return home and that ultra-frequency members seem to perform a substantial number of symmetric trips, meaning they are more inclined to use free-floating cars for commute-like trips. Interaction with the metro network is also investigated, with a proportion of members using free-floating carsharing to access stations. When looking at the activity space formed by members’ trip ends, users seem to constantly discover new portions of the service area. Multiple clusters of activity locations are determined for each member, with a recurrence level showing a fair amount of variability among members.

Free-Floating Carsharing Experiences in German Metropolitan Areas

Abstract In 2009, the first free-floating carsharing service in Germany was introduced: car2go in the City of Ulm. Since then, the number of these services has been largely increasing. Today, many providers are competing in the German carsharing market. This paper presents the evaluation of the car2go service in the Free and Hanseatic City of Hamburg. A systematic two-stage evaluation system was set up directly with the launch of the service in 2011, including two online surveys in 2011 and 2016. This paper first describes the survey approach including methodological issues and survey content, then analyzes the car2go users: their characteristics, their access to mobility tools, motivations, preferences, and daily travel behavior. Three user groups are distinguished: (1) Early Adopters 2011, (2) All Customers 2016, and (3) Active Users 2016. Third, findings for car2go users are compared with the general population based on data from the National Household Travel Survey (NHTS) “Mobility in Germany 2008” with a comparatively large sample size for the metropolitan area of Hamburg. The insights gained in these analyses give an up-to-date overview of users’ characteristics, their usage of the car2go services, and their general travel behavior in comparison to the general population.

The impact of free-floating carsharing on car ownership: Early-stage findings from London

Free-floating carsharing is a rapidly growing urban mobility service. It has emerged at commercial scale more recently than traditional ‘round-trip’ carsharing, and at present its growth trajectory is steeper. The evidence base regarding its impacts on sustainable transport indicators is, however, less well-developed. This issue is topical for a variety of reasons, including the importance of public policy to the success of this form of carsharing.The research objective of this study is to establish the early-stage impact of free-floating carsharing on private car ownership. We report findings from a point in time three months following the initiation of a free-floating carsharing service in London (UK). We investigate characteristics of FFCS users that are associated with having one's car ownership impacted, as well as the distinction between deterrence of increased car ownership and sale/disposal of a previously owned private car.We find that 37% (n=347; 95% confidence interval:±5%) of users indicate that free-floating carsharing has impacted their ownership of private cars. Of this 37%, a large majority (83%) indicated that the mechanism of impact was that they decided not to buy a car that they otherwise would have purchased. 11% reported that they had disposed of a car in the past three months, and 6% stated that they will sell a private car within the next three months.The average income and education level of users are both higher than for the general population. Within the population of service-users, multivariate analysis demonstrates that, net of confounding effects, heavier (more-frequent) service-users are more likely to indicate impacts on car ownership, and that being highly-educated and higher-income than other users were both (independently) associated with maintaining one's car ownership level. Additional findings are presented that relate car ownership impacts to further demographic characteristics as well as behavioural indicators.Our findings should be interpreted to pertain to the ‘early adopter’ cohort of FFCS users, and as free-floating carsharing services mature and grow further research will be needed to ascertain how user characteristics, behaviour and impacts are evolving.

What about Free-Floating Carsharing?

In recent years, carsharing has become a practical, ecological, and economical alternative to private car ownership around the world. Traditional carsharing is station-based, but new types of shared services have appeared recently. One of those is free-floating carsharing. It is more flexible, but what is its impact on user behavior? This paper aims to characterize the use of the free-floating carsharing service in central Montreal, Canada. Use of the traditional, station-based service and the new service was compared. Because some people were members of both services, it was possible to examine the specific contribution of each service to meeting travel needs. The impact of the introduction of this new transportation alternative was also examined. The results showed that compared with traditional carsharing, more women were members of the free-floating service and the trip distances and durations were much shorter. Shopping was the most important activity, and there was a concentration of trip ends near the central business district in the midday period. When asked what mode users would have used in the absence of the free-floating service, people mentioned public transit, taxis, and walking; the popularity of these alternatives varied, probably in relation to seasonal changes. Further studies are required to measure the environmental impact of this new transportation mode.

Do sharing people behave differently? An empirical evaluation of the distinctive mobility patterns of free-floating car-sharing members

Characteristics of users and usage of station-based car-sharing services have been discussed in various studies. First analyses of the free-floating car-sharing model DriveNow have shown that member composition and patterns of use are not very different from those of station-based car-sharing schemes. Nevertheless, free-floating car-sharing members were drawn from a new pool of travellers, they were not attracted by existing station-based car-sharing schemes. This paper goes beyond these analyses and looks not only at the usage of car-sharing services but at the overall travel behaviour of free-floating car-sharing members (FFCS). To the best of our knowledge, this is the first time that the specifics of this travel behaviour have been analysed based on substantial data that was collected specifically for this purpose with an innovative survey design based on a GPS tracking smartphone application. The goal of this study is to contrast the core group of members of the free-floating car-sharing model DriveNow (male, 25-45 years old) with people who do not use car-sharing. Key travel indicators are compared for FFCS and non-car-sharers (NCS) with a special emphasis on type and extend of multimodal travel behaviour within those two groups. The results show higher trip frequency for FFCS and differences in mode choice pattern. FFCS are more intermodal and multimodal in their behaviour. Shares of cycling are significantly higher, shares of private car trips are significantly lower for FFCS compared to NCS. The insights gained in this study can help cities and car-sharing operators to develop framework conditions and services that optimally integrate free-floating car-sharing services into the overall urban transport systems.