Free-floating Carsharing Research Articles

Dynamic pricing of free-floating carsharing networks with sensitivity to travellers’ attitudes towards risk

Free-floating carsharing (FFCS) systems are characterised by volatile fleet distribution as well as customers’ heterogeneous price sensitivity and spatiotemporal flexibility. There is thus an opportunity for operators to employ dynamic pricing to manage various aspects of fleet allocation: which customer is provided which vehicle, at what time and price, and the agreed pick-up and drop-off location. While there are emerging examples of dynamic pricing in FFCS, there is as yet no general framework for the interaction of consumer and operator behaviours in this context, most particularly consumer response to the inherent risks and uncertainties in the journey characteristics noted above. In this study, we propose a choice-based framework for modelling the supply/demand interaction, drawing on behavioural models of decision-making in risky choice contexts and empirical stated-choice data of user preferences in a dynamically priced FFCS market. In addition to the ‘spot market’ mechanism of dynamic pricing, the proposed framework is capable of evaluating operator strategies of allowing (at an agreed price) customers to make guaranteed advance reservations. We demonstrate that this approach allows the system operator to set an optimal pricing strategy regardless of whether user risk preferences are risk-seeking or risk-averse. We also demonstrate the applicability of the proposed framework when the operator seeks to maximise revenue (as with a private operator) vs social welfare (as with a public operator). In the case study which employs empirical user preferences, we show that users’ risk preferences have a relatively small impact on revenue, however the impacts are much larger if there is a mismatch between users’ actual risk preferences and the system operator’s assumptions regarding users’ risk preferences.

Integration of Free Floating Car Sharing Systems in Rail Stations: A Web Based Data Analysis

In the last decades, car sharing has been a tool for city planners to reduce private car traffic and pollution in big urban areas. The emergence of the ICTs (Information and Communication Technologies), together with the development of the collaborative economy, has allowed for the birth of the new Free-Floating Carsharing (FFCS): A more flexible type of carsharing, in which electric cars can be used. Little research has been devoted using real FFCS flows data, to the FFCS impacts on user behavior and even on the public transport system thus far. Furthermore, in big metropolitan areas, central rail stations should promote modal interchanges, including new modes of electric FFCS systems. The aim of this paper is to design a web-based platform to collect and analyze FFCS demand on the surrounding areas of rail stations and makes a proposal to provide these systems with electrical recharging energy obtained from the regenerative braking of high-speed trains. This case study includes Atocha and Chamartín Central Stations in Madrid (Spain). Scientific evidence shows a high demand of FFCS cars at central rail stations and a trip profile with a short time duration linked to the closest districts of rail stations.

Feature-based selection of carsharing relocation modes

One-way and free-floating carsharing systems must be rebalanced to achieve a high service level, and thus generate benefits for users and society. In practice, vehicles can be relocated with multiple different modes (e.g., by truck or by driving them), but a single mode is sufficient in many instances. Obviously, a single mode is preferred from a computational standpoint: The routing problems are less complex since less synchronization is necessary, and thus solve much faster. It remains an open question which features drive the decision on the best mode, and if operators can decide a priori whether hybridization of several modes is beneficial/necessary, and which modes one should hybridize among. We build a classifier based on linear regression which predicts the costs for all individual modes. The advantage of this approach is that cost estimates (i) can be used as a feature in other approaches, and (ii) allow operators to estimate the necessary budget upfront. However, cost estimates cannot be used directly to determine key drivers for modal choice. We, thus, use logistic regression and decision trees for determining the best mode. These approaches are better at determining relevant features that explain which mode is preferred in an instance. We find that the most important features to decide between modes are vehicle and truck costs per kilometer as well as their velocities, and the average number of vehicles that shall be relocated per day (that is, the imbalance of the system). In most instances, the decision is between driving vehicles to rebalance them and rebalance staff by biking, or loading vehicles onto a truck. Hybridization proves useful in ≈20% of all instances, and a simple rule-based classifier is able to predict correctly that hybridization is necessary in most instances.

Acceptance of free-floating car sharing: A decomposed self-efficacy-based value adoption model

ABSTRACT Free-floating car sharing (FFCS) service draws attention and has the potential to improve transportation system efficiency and achieve sustainability. This study proposes a self-efficacy-based value adoption model (SVAM) to investigate consumer adoption intention of FFCS by decomposing the perceived value and the self-efficacy. Specifically, the model examines the functional, economical, emotional, and comparative value of FFCS. Additionally, six-dimensional factors are employed to explore consumers’ self-efficacy in the context of FFCS. A population-based survey was implemented in Chinese major cities to validate the proposed decomposed SVAM. The results indicated that 66% of respondents intended to adopt FFCS, which may be explained by a combination of perceived value and self-efficacy factors. Moreover, the model explained 79% of the variance in perceived value and 77% of the variance in self-efficacy. Finally, practical implications are provided to better understand value propositions and the formation of users’ self-cognition.

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.

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.

A polynomial-time algorithm for user-based relocation in free-floating car sharing systems

Free-floating car sharing (FFCS) systems are a promising concept to reduce the traffic volume in cities. However, spatial and temporal mismatches of supply and demand require a relocation of rental cars in order to avoid low degrees of utilization. Here, especially user-based relocation strategies seem to be promising to increase utilization in a cost-efficient manner. However, a thorough optimization-based assessment of user-based relocation strategies for FFCS systems is still missing.In this paper, we introduce an integer program that optimizes the assignment of user-based relocation strategies in FFCS fleets. We develop a graph representation that allows to reformulate the problem as a k-disjoint shortest paths problem and propose an exact algorithm to solve large-size instances. We show that this algorithm can solve real-world instances within a few milliseconds as well as instances with up to 100,000 customers and 10,000 vehicles in a few minutes.Furthermore, we present a case study based on real-world data and derive managerial insights on user-based relocation strategies. Our results reveal an upper bound on the benefit of user-based relocation strategies and demonstrate that the employment of such strategies can increase the number of fulfilled rental requests by 21%, while increasing the operator’s revenue by 10%.

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.

Real-world insights on public charging demand and infrastructure use from electric vehicles

The city of Berlin has significantly expanded public charging infrastructure for electric vehicles. As a result of this investment, real-world charging data for the city of Berlin are available for the first time. In addition to other metrics, this dataset contains specific information about carsharing vehicles. This research letter offers numerous insights into public charging demand and infrastructure. The results are only now available due to a sufficient fleet size of electric vehicles.The analysis shows that the distribution of charging stations is very unequal in Berlin. The data also show that the infrastructure network is much denser in the city center. While there is an unequal distribution of infrastructure, we see that the utilization of infrastructure is relatively equal. This reflects unequal charging demand, as can be expected based on the location of the infrastructure. We also determine that the majority of public charging events come from free-floating carsharing vehicles. The analysis of infrastructure use shows that the edge of the city center has the highest rates of stations occupied by vehicles after completing charging. Carsharing users occupy infrastructure after charging significantly more than individual private and commercial users. However, if the pricing scheme allows, individual users also occupy infrastructure after completing charging.The research letter provides several policy recommendations for the build-up and operation of charging infrastructure. These focus on charging demand from individual users, infrastructure efficiency, and carsharing operators and their business models. The results are timely as decisions on public charging infrastructure must be made now to meet electric vehicle demand.

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.

Free-floating carsharing users’ willingness-to-pay/accept for logistics management mechanisms

The spatio-temporal flexibility of free-floating carsharing (FFCS) fleets leads to vehicle stock imbalances across the network. One set of strategies for managing fleet distribution involves incentivising users to participate in relocating the vehicles. The objective of this study is to establish FFCS customers’ preferences for each of four incentivisation mechanisms: 1) vehicle delivery, 2) paid relocation, and 3–4) incentivisation for alternate vehicle pick-up and drop-off locations. Survey data (n = 311; collected Sept. 2017) from FFCS users in Vancouver and Washington D.C. are employed to quantify willingness-to-pay/accept (WTP/WTA) for these mechanisms. We find that a majority of respondents report positive attitudes (“definitely” or “possibly” willing to use) toward each of the four incentivisation mechanisms, with alternate drop-off the highest (57%) and paid relocation the lowest (40%). Regression analysis finds that user experiences using FFCS are generally stronger predictors of WTP/WTA than socio-demographic features, with (intuitively) the frequency of FFCS unavailability the strongest predictor. Age is the strongest socio-demographic predictor, with the WTP for vehicle delivery increasing and the size of required incentives for alternate pick-up/drop-off locations decreasing with age. Finally, we performed k-means cluster analysis of respondents based on the times-of-week that they report experiencing difficulty finding an available FFCS vehicle, and identified four distinct segments of users. However, we found generally weak relationships between WTP/WTA and the specific time-of-week periods that unavailability is experienced.

Optimizing Relocation Cost in Free-Floating Car-Sharing Systems

This paper addresses the problem of operator-based vehicle repositioning in free-floating electric car-sharing systems (FFCSs). New algorithmic techniques are developed which derive cost-effective relocation tours by primarily minimizing relocation drivers’ walking time. Car repositioning takes place overnight and aims at complete rebalancing of the system i.e., at achieving an optimal, based on user demand, cars distribution among non-overlapping cells of the FFCS’s operating area. The battery level of the electric cars is also taken into account when deciding if and where each car will be relocated. If $k$ is the number of employees handling car relocations from oversupplied to undersupplied regions, when $k=1$ i.e., there exists a single employee carrying out a relocation tour comprising alternating phases of a car relocation followed by walking to the next to be relocated car, we define and solve the Minimum Walking Car Repositioning Problem (MWCRP) whose main objective is to minimize the walking distance in the relocation tour as the walking part of such a tour is tiresome and takes more time than the driving one. We also define and solve the $k$ -MWCRP when $k>1$ to handle the case that a team of more than one drivers is employed to take on the relocation plan. It is worth mentioning that these are the first vehicle relocation techniques lowering the relocation cost by mostly minimizing the walking cost. Performance results confirm the effectiveness of the proposed algorithms.

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.

The effect of car sharing on car sales

Free-Floating Car Sharing is a potential substitute to private car ownership. Its staggered rollout in German cities is exploited with a difference-in-difference methodology using an original administrative panel dataset on car registrations to estimate the effect of Free-Floating Car Sharing on new car sales. One car sharing vehicle reduces annual new car sales by three vehicles. This effect is driven by a reduction in sales of small, compact and medium-sized car models.

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.

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.

On Car-Sharing Usage Prediction with Open Socio-Demographic Data

Free-Floating Car-Sharing (FFCS) services are a flexible alternative to car ownership. These transportation services show highly dynamic usage both over different hours of the day, and across different city areas. In this work, we study the problem of predicting FFCS demand patterns—a problem of great importance to the adequate provisioning of the service. We tackle both the prediction of the demand (i) over time and (ii) over space. We rely on months of real FFCS rides in Vancouver, which constitute our ground truth. We enrich this data with detailed socio-demographic information obtained from large open-data repositories to predict usage patterns. Our aim is to offer a thorough comparison of several machine-learning algorithms in terms of accuracy and ease of training, and to assess the effectiveness of current state-of-the-art approaches to address the prediction problem. Our results show that it is possible to predict the future usage with relative errors down to 10%, while the spatial prediction can be estimated with relative errors of about 40%. Our study also uncovers the socio-demographic features that most strongly correlate with FFCS usage, providing interesting insights for providers interested in offering services in new regions.

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.

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.