Observed patterns of free-floating car-sharing use

Fabra, Natalia; Pintassilgo, Catarina; Souza, Mateus Article Observed patterns of free-floating car-sharing use SERIEs - Journal of the Spanish Economic Association Provided in Cooperation with: Spanish Economic Association Suggested Citation: Fabra, Natalia; Pintassilgo, Catarina; Souza, Mateus (2024) : Observed patterns of free-floating car-sharing use, SERIEs - Journal of the Spanish Economic Association, ISSN 1869-4195, Springer, Heidelberg, Vol. 15, Iss. 3, pp. 259-297, https://doi.org/10.1007/s13209-024-00298-2 This Version is available at: https://hdl.handle.net/10419/326957 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ SERIEs (2024) 15:259–297 https://doi.org/10.1007/s13209-024-00298-2 ORIGINAL ARTICLE Observed patterns of free-floating car-sharing use Natalia Fabra1·Catarina Pintassilgo2·Mateus Souza3 Received: 12 May 2023 / Accepted: 15 April 2024 / Published online: 9 June 2024 © The Author(s) 2024 Abstract Free-floating car-sharing (FFCS) services allow users to rent electric vehicles by the minute without restrictions on pick-up or drop-off locations within the service area of the rental company. Beyond enlarging the choice set of mobility options, FFCS may reduce congestion and emissions in cities, depending on the service’s usage and substitution patterns. In this paper, we shed light on this by analyzing the universe of FFCS trips conducted through a leading company in Madrid during 2019. We correlate FFCS usage patterns with data on traffic conditions, demographics, and public transit availability across the city. We find complementarities between FFCS and public transport in middle-income areas with scarce public transport options. Moreover, we find that the use of FFCS peaks earlier than overall traffic and is broadly used during the summer months. This suggests that FFCS may have smoothed road traffic in Madrid, contributing to a reduction in overall congestion. Keywords Car-sharing ·Shared mobility ·Road congestion ·Electric vehicles JEL Classification R41 ·Q52 1 Introduction Road transport is associated with several types of negative externalities, including traffic accidents (Edlin and Karaca-Mandic 2006), congestion (ECA 2019), and pollution, such as fine particulate matter (PM2.5) (ECA 2019; Böhm et al. 2022; Li and Managi 2021). These pollutants, in turn, have been linked to increased mortality among adults The replication material for the study is available at https://doi.org/10.5281/zenodo.7707902. BMateus Souza [email protected] 1Department of Economics, Universidad Carlos III de Madrid, Madrid, Spain 2Department of Economics, University of Warwick, Warwick, UK 3Department of Economics, University of Mannheim, Mannheim, Germany 123 260 SERIEs (2024) 15:259–297 and infants (Currie and Neidell 2005; Deryugina et al. 2019; Gehrsitz 2017; Lelieveld et al. 2015) through a variety of mechanisms, ranging from respiratory (Wu et al. 2018; Wei and Tang 2018) to cardiovascular diseases (Al-Kindi et al. 2020; Sanidas et al. 2017). The transport sector also accounts for approximately 23% of energy-related CO2 emissions globally (IEA 2022). Additionally, car ownership in cities may lead to inefficient land use. On average, private cars are parked 96% of the time, or 23 h per day, thus having relatively low usage rates (Nagler 2021). This inefficiency is exacerbated by the increasing single-occupancy vehicle commuting rates (Machado et al. 2018). Worldwide, a breadth of policies are being implemented to mitigate these externalities, including congestion pricing (Anas and Lindsey 2011; Börjesson et al. 2012), subsidies for electric vehicle purchases (Rapson and Muehlegger 2023), or lowemission zones in cities (Galdon-Sanchez et al. 2022; Börjesson et al. 2021), among others. Reliance on these policies is expected to grow as countries seek to comply with their commitments to achieve net-zero emissions by 2050 (European Commission 2020; U.S. DoS and EOP 2021). To improve their effectiveness, we still need a deeper understanding of the benefits and costs of the various policy options. For equity purposes, we must also understand who benefits and who is harmed by those policies. Significant headway has been made in some fronts (Parry 2002), yet little is known about the potential impacts of some rapidly proliferating technologies. This study contributes in this direction by analyzing the characteristics of users and usage patterns of one such technology, free-floating car-sharing (FFCS). In part thanks to advances in communications and tracking technologies, as well as the widespread adoption of smartphones, companies are now able to rent cars by the minute, at rates ranging from approximately 0.19 e/min to 0.31 e/min.1In earlier stages of the deployment of these services, users were required to drop off the vehicles at specific locations or at charging stations. However, in recent years, there has been a rapid growth of demand for FFCS services allowing users to pick up the vehicle and drop it off at any place within the service area of the rental company (AmpudiaRenuncio et al. 2020a). Importantly, the companies providing these services often do so with electric vehicles. Therefore, FFCS can potentially affect congestion and environmental externalities in opposing directions, depending on which mode of transport it is substituting. On the one hand, FFCS may alleviate urban congestion and pollution through the extensive and intensive margins, i.e., if they reduce car ownership (car shedding) or the number of trips with private polluting cars (Bucsky and Juhász 2022). This possibility is supported by the fact that shared vehicles are mostly electric and have much higher utilization rates than private vehicles (Habibi et al. 2017). On the other hand, carsharing may increase congestion if used as a substitute for public transport, walking, or cycling (Machado et al. 2018). Whether one effect or the other dominates is an empirical question related to the usage patterns of FFCS. As a starting point for shedding light on the welfare effects of FFCS, this study provides a comprehensive description of car-sharing usage patterns, seasonality of 1Rates published on the websites of the main FFCS companies operating in Madrid (Emov, ShareNow, WiBLE, Zity), as of December 2023. 123 SERIEs (2024) 15:259–297 261 use, users’ incomes, and primary usage purposes. In particular, we investigate the correlations between the variables of interest without claiming causation. While most of the early literature on car-sharing has focused on German cities,2or relied mostly on survey data (Amirnazmiafshar and Diana 2022; Müller et al. 2017; Schmöller et al. 2015; Kopp et al. 2015), in this paper, we provide new evidence based on thousands of observed trips in the city of Madrid. According to surveys across several European and North-American cities (Sprei et al. 2019; Habibi et al. 2017), Madrid had the highest FFCS utilization rate (between 17 and 21.6% of the time when cars can be potentially driven per day). Additionally, together with Amsterdam, Madrid is one of the only cities in Europe where the FFCS fleet is fully electric (Ampudia-Renuncio et al. 2020a). One of the strengths of this study is that it combines a rich set of data sources. Importantly, we have access to a unique proprietary database of the universe of carsharing trips (more than 1,500,000) carried out in 2019 by one of the leading FFCS companies operating in Madrid.3To correlate car-sharing usage patterns with sociodemographic characteristics, we use neighborhood-level data on population, income, car ownership, public transport network, and parking availability data. We also analyze daily road traffic data, comparing it against the seasonality of car-sharing trips. Ourmaindatasetcontainsprecisegeo-referenceddataonthecar-sharingtriptiming, origin, and destination but lacks information on the customers’ demographics. In particular,whilewecantrackusersthroughuniqueidentifiers,wedonotobservewhere they live. Hence, we developed a strategy to impute users’ neighborhood of residence based on repeated trips from and to the same destination during commuting times. Our approach constitutes an improvement relative to the previous literature, which was restricted to analyzing the characteristics of the place of origin/destination of trips (Schmöller et al. 2015; Ampudia-Renuncio et al. 2020a) and therefore abstracted away from users’ place of residence. Besides imputing members’ residences, we also exploit the hourly frequency of repeated origins and destinations to infer the trips’ purposes, as either commute or leisure. These strategies allow us to match the trip data and the socio-demographics to study the determinants of car-sharing usage and the differences across income groups. We obtain meaningful insights into the usage patterns and characteristics of the FFCS users. First, we find that car-sharing users mostly live in middleand highincome neighborhoods. This finding is partly driven by the service area restriction from the FFCS company (Fig. 1), revealing that FFCS companies expect to have greater demand in higher-income areas. Hence, lower-income neighborhoods do not have access to FFCS, which is a strong force toward a positive correlation between neighborhood income and car-sharing use. Usage intensity in the covered neighborhoodsprovidesa different picture, as the most loyal users tend to liveinmiddle-income 2Some earlier studies within Madrid exist (e.g., Ampudia-Renuncio et al. 2020b), however, with a shorter period of analysis and smaller coverage area. The data used in those studies were obtained from the companies’ websites. 3During the period that we analyze, the company charged fixed prices per minute, such that total costs depended exclusively on the trip duration. There was no price discrimination across users and no rebates for frequent users. 123 262 SERIEs (2024) 15:259–297 Fig. 1 Coverage area of the car-sharing service, in 2019. Notes The figure presents the study area of this paper and the coverage area of the car-sharing service, i.e., the area in which customers can start or end a trip. Users are allowed to drive outside the coverage area and leave the car in standby mode if they need to make a stop outside the coverage area. All territorial units represented are neighborhoods inside Madrid Municipality, except for the darker shaded units: Alcobendas, Coslada, Pozuelo de Alarcón, and San Sebastian de los Reyes municipalities Car sharing coverage area Neighborhoods inside Madrid Municipality Outside Madrid Municipality neighborhoods, i.e., conditionally on having access to FFCS, there is a negative correlation between neighborhood income and the frequency of car-sharing use. Based on the timing of use, we also provide evidence on the purposes of car-sharing trips. We are able to classify almost 536 thousand commuting trips and 386 thousand leisure trips. This allows us to explore heterogeneity along these dimensions. For example, we find that income ispositively correlated with commuting with car-sharing but negatively correlated with leisure trips. Since most loyal customers live in neighborhoods with high car ownership rates and fewer public transport options, our analysis suggests that these customers use the service to complement public transport. If car-sharing members are car owners who prefer to leave their car at home, or even eventually sell it, FFCS would imply an overall reduction of vehicles in the city and, consequently, reduced use of parking space, decreased road traffic, and improved air quality. Additionally, we find that the seasonality of car-sharing trips closely matches the trends of other motorized vehicles, although car-sharing usage peaks slightly earlier. Moreover, while road traffic decreases during the summer months, the number of car-sharing trips increases in the months of July and September. These patterns suggest that FFCS contributes to smoothing overall traffic and, thus, reducing congestion in the city. Importantly, during summer months, usage by the highest-income customers decreases substantially. We see a higher usage of the car-sharing service during workdays, particularly on Fridays. During weekends, trips are less frequent but longer. Overall, car-sharing trips are generally short, both regarding the duration and distance. Finally, we implement gravity-type regression specifications to uncover potential frictions or facilitators for adopting car-sharing across the city. This analysis is performed at the level of Madrid’s “transport zones” (“zonas de transporte,” in Spanish), which are smaller than neighborhoods but larger than census tracts. We observe nearly 500transportzoneswithinthecar-sharingcompany’s area, corresponding to about 250 123 SERIEs (2024) 15:259–297 263 thousand origin/destination pairs (or dyads). Our results suggest that the likelihood of taking a car-sharing trip decreases with the distance between origin and destination. This could be due to the high costs associated with long car-sharing trips. Conversely, the likelihood of car-sharing is higher for origin/destination dyads that are poorly connected by public transit (i.e., dyads with long expected travel times by public transit). 2 Data sources Table 1provides a summary of the main variables used in this study, including sample sizes and data sources. We describe each data source in detail below. 2.1 Free-floating car-sharing trips We use the universe of car-sharing trips made through one of the leading companies operating in Madrid during 2019. This database, which the company provided directly to the authors, contains unique vehicle and customer IDs, the starting and ending time and location of each trip, and the duration and distance traveled. During the analyzed sample period, the company had a fleet of 656 operating electric vehicles, which virtually remained constant throughout the year 2019 (Fig. 11). We excluded trips by company employees and focused only on trips made by customers. We exclude trips that last less than 2 minutes or more than 180 minutes. We further excluded observations for which the same member used the service more than 6 times on the same day.4 2.2 Demographics In terms of demographics, we primarily use data on population and income, obtained from the Madrid City Council (AM 2018c) and the National Statistics Institute (INE 2018a;b). This entails combining data at different spatial aggregation levels. For the cityofMadrid,weobtainneighborhood-leveldemographicsforits131neighborhoods. We also collect municipality-level data for the other municipalities that are part of the Community of Madrid (metropolitan area). We also collect data on other demographic variables, such as gender and age. However, we find no significant heterogeneity along those dimensions, such that they are omitted from the remainder of this paper. Note that we do not directly observe demographics of the car-sharing users, which limits the scope of our heterogeneity analysis. Regardless, in Sect. 3we present a proposal for imputing demographics.5 4These are outlier cases that may represent bugs in the recording of trip durations. 5Previous studies, mostly relying on survey data, found that car-sharing users are predominately male, high-income, and young (Amirnazmiafshar and Diana 2022; Vine and Polak 2020). Similarly, Schmöller et al. (2015) argue that car-sharing services can be especially appealing to young adults, since owning a private car is becoming less valuable now, compared to the past. 123 264 SERIEs (2024) 15:259–297 Table 1 Descriptive statistics Variable Obs Mean Std. Dev Min Max Source Year Individual-level data Full sample of FFCS trips Number of trips 1,474,287 Trip data 2019 Number of customers 143,315 Trip data 2019 Customer loyalty No. of days used in 2019 143,315 8.224 14.239 1 344 Trip data 2019 No. of times used in 2019 143,315 10.287 20.306 1 726 Trip data 2019 Percentage of days used in 20191(%) 143,315 4.027 7.212 .274 100 Trip data 2019 Weighted no. of days used in 20192143,315 14.699 26.323 1 365 Trip data 2019 Sub-sample of trips with identified residence Number of identified neighborhoods 95 Trip data 2019 Number of trips 511,070 Trip data 2019 Number of customers 12,143 Trip data 2019 Annual net income (e)312,143 20,994 5,247 8,758 31,239 AM, INE 2018 Customer loyalty No. of days used in 2019 12,143 31.925 28.209 1 344 Trip data 2019 No. of times used in 2019 12,143 42.088 43.256 1 724 Trip data 2019 Percentage of days used in 20191(%) 12,143 10.74 9.45 0.28 94.25 Trip data 2019 Weighted no. of days used in 2019212,143 39.21 34.48 1.01 344 Trip data 2019 Survey sample of EDM 4 Number of cars per household 28,922 0.96 0.80 0 5 EDM 2018 Inadequate public transport (%)536,030 21.34 37.85 0 100 EDM 2018 % individuals with driving license 119,423 0.64 0.48 0 1 EDM 2018 123 SERIEs (2024) 15:259–297 265 Table 1 (continued) Variable Obs Mean Std. Dev Min Max Source Year Neighborhood/municipality averages Neighborhood population size 135 26,589 17,971 1,176 116,037 AM, INE 2018 Average annual net income per capita (e) 135 17,425 6,318 6,712 32,242 AM, INE 2018 Average age 135 43.60 3.58 32 48.91 AM, INE 2018 Percentage of females (%) 135 53.31 1.86 48.30 58.22 AM, INE 2018 Average number of cars per household 135 0.95 0.31 0.42 1.91 EDM 2018 % individuals with driving license 135 59.58 8.83 32.13 78.71 EDM 2018 Inadequate public transport (%)5135 18.74 8.45 3.44 52.17 EDM 2018 No. of metro stations per km2135 1.51 2.26 0 15.73 CRTM 2018 No. of train stations per km2135 0.15 0.40 0 2.58 CRTM 2018 No. of bus stations per km2135 24.6 12.05 0.12 53.16 CRTM 2018 No. of regulated parking slots per car648 0.36 0.35 0.12 2.55 AM 2018 Neighborhoods in the study area Car sharing member density (%) 95 0.56 0.58 0.01 3.66 Trip data 2019 Neighborhood population size 95 27,471 18,994 1,176 116,037 AM, INE 2018 Average annual net income per capita (e) 95 19,220 5,783 8,758 31,239 AM, INE 2018 Average age 95 44.04 3.40 32.5 48.91 AM, INE 2018 Percentage of females (%) 95 53.66 1.92 48.30 58.22 AM, INE 2018 Average number of cars per household 95 0.96 0.29 0.42 1.71 EDM 2018 % individuals with driving license 95 61.72 8.04 32.13 78.71 EDM 2018 Inadequate public transport (%)595 17.65 8.33 5.69 52.17 EDM 2018 123 266 SERIEs (2024) 15:259–297 Table 1 (continued) Variable Obs Mean Std. Dev Min Max Source Year No. of metro stations per km295 1.74 2.08 0 13.07 CRTM 2018 No. of train stations per km295 0.11 0.37 0 2.58 CRTM 2018 No. of bus stations per km295 26.2 11.2 1.7 53.16 CRTM 2018 No. of regulated parking slots per car648 0.36 0.35 0.12 2.55 AM 2018 1Computed as the number of days the customer used the car-sharing service divided by the total number of days left in 2019 since the first day they used the service. 2Computed as the percentage of days a customer used the car-sharing service, multiplied by total number of days in a year. 3Assuming all residents in a certain neighborhood have an annual net income equal to the annual income per capita of that same neighborhood. 4Survey data from EDM 2018, restricted to our four municipalities of analysis (Madrid, Alcobendas, Coslada, Pozuelo de Alarcón, and San Sebastian de los Reyes). The observation units of the variable “% individuals with driving license” correspond to an individual, for “number of cars per household” to a household, and for “inadequate public transport” to a trip made by car by one of the survey respondents. 5Using survey data from EDM 2018, we created a measure of adequacy of the public transport network. It measures the percentage of trips, out of total trips made by car, in which the respondents of EDM affirm using this travel mode because they have no public transport alternative. 6Number of slots from the city’s regulated parking service (Servicio de Estacionamiento Regulado, SER), normalized by the number of registered vehicles. In 2018, only 48 neighborhoods in the Municipality of Madrid were covered by the SER 2.3 Mobility-related data Another key data source is Madrid’s Mobility Survey (Encuesta Domiciliaria de Movilidad,CRTM2018b). This is a recall survey of over 58 thousand households, aiming to assess mobility patterns in the Community of Madrid during working days.6 The mobility survey includes information on the number of cars per household, which we use to proxy car ownership in each neighborhood. Moreover, using trip data from themobility survey, webuilda measureofthe adequacyof thepublic transport network to assess the degree of complementary and substitutability between the car-sharing service and public transport (see Sect. 3for further details). We also downloaded data from Madrid’s Regional Transport Consortium (Consorcio Regional de Transportes de Madrid,CRTM2018a) containing the geographical coordinate of each metro, train, and bus station in the Madrid Autonomous Community, to measure neighborhood-level density of public transport stations in Madrid and neighboring municipalities. The Consortium also provides detailed georeferenced 6Respondents were asked to describe all of their trips from the day prior to the interview. Data are representative for trips taken from Monday to Thursday. 123 SERIEs (2024) 15:259–297 273 that start or end in Atocha are multi-modal. For example, users might take a train to Atocha and, from there, take the car-sharing service to their final destination. Thus, as explained in Sect. 3, we exclude Atocha from our analyses. Likewise, we exclude the Castilla and Aeropuerto neighborhoods, which are almost entirely occupied by the Chamart´ın train station and Madrid-Barajas Airport. According to our methodology, the Castilla and Aeropuerto neighborhoods would be the 7th and 5th areas with the highest member density (1.52 and 1.66%, respectively). Figure 2panel (b) shows that more loyal car-sharing members live in the periphery of our study area, particularly in the four municipalities outside Madrid and the southeastern neighborhoods. A comparison of panels (a) and (b) reveals that neighborhoods with a higher percentage of car-sharing customers have a lower loyalty to the service, which might suggest that those customers use the service for distinct purposes. The sections below explore this further. 4.2 Income distribution Prior literature suggests that FFCS in Madrid is about three times more expensive than public transport when considering single-trip fares (Ampudia-Renuncio et al. 2020b). The availability of monthly passes for public transportation increases the cost difference. However, car-sharing is cheaper than ride-hailing and, depending on the usage intensity, car-sharing can be a more affordable option than owning a car. The implication of these cost differences is that car-sharing might not be accessible or attractive to some income groups while appealing to others. In this section, we provide insights into how income relates to car-sharing usage in the city of Madrid. Having identified the users’ residence location through the carsharing usage patterns, we then impute their income based on neighborhood-level data (see Table 1). This shows that the average annual net income of car-sharing users in our sample is close to e20 thousand, which is substantially higher than the average income per capita for the entire municipality of Madrid (e16,700) (INE 2018b).13This is in line with the fact that the car-sharing service area (Fig. 1) excludes some of the lowest-income neighborhoods in the city. Our findings are consistent with prior literature showing that car-sharing users tend to have an above-average income (Amirnazmiafshar and Diana 2022; Caulfield and Kehoe 2021). Although our data suggest the existence of a positive correlation between carsharing member density and income, we would expect to find a negative correlation between member loyalty and income, given that, as discussed in Sect. 4.1, neighborhoods with a higher percentage of car-sharing customers seem to have a lower loyalty to the service. We formally test these relationships with regressions as follows: Yi=α+4 g=2βg1i∈income quartile g+εi(2) where Yiis the outcome of interest (customer density or loyalty) for the unit of analysis i; the unit of analysis is the neighborhood for the density variable, and the customer 13 The average annual income per capita of the four municipalities of the study area (Madrid, Alcobendas, Coslada, Pozuelo de Alarcon, and San Sebastian de los Reyes) was e17,425. 123 274 SERIEs (2024) 15:259–297 for the loyalty variable; αis a regression constant; the indicators 1[i∈income quartile g] are equal to one when ibelongs to a given income quartile, zero otherwise; note that the 1st quartile (income from e8000–e14,500) is the omitted comparison group; and εiis an idiosyncratic error term. The coefficients βgthus capture how density or loyalty changes depending on the income quartiles of neighborhoods or customers. Regression estimates from Eq. (2) are presented in Table 4. Column (1) shows that the highest-quartile neighborhoods have a member density of about 0.72 p.p. higher than the lowest-quartile neighborhoods. Conversely, column (2) suggests that the higher-income customers are less loyal, i.e., they use the service almost 6.6 days less than the lower-income customers. In Figs. 3and 4, panel (a), these opposite relationships are evident. While neighborhoods with higher income tend to have a higher proportion of car-sharing users, those with the lowest income in our study area concentrate the most loyal members. Regardless, in results presented in appendix (see Table 11), we show that both loyalty and income are positively correlated with the probability of taking a car-sharing trip. Table 4 Regression results for purpose of trip: trip level (sub-sample with identified residence) Member loyalty Member density Days used Times used Days used (weighted) (1) (2) (3) (4) Annual net income (e) 14,500–19,500e0.104 (0.071) −2.443** (0.960) −4.129*** (1.498) −4.439*** (1.190) 19,500–24,000e0.379*** (0.106) −4.920*** (0.878) −8.565*** (1.366) −8.597*** (1.072) +24,000e0.722*** (0.118) −6.601*** (0.848) −11.286*** (1.305) −10.385*** (1.045) Observations 92 11,818 11,818 11,818 R20.358 0.007 0.008 0.011 Standard errors in parentheses: ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01 The above linear regressions are restricted to the sub-sample of car-sharing users with identified residence, excluding those living in Atocha, Aeropuerto, and Castilla neighborhoods. The regressions provide correlations between car sharing member density and loyalty with the neighborhood income quartiles, using as base category the lowest quartile: e8,000–e14,500. Column (1) uses neighborhood observations, while in columns (2–4) each observation represents a customer The outcome variable in column (1) measures the percentage of car-sharing users in a neighborhood, i.e., total number of users divided by total resident population Columns (2–4) provide different measures of member loyalty. The dependent variable in column (2) Measures the total number of days a customer used the car-sharing service in 2019, while column (3) Measures the number of times she/he used the service. In column (4), the outcome variable measures the weighted number of days used, given by the number of days the customer used the car-sharing service, weighted by the total number of days left in 2019 since the first day they used the service, multiplied by total number of days in a year 123 SERIEs (2024) 15:259–297 275 2 1.5 1 .5 0 10,000 15,000 20,000 25,000 30,000 Average annual income per capita (€) (a) Average annual income 2 1.5 1 .5 0 .5 1 1.5 2 Average number of cars per household (b) Car ownership Proportion of car-sharing users (%) Proportion of car-sharing users (%) Fig. 3 Correlation plots between member density and neighborhood-level attributes. Notes The scatter plots correlate car-sharing member density with two neighborhood attributes. Member density is computed as the number of car-sharing users living in each neighborhood, weighted by the total population. Panel (a) correlates member density with the average annual net income per capita (INE 2018b;AM2018c), while panel (b) provides the correlation with the average number of cars per household (CRTM 2018b) in each neighborhood of the study area. We exclude the neighborhoods of Atocha and Castilla (where the main train stations are) and Aeropuerto. 4.3 Effect on mobility patterns One crucial factor to consider when assessing the implications of FFCS in cities is the impact of the car-sharing service on mobility patterns. For example, fully electric FFCS cars could accelerate the decarbonization of cities through car shedding. In this scenario, the majority of car-sharing members would be car owners considering 123 276 SERIEs (2024) 15:259–297 Average number of days a customer used the service 70 70 60 60 50 50 40 40 30 30 20 10,000 15,000 20,000 25,000 30,000 Average annual income per capita (€) (a) Average annual income 20 .5 1 1.5 2 Average number of cars per household (b) Car ownership Average number of days a customer used the service Fig. 4 Correlation plots between member loyalty and neighborhood-level attributes. Notes The scatter plots correlate car-sharing member loyalty with two neighborhood attributes. Member loyalty is measured as the weighted average number of days the members living in the neighborhood used the service in 2019. This loyalty measure is computed by taking the total number of days each customer used the service, weighted by the total number of days left in 2019 since they first used the service, and multiplied by the total number of days in a year. Panel (a) correlates member loyalty with the average annual net income per capita (INE, 2018b;AM,2018c), while panel (b) provides the correlation with the average number of cars per household (CRTM, 2018b) in each neighborhood of the study area. We exclude the neighborhoods of Atocha and Castilla (where the main train stations are) and Aeropuerto. We also exclude the Pavones neighborhood, which exhibits outlier loyalty above 100 123 SERIEs (2024) 15:259–297 277 the possibility of selling their vehicle and using the car-sharing service as an alternative (Jochem et al. 2020; Becker et al. 2018).14 This may be particularly likely for households owning more than one car. Another potential mechanism is that drivers may switch their polluting vehicle for an electric one following a positive car-sharing experience. In that case, even when shedding is limited, car-sharing may contribute to reducing pollution by accelerating the electrification of the fleet. Moreover, FFCS mightpreventfuturevehiclepurchasesifdriversconsiderthattheFFCSservicealready fulfills their needs. Besides the effect on car ownership, however, it is essential to analyze the relationship between the use of car-sharing services and public transport. On the one hand, if users consider them to be substitutes, FFCS could reduce the use of collective public transport, leading to an overall increase in the number of cars in the city. For instance, users may see FFCS and public transportation as substitutes for short trips between nearby areas, for which the travel time by public transport is generally higher than by car (Ampudia-Renuncio et al. 2020b). However, when considering the time required to reach the vehicle and to find a parking slot, the travel time difference between FFCS and public transport may not be significant (Sprei et al. 2019). Indeed, this outcome is unlikely, according to prior literature (Habibi et al. 2017; Becker et al. 2018; Tyndall 2019). On the other hand, FFCS could be a complement to public transport, particularly in areas where the public transport network is scarce (Vine and Polak 2020). In this case, FFCS would prevent the use of private vehicles, contributing to reduced land occupancy and lowering emissions. However, even where car-sharing complements incomplete public transport networks, FFCS services must be implemented where alternative transport modes exist, given the uncertainty regarding the availability of cars for the return trip (Ampudia-Renuncio et al. 2020a). This is another important source of complementarity. Building on data from several sources, we provide insights into which of the above mechanisms is more likely to dominate in Madrid. We relate FFCS usage with car ownership and the public transport network in the city. To measure the availability of public transport services, we first focus on the density of metro, train, and bus stations/stops, i.e., the number of stations/stops per Km2in each neighborhood. Since the presence of several stations might not imply a proper connection between different destinations, we further created a measure of public transport adequacy based on a question from Madrid’s Mobility Survey (see Sect. 3). We assess the correlation between car-sharing usage and mobility patterns through the following regression: Yi=γ+Xθi+εi,(3) 14 According to a survey conducted among car-sharing users in 11 European cities (Jochem et al. 2020), the share of survey participants selling a car after trying the service ranged from 3.6% to 16.0%, with the lowest value being found in Madrid. However, compared to station-based car-sharing, FFCS members are less likely to decrease vehicle ownership, as these users see the FFCS service as a complement rather than a replacement for their private car (Amirnazmiafshar and Diana 2022). 123 278 SERIEs (2024) 15:259–297 where Yiis the outcome of interest (customer density) for the neighborhood i;γis a regression constant; Xis a vector of explanatory variables; and εiis an idiosyncratic error term. The coefficients θicapture how car-sharing member density changes according to neighborhood characteristics and amenities, notably, the annual income per capita, rate of car ownership, and adequacy of the public transport network. Table 5provides the results for the above regression. Firstly, we note that neighborhoods with higher rates of car ownership tend to have a higher percentage of car-sharing members (see also panel (b) of Fig. 3). In particular, columns (1) and (5) suggest that an additional car per household is associated with a 0.56 p.p. increased member density. Since these regressions already control for the income level of the neighborhood, these results suggest that car-sharing users are car owners, substituting their private vehicle for a shared one, when using the service. In column (2) we test the influence of the public transport network on car-sharing usage. The estimates indicate that neighborhoods with fewer metro and bus stops per Km2have a higher density of car-sharing members. In Table 9in appendix, we estimate Eq. 3for a different outcome variable: member loyalty. In this specification, θicaptures changes in customer loyalty, for customer i, according to the same neighborhood characteristics and amenities. The results support ourpreviousconclusions,neighborhoods with fewer public transportstations/stopsper Km2and with more complaints of inadequate public transport network have the most loyal customers. However, these effects are muted once we control for car ownership, which is negatively correlated with the availability of public transport. These results suggest that car-sharing might be used in Madrid as a substitute for private vehicles, and to complement the existing public transport network. In fact, the negative correlation between car ownership and the availability of public transport could indicate that residents in areas with poorer public transport networks own a car to counter insufficient public transport options. Our findings could imply that FFCS services decrease commuting/travel times, improve connections, and promote car shedding. In Table 10 in appendix, we estimate the original Eq. 3, extending the previous analysis to all neighborhoods in Madrid municipality. In neighborhoods where we did not identify car-sharing members, we impute a member density of zero. Our results are robust to this sample modification. For the complete decarbonization of the city, preference should be given to more sustainable travel modes, such as walking, cycling, and public transport. The first two modes are not feasible for long distances, and expanding the public transport network is generally costly and requires a long implementation period. In this sense, electric free-floating car-sharing services might be an optimal solution to complement the existing public transport network. It is important to stress that these shared vehicles are considerably more efficient than private cars. Firstly, the FFCS fleet in Madrid is fully electric. Secondly, FFCS provides more efficient use of the scarce space in cities, particularly parking. On average, a private car is parked 23 hours a day, translating into a usage rate of 4% (Nagler 2021). By being shared among several individuals, FFCS vehicles have a higher utilization rate. In our data, vehicles’ average usage rate was 23%, measured by the total number of hours each car was used, divided by the maximum number of hours it could have been used. Habibi et al. (2017) and Sprei 123 SERIEs (2024) 15:259–297 279 Table 5 Regression results for mobility patterns: neighborhood level (sub-sample with identified residence) Member density No. trips at destination (1) (2) (3) (4) (5) (6) Log annual net income (e) 0.739*** 0.933*** 0.877*** 0.906*** 0.737*** 32.742*** (0.198) (0.123) (0.213) (0.126) (0.200) (6.908) Avg. no. of cars per household 0.561*** 0.298 0.568** (0.212) (0.244) (0.218) % of residents with driving license −0.003 −0.004 −0.003 (0.009) (0.010) (0.009) No. metro stations per km2−0.033** −0.021 (0.013) (0.015) No. train stations per km20.030 0.048 (0.082) (0.092) No. bus stations per km2−0.009** −0.006 (0.004) (0.004) Inadequate public transport network (%) 0.008 −0.001 (0.006) (0.006) No. of parking slots per car 0.573*** (0.051) Observations 92 92 92 92 92 47 R2 0.459 0.467 0.480 0.376 0.459 0.715 Standard errors in parentheses: ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01 Notes The linear regressions use neighborhood observations. Columns (1) to (5) are restricted to the subsample of car-sharing users with identified residence, excluding those living in Atocha, Aeropuerto, and Castilla neighborhoods. The outcome variable in columns (1) to (5) measures the neighborhood member density, i.e., the percentage of car-sharing users in each neighborhood, out of the total resident population. These regressions provide correlations between car-sharing membership and the characteristics and amenities of the neighborhood. The dependent variable in column (6) measures the total number of trips ending in each neighborhood, weighted by the resident population, while the independent variable is given by the number of parking slots per car. These parking slots are integrated within the Regulated Parking Service (SER), which, in 2018, only covered 48 neighborhoods in the Municipality of Madrid. Regression (6) provides the correlation between the number of trips and parking availability at the destination 123 280 SERIEs (2024) 15:259–297 et al. (2019), which studied FFCS in several European and North-American cities, concluded that Madrid had the highest usage rate of all cities analyzed. 4.4 Seasonality and purpose of car-sharing usage Figure 5shows the temporal distribution of car-sharing trips. For graphs on the lefthand side, we use the full sample of car-sharing trips in 2019. Right-hand side graphs are for a restricted sample of trips for which we identified users’ residences, such that we can correlate seasonality patterns with demographic characteristics (income). For graphs on the left, we contrast the car-sharing trip seasonality with road traffic on the main highway (M-30) and in urban areas inside the municipality. We observe that the seasonality of car-sharing trips closely follows the pattern of other motorized vehicles in Madrid. We see a higher usage during peak hours (morning and afternoon) on weekdays (panel a). In Madrid, there is a third peak hour at lunchtime, between 2 pm and 5 pm, when many stores close (Ampudia-Renuncio et al. 2020b; Habibi et al. 2017). Nonetheless, we notice that the morning peak for car-sharing users happens earlier, between 6 am and 7 am, with the number of car-sharing trips dropping significantly during the usual morning peak, between 8 am and 9 am. This might suggest that car-sharing users avoid high congestion hours to avoid paying higher bills, since the car-sharing service is charged by the minute. Additionally, car-sharing trips exhibit a higher peak at lunchtime than road traffic. According to the graphs on the right, during both these peaks, most car-sharing members are above the medium income of all car-sharing users. There are only two peaks at lunchtime and in the afternoon on weekends (panel b). Similarly, the peaks for car-sharing users happen slightly earlier than usual road traffic. Looking at the patterns by day of the week (panel c), we see higher usage of the car-sharing service on weekdays, particularly on Fridays. We also note that trips during the weekend are dominated by below-average income customers, while during weekdays, especially at peak hours, higher-income users are more prevalent. These figures suggest that higher-income customers use the service for commuting rather than leisure. This is in line with findings for Germany, where most free-floating carsharing trips happen on Saturday, and the peak usage hours happen considerably later thanthose ofprivatecars, particularlyduring the afternoon(Schmöller etal. 2015).The seasonality of car-sharing trips seems to be city-specific, especially when comparing workdays versus weekends (Sprei et al. 2019). Regardingthedistributionoftripsthroughouttheyear(panel d),Julyand September show more car-sharing usage. However, road traffic, which is relatively consistent throughout the year, sees a drop in these months, intensifying in August. Although we see the same drop in August for car-sharing trips, the reduction is much less pronounced,suggestingthattourists alsousethisservice. AstudyinBerlinandMunich foundadropincar-sharingutilizationbetweenJuneandSeptember,arguingthatduring these months, users opt for other travel modes, such as walking and cycling (Schmöller et al. 2015). The right-hand side graph of panel (d) is consistent with higher-income customers leaving the city during the summer for holidays. 123 SERIEs (2024) 15:259–297 281 Annual net income (€) Annual net income (€) Annual net income (€) 822000 621000 420000 219000 0 01234567891011121314151617181920212223 Hour of the Day Car sharing trips M-30 Urban areas 18000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of the Day Average 95% CI (a) Patterns by Hour of Day – weekdays 822000 621000 420000 219000 0 01234567891011121314151617181920212223 Hour of the Day 18000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of the Day (b) Patterns by Hour of Day – weekends 20 22000 15 21000 10 20000 519000 0 Sunday Monday Tuesday Wednesday Thursday Friday Saturday Day of the Week 18000 Sunday Monday Tuesday Wednesday Thursday Friday Saturday Day of the Week (c) Patterns by Day of Week 10 22000 8 21000 6 20000 4 19000 2 0 1 2 3 4 5 6 7 8 9 10 11 12 Month of the Year 18000 123456789101112 Month of the Year (d) Patterns by Month of Year Percent of Trips Percent of Trips Percent of Trips Percent of Trips Annual net income (€) Fig. 5 Seasonality of car-sharing trips. Notes Left-hand side figures use the full sample, while right-hand side figures are restricted to the sub-sample of members with identified residence. The blue and red lines in the left-hand side figures represent road traffic seasonality in the main highway (M-30) and in urban areas of the municipality of Madrid, respectively (AM 2018a). The red lines in the right-hand side figures represent the imputed average annual net income of the car-sharing members with identified residence, excluding those living in Atocha, Aeropuerto, and Castilla neighborhoods (20,435e) (INE 2018b;AM2018c) 123 282 SERIEs (2024) 15:259–297 To test the hypothesis that higher-income customers predominantly use the service to commute, we categorized trips according to their purpose, based on the frequency of usage at different times of the day and the week (see Sect. 3). With this specification, we could categorize 56% of the trips from members with an identified residence. Of those,68.8%were commuting trips, while 31.2% hadaleisure purpose. The regression estimates from Table 6suggest that higher-income customers are more likely to take car-sharing trips to commute and less likely to use this service for leisure purposes. According to Becker et al. (2017), who focus on the case of Switzerland, “freefloating car-sharing is mainly used for discretionary trips, for which only substantially inferior public transportation alternatives are available.” Schmöller et al. (2015) find that car-sharing services in Berlin and Munich are used predominately for shopping and social-recreational activities. In Vine and Polak (2020), the authors argue that the purpose of car-sharing usage differs according to car ownership status. Car owners are more likely to use the service for business purposes (for instance, meetings), while non-car owners use the service for shopping purposes. Finally, Fig. 6represents the seasonality of trips according to their distance and duration. Car-sharing trips were generally short as a function of distance and travel time. The average figures were 23 minutes and 7 Km per trip (in line with previous studies,e.g., Ampudia-Renuncioet al.2020b;Sprei etal. 2019). Asexpected,trips take longer during peak hours due to heavier road traffic. On weekends and Fridays, trips are relatively longer in distance and time traveled. Additionally, over time, customers started to take longer trips (in terms of distance). 4.5 Gravity specifications Tables 7and 8present results from estimation of gravity-type models such as Eq. (1). Table 7is for baseline specifications. In the first column, we show results when only log distance is included as a friction variable, ignoring the availability of public transit. We find that car-sharing trips become less likely with increased distance between origin/destination pairs. According to our preferred specification (column 3), the point estimate is (−0.47). Given our Poisson specification, this coefficient needs to be transformed for ease of interpretation. For example, if the distance between origins/destinations were to double, then the number of car-sharing trips would drop by almost 28% [=exp(−0.47 ×log2)−1]. This may be partly explained by the fact that the costs of car-sharing trips can also increase significantly with distance (as the trip duration increases, and rentals are paid by the minute). In terms of the impact of public transit travel times, note that column (2) of Table 7 suggests a negative elasticity, while column (3) suggests a positive elasticity. However, the negative elasticity from column (2) may be confounded by the effect of distances. Column (3) is the preferred specification in the sense that it estimates the elasticity of car-sharing with respect to public transit travel times, while simultaneously controlling for the distance between the origins/destinations of the trips. In that case, the elasticity becomes positive, such that car-sharing is more likely as origins/destinations are less well-connected through public transit. The point estimate is (0.0037), which can be interpreted as follows: if travel time by public transit increases by 10 minutes, then 123 SERIEs (2024) 15:259–297 289 Table 9 Regression results for mobility patterns: customer level (sub-sample with identified residence) Member loyalty (1) (2) (3) (4) (5) Log annual net income (e) −14.351*** −13.774*** −13.307*** −13.873*** −14.840*** (1.577) (1.255) (1.714) (1.257) (1.596) Avg. no. of cars per household 13.212*** 10.403*** 14.189*** (1.279) (1.928) (1.387) % of residents with driving license −0.291*** −0.283*** −0.289*** (0.074) (0.075) (0.074) No. metro stations per km2−0.567** −0.169 (0.221) (0.241) No. train stations per km2−3.182** −1.562 (1.251) (1.291) No. bus stops per km2−0.152*** −0.052 (0.035) (0.041) Inadequate public transport network (%) 0.126*** −0.065 (0.039) (0.043) Observations 11,818 11,818 11,818 11,818 11,818 R20.020 0.018 0.020 0.012 0.020 Standard errors in parentheses: ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01 The linear regressions use customer observations and are restricted to the sub-sample of car-sharing users with identified residence, excluding those living in Atocha, Aeropuerto, and Castilla neighborhoods. The loyalty measure, used as outcome variable, measures the weighted number of days a customer used the car-sharing service, given by the total number of days used, weighted by the total number of days left in 2019 since the first day they used the service, multiplied by the total number of days in a year. The regressions provide correlations between the characteristics and amenities of the neighborhood and the loyalty/intensity of usage of the resident customers 123 290 SERIEs (2024) 15:259–297 Table 10 Regression results for mobility patterns: neighborhood level (full sample) Member density (1) (2) (3) (4) (5) (6) Log annual net income (e) 0.745*** (0.110) 0.935*** (0.329) 0.841*** (0.128) 1.001*** (0.259) 0.761*** (0.114) 0.948*** (0.326) Average no. of cars per household 0.474 (0.307) 0.218 (0.294) 0.358 (0.264) % of residents with driving license −0.020 (0.021) −0.014 (0.014) −0.018 (0.019) No. metro stations per km2−0.047* (0.027) −0.035* (0.020) No. train stations per km20.443 (0.315) 0.379 (0.230) No. bus stations per km2−0.002 (0.004) −0.002 (0.005) Inadequate public transport Network (%) 0.013 (0.011) 0.007 (0.010) Observations 135 135 135 135 135 135 R20.241 0.307 0.348 0.368 0.279 0.316 Standard errors in parentheses: ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01 These regressions provide correlations between density of car-sharing membership and the characteristics and amenities of neighborhoods. The linear regressions use neighborhood-level observations and include all neighborhoods of the municipality of Madrid in addition to the four municipalities covered by our study area. The outcome variable measures the neighborhood member density, i.e., the percentage of car-sharing users in each neighborhood, out of the total resident population. This density is set to zero for neighborhoods where we did not identify car-sharing members 123 SERIEs (2024) 15:259–297 291 Table 11 Regression results for probability of trip happening, linear probability model: trip level (extended sub-sample with identified residence) Probability of taking a car sharing trip (1) (2) (3) Log annual net income (e)−0.03777*** (0.00063) −0.00210*** (0.00060) Member loyalty10.00275*** (0.00001) 0.00275*** (0.00001) Control for weekly seasonality Yes Yes Yes Control for monthly seasonality Yes Yes Yes Observations 3,682,648 3,682,648 3,682,648 R2 0.015 0.101 0.101 Standard errors in parentheses: ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01 The table provides regressions results using a linear probability model, where the dependent variable is an indicator equal to 1 when a customer takes a car-sharing trip and 0 otherwise. We use customer-by-date observations. The dependent variable is set as missing for all dates prior to the first usage date of a given customer. This variable therefore measures the probability of taking a car-sharing trip after trying the service for the first time in 2019 1Weighted number of days a customer used the car-sharing service in 2019, computed as the number of days the customer used the car-sharing service weighted by the total number of days left in 2019 since the first day they used the service, multiplied by total number of days in a year 123 292 SERIEs (2024) 15:259–297 Table 12 Regression results for trip duration and distance: trip level (sub-sample with identified residence) Trip distance Trip duration (1) (2) (3) (4) (5) (6) Log annual net income (e)−2.10846*** −2.01185*** −1.93357*** −3.77156*** −3.85011*** −3.81258*** Member loyalty1(0.02620) (0.02639) 0.00379*** (0.02640) 0.00249*** (0.10407) (0.10492) −0.00308*** (0.10528) −0.00467*** (0.00014) (0.00014) (0.00054) (0.00055) Control for weekly seasonality No No Yes No No Yes Control for monthly seasonality No No Yes No No Yes Observations 497,009 497,009 497,009 497,011 497,011 497,011 R2 0.013 0.014 0.023 0.003 0.003 0.005 Standard errors in parentheses: ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01 Notes The linear regressions use trip observations and are restricted to the sub-sample of car-sharing users with identified residence, excluding those living in Atocha, Aeropuerto, and Castilla neighborhoods. The outcome variables are trip distance [in kilometers, for columns (1) to (3)] and trip duration [in minutes, for columns (4) to (6)] 1Weighted number of days a customer used the car-sharing service in 2019 123 SERIEs (2024) 15:259–297 293 Number of customers using the service 48 44 40 36 32 28 24 20 16 12 8 4 0 123456789 1011 12 Month of Year 2019 (a) All users Number of new customers joining the service 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 123456789 1011 12 Month of Year 2019 (b) New users Number of customers (thousands) Number of new customers (thousands) Fig. 7 Number of car-sharing customers per month, in 2019. Note The figure portrays the number of carsharing customers in 2019. Panel (a) shows the total number of customers that used the service in each month of the year, while panel (b) presents the number of new customers joining the car-sharing service for the first time, in each month 123 294 SERIEs (2024) 15:259–297 50 40 30 20 10 0 0 50 100 150 200 250 300 350 Weighted number of days a customer used the service, since first use Percent of customers Fig. 8 Frequency distribution of member loyalty. Note The figure shows the frequency distribution of the weighted member loyalty measure, which is given by the number of days a customer used the car-sharing service, weighted by the total number of days left in 2019 since the first day he/she used the service, multiplied by the number of days in a year. The vertical lines give the average loyalty of the full sample of customers (14.7 days). The median loyalty is 6.8 days Location of metro and train stations Train (Cercanias) Metro Neighbourhoods inside Madrid Municipality Outside Madrid Municipality (a) Train and metro Location of bus stops Bus stops Neighbourhoods inside Madrid Municipality Outside Madrid Municipality (b) Bus Fig. 9 Public transport network (metro, train, and buses). Note The figures show the geographical location of each metro, train, and bus station/stop in the municipalities of our study area (CRTM 2018a). All territorial units correspond to neighborhoods inside Madrid Municipality, except for the dashed units: Alcobendas, Coslada, Pozuelo de Alarcón, and San Sebastian de los Reyes municipalities 123 SERIEs (2024) 15:259–297 295 (0.42,2.55] (0.34,0.42] (0.28,0.34] (0.26,0.28] (0.23,0.26] [0.14,0.23] No data Fig. 10 Regulated parking slots, in 2018. Note: The figure portrays the number of regulated parking slots, weighted by the number of registered vehicles in each neighborhood. These parking slots are integrated within the Regulated Parking Service (Servicio de Estacionamiento Regulado, SER). In 2018, only 48 neighborhoods in the Municipality of Madrid were covered by the SER. All territorial units represented are neighborhoods inside Madrid Municipality, except for the municipalities of Alcobendas, Coslada, Pozuelo de Alarcón, and San Sebastian de los Reyes. The yellow line delimits our car-sharing study area Size of Fleet 400 0 200 600 800 01jan2018 02mar2018 01may2018 30jun2018 29aug2018 28oct2018 27dec2018 25feb2019 26apr2019 25jun2019 24aug2019 23oct2019 22dec2019 Fig. 11 Fleet of car-sharing company. Note The figure shows the FFCS rental company’s cumulative number of cars (fleet) operating in Madrid during 2018/2019. 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