XVIII RIUNIONE SCIENTIFICA DELLA SOCIETÀ ITALIANA DI ECONOMIA DEI TRASPORTI E DELLA LOGISTICA, GENOVA, 4-5 LUGLIO 2016.

Carpooling for long-distance in : first insights on users, usage and geography

Alberto Bertolin1, Paolo Beria1∗, Gabriele Filippini2

1Dipartimento di Architettura e Studi Urbani, Politecnico di Milano, Via Bonardi 3, 20133 Milano, Italia 2Studio META, Via Magenta 15, 20090 Monza, Italia

Extended abstract

Innovative mobility practices (, carpooling, electric mobility, etc.) show an increasing penetration in European markets. Although still marginal in terms of total mobility, these new modes are becoming important niches in specific contexts. At the same time, they provide useful information in terms of mobility practices. The paper analyse a sample of data collected from the well-known carpooling web-platform BlaBlaCar, in Italy. The aim of the work is twofold. On the one side we want to analyse and better understand the dynamics of diffusion of the carpooling at the national scale. Secondly, we want to verify if and how, in perspective, such data could be used to obtain information on the least known segment of mobility, namely the occasional long-distance mobility.

Data has been collected from the BlaBlaCar online portal during July 2015, recording all publicly accessible trips from a sample of five Italian cities: Milano, Roma, Napoli, Ancona e Vicenza. In total, the observations include 10.838 trips, offered by 6.557 drivers. The information collected include date and time of the trip, itinerary of the trip, price, sex and age of the driver. All trips collected has been cleaned, localised in a GIS-database and passed to an Access database to be elaborated. The analyses focus on the profile of the drivers (age, sex, type of car), the mapping of the origins and destinations of the trips, the different catchment areas of the five cities sampled, the average distance, the frequency and timing of trips.

The first results show how the users of the service are still a marginal share of total mobility, but may provide important data to understand the mobility practices. For example, the trips supplied have an average distance of 300km, but this average varies significantly in function of the city of origin, with the main cities generating trips towards larger areas. To a certain extent also latitude matters: carpooling is, to date, more diffused in the main cities of the North. Also the timing of trips is differentiated across the city sample, differentiating among generators and attractors. In conclusion, the phenomenon of carpooling is not only interesting per se, but also because providing information about trips otherwise impossible to map, such as weekly commuters or second-home weekend trips. keywords: Carpooling, mobility, long-distance, geography.

∗ corresponding author: Paolo Beria ([email protected]) 1 XVIII RIUNIONE SCIENTIFICA DELLA SOCIETÀ ITALIANA DI ECONOMIA DEI TRASPORTI E DELLA LOGISTICA, GENOVA, 4-5 LUGLIO 2016.

1. Introduction and aims Cities are considered the core-place of innovation in mobility practices. Carsharing, bikesharing, infrastructure for electric charging, re-use of public space, ITS applications to facilitate urban trips, urban logistics, etc. The scope for innovation of all of these trends is definitely large and many successful experiences are popping-up in most of European cities. Long-distance transport is less in the spotlight, and consequently also less studied by academic literature. But also here, significant changes are taking place, especially where liberalisation of public transport services have taken place, such as in rail and coach, not to talk about the revolution of low-cost airlines of the year 2000s (Dobruszkes, 2013). Scope of this paper is to analyse an apparently niche-phenomenon, whose importance and interest is actually rising, such as long-distance ridesharing. Ridesharing, differently from carsharing or mobile-apps for taxi, jitneys or lift booking, is focused at sharing the costs of car travels among private users and not in making a profit for the driver. To do that, we analyse a sample of data collected from the well-known carpooling web- platform BlaBlaCar in Italy and represent it geographically in order to better understand the form of this innovative and potentially ground-braking transport “mode”: who are the users, where is most effective and which are the characteristics in space and time of supplied routes. At the same time, we will discuss if and how this mass of observations might be used to enrich the knowledge on the entire segment of long-distance mobility, which is by far more difficult to be known and represented by means of traditional sources, such as counts, surveys or census. To date, it is a still unexplored and extensive source of technology-enabled data (Grant-Muller et al., 2014), aside to more studied social media, GPS, and digital data from intelligent transport systems.

2. Carpooling and ridesharing: literature review and first evidences Carpooling and ridesharing (or even carsharing) are partially overlapping concepts because they all refer to the optimisation of the use of a vehicle by sharing it with other users. Actually, their main difference lays in what is shared, namely a ride which is done anyway by one driver (ridesharing), or a car which is pooled among a group of users (carpooling) or owned by a provider and short-rented to subscribed users for an occasional use (carsharing). Another substantial difference is the absence of a profit for the driver in ridesharing: prices are fixed in order to cover part of the cost of the trip (Chan & Shaheen, 2012), exactly as if a group of friends would reach a common destination with one car instead of separately. In this paper we mainly refer to ridesharing, because we use the data of a platform whose aim is not to create groups of users to pool a car for commuting, but to help the matching between the supply of a single trip and the demand for it. However, the literature on this kind of service is very scant and must be integrated with that of carpooling. Ridesharing is a recent concept of rising importance, thanks to a new generation of systems (mostly mobile-based) which make extremely easy and effective the matching between supply and demand (Chan and Shaheen, 2012). BlaBlaCar is just one of these applications for technology-enabled ridematching, with specific characteristics and focudes on not-for-profit. Other well-known services are or , adopt a totally

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different business model, relying also on professional drivers and being more devoted to “on-demand” services.

Going back to the first experiences, older studies, dating back to Seventies and Eighties (see for example the review in Erdogan et al, 2015), focused on vanpooling and on its potentialities to reduce traffic and externalities. Initially, the target people were those not needing a car during the day, have fixed and limited work schedules and long and regular travels. In other words: commuters. Soon, concerns raised on the accompaining policies needed to foster this transport modality. Incentives and disincentives (Koppelman, Bhat, and Schofer, 1993) appeared to be crucial, for example the availability of HOV lanes or differentiated pricing at highways, but not sufficient to make a real momentum and prevent even a decline in share of this mode of travel in the US (Furuhata et al., 2013). One of the main expectations (Chan and Shaheen, 2012; Noland et al., 2006) around vehicle sharing and pooling is related to environmental benefits: a pooled trip is a car trip less. Toth (2015), however, interestingly argues that in contexts well supplied with public transport, the main users of carpooling are public transport users. This may give null or even negative effects in terms of CO2 and riderships because pooling decreases the cost of private transport and consequently increases it use. The variables found to influence the use carpooling and ridesharing services are residential distance, parking cost, the existence of web application and, not surprisingly, car availability (Zhou and Kockelman, 2011), gasoline price (Zolnik, 2015) and other socio-economic characteristics.

From an empyrical point of view, most of the literature deals with operational research and computing, based on algorithms design and optimisation, partially aimed at defining the potentiality of ridesharing markets. For example, Bicocchi and Mamei (2014) design procedures and software to support ridesharing by identifying automatically the type and the trace of past trips and suggesting possible matches between different users on similar trips. Cici et al. (2014) apply algorithms for trips recognition and ride-matching to four cities, obtaining an upper theoretical threshold of shared urban trips of 31%. Even larger results are found by Shmueli et al. (2015), in the sub-sample of taxi rides: in New York the potentiality of trips saved is as high as 70% with limited disconfort by users (5 minutes). Ridesharing is also increasingly introduced in transport modelling where is treated as a separate new mode models (Aissat & Varone, 2015; Xu et al., 2015).

In conclusion, what is interesting from our perspective is that almost all studies consulted, especially from the US, concentrate on commuters and on the urban or suburban range of trips, eventually finding limits and missed expectations. Even after decades of trials and hundreds of applications (Chan and Shaheen revise even 638 ridematching programmes just in North America), ride-sharing is not considered as a large-scale phenomenon and not among disruptive policies (Shay and Khattak, 2010), mostly interesting for niche applications.1 Just a few years later, as we will show in the paper, ridesharing is involving hundreds of thousands of trips per year in a country, like Italy, which is surely not a fist-comer in the

1 Such as pooling among university students. Erdogan et al., 2015. 3

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sector. And, more unexpectedly, the penetration is not (only) on short-range commuting, as most of literature seems to suggest, but especially in the medium-long range transport. In this work we will try to contribute to extend the knowledge on this segment, aside to the few works revised here. Furuhata et al. (2013) are among the first showing the dimension of the phenomenon, revising 25 web-services for long-distance ridesharing, of which 15 exclusively dedicated to it. They also classify them, for example separating bullettin-board based ones from active coordination tools, usually working through mobile or web-apps. In general, users of these services have more flexible travel schedules than on-demand travelers and commuters. The most similar study than ours, both for object (long-distance carpooling) data and purpose, is the already mentioned Toth (2015). He has analysed nearly 200.000 routes offered by the website www.oszkar.com in during 2011, and complemented them with a survey. He has been able to draw a picture of the system, documenting a number of elements which might be present also abroad. First of all, the platform is much more effective in matching trips on the long distance than on the short or even urban. Despite many trips are posted even for urban relations, these usually remain unmatched (often at rates of 0%-1%). Main intercity routes, especially departing from Budapest, have rates of pooling often above 50% or more. Also distributions along the week are analysed, finding peaks of trips on Friday, Sunday and limitedly morning and lifts almost concentrated on Friday and Sunday. This is the typical pattern of weekly commuting, with people working in main cities and going back home or going to leisure destinations for the weekend. It is also given a glimpse on the effect of intermodal competition. One of the causes of success of the system is the fact that carpooling contributions are usually lower than the corresponding public transport fare. In the following section we will introduce the web-tool used as data source, BlaBlaCar, and its main features.

3. Main features of Blablacar Blablacar, a company founded in in 2004, represents today the largest online platform devoted to ridesharing (carpooling) service in the world. Recently, the platform reaches a base of about 25 million members (drivers and/or potential passengers), is active in 22 countries and quarterly through its portal are organized around 10 million trips on an average distance, in Italy, of about 250 km. (Blablacar, 2016. The Post, 2015) The growth of this start-up, which today may be seen as a monopolist in countries where it is widespread, has been possible thanks to both the progressive acquisition of similar platforms already present in the each national markets (until 2012 the largest competitor was PostoinAuto.it in Italy) and the financial support of international investment funds (i.e. Partners with $ 10 million in 2012, ISAI with $ 125,000 in 2011 and Index Ventures with $ 100 million in 2014) (CrunchBase, 2016). The main strengths of this service are:

• A common platform that collect all lifts publisched by each user for every country where this type of service is active;

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• A very large and active community that ensures a large user base (both demand and supply) and a constant control from below which, in principle, penalizes the improper behavior (both drivers and hitchers can enter feedback on users encountered and on the trip experience)2; • A centralized service of moderation, which prevents the improper use, such as the commercial use of the platform; • Costs and travel times comparable to or lower than those of competitors, in particular rail services.

Initially hitchers were required to pay only a tariff equal to the cost of the journey (fuel, toll if any and, in part, vehicle maintenance). In order to allow comparison between lifts, BlaBlaCar decided to set a standard cost of 5.5 cents/km per passenger, giving to drivers the possibility to arbitrarily increase or decrease this tariff up to a maximum of 50%3. Since 2011, together with the on-line payment, it was gradually introduced a fee for the service provided at the expense of passengers only. Thus, since that year, the travel cost (€) for the passenger is equal to the result of the formula presented below:

P = (0.55€ + PP*(1 + 6.6%)) * (1 + 22%)

Where [PP] indicates the price defined by the driver to which it applies a commission with a fixed component (€ 0.55 excluding VAT) and a variable one (6.6% VAT). To the value thus obtained, an additional 22% is added (equivalent to the current value of VAT in Italy) and the final result represent the tariff for each passenger [P]. For example, from Milan to Venice standard tariff are between 15 to 20 € (from 0.055 to 0.075 €/km), between Milan and Naples it is from 35 to 53 € (0.045 to 0.068 €/km) and between Rome and Pescara it is from 10 and € 15 (from 0.048 to 0.072 €/km).

4. Methodology This article is based on data that authors manually collected from Blablacar website in July 2015. Through more queries, all lifts, publicly accessible4, related to the municipalities of Milan, Rome, Naples, Ancona and Vicenza, in the week of 15 to 21 June 2015 were

2 In respect to the online reality that base their business model on the , some scholars raised doubts about the effectiveness of the rating practice as a tool for user discrimination. Thanks to a dataset of 190,000 reviews of Blablacar users, Slee (2013) shows that, given a choice between a score of 1 (low) to 5 (very high), in 98.9% of cases users opted to give the highest rating to the overall travel experience, while in the remaining 1.1%, the detected score turns out to be the minimum. According to Slee, users are led to give in any case the maximum score to the trip, except when it is not taking place, so that the counterparty will behave in a similar way, thus not affecting the possibility to make/receive a lift in the future. 3 This variable was presumably introduced to allow drivers to take into account other aspects such as the comfort of their own vehicle. 4 The collected data do not inculde lifts defined as "Ladies Only". These trips are published by female drivers and viewable only by other female users once they are logged in the BlaBlaCar website. 5

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extracted (10,838 total trips published by 6,557 drivers and equivalent to about 1/3 of lifts5 offered in Italy at that time). Due to the impossibility to consider lifts from every Italian municipality, it was decided to concentrate the collection and analysis of data on the five provincial capitals listed before. These cities were selected on the basis of two criteria. First, since the service is mainly oriented towards long-distance journeys, it was decided to include in the analysis only cities that, for number of workers and students, have a great attraction power towards other locations, either at regional level (i.e. Ancona and Vicenza) or at supra-regional level (i.e. Milan, Rome and Naples). Secondly, we tried to obtain a sample both quantitatively significant and uniformly distributed between North, Central and South of Italy, in order to capture the phenomenon at the national level. The information collected for every single lift are referring to:

• departure date and time; • route; • tariff per hitcher; • gender and age of the driver.

Once collected, data were filtered in order to maintain only lifts offered at a tariff above or equal to 3€6. Subsequently they were geolocated and transferred in an Access database in order to process them. One of the most time (and patience) consuming operation was the data cleaning and restructuring, needed to make the data useable.7 The cleaning has been made through semi-automatic procedures, both to recognise the localities selected by the users and to geolocalise them. The typical form of data before and after the cleaning is represented in Table 1, Table 2 and Table 3.

5 The approximate value of 30,000 lifts was estimated thanks to a fast inspection of BlaBlaCar website by setting as place of origin and/or destination each of the 110 Italian provincial capital and 3€ as a minimum tariff. The value shown represents the multiplication of the number of results per page and the number of total pages obtained from each query. 6 From the sample collected were excluded lifs offered at a tariff lower than 3€. We have chosen not to include these lifts because we were focusing on medium and long distance trips, while, trips with a tariff lower than 3€, were mainly occuring inside the boundary of a single municipality or in its surrounding area. 7 Grant-Muller et al. (2014) discuss the large potentialities of social media to collect rich transport data. However, a number of problems and challenges must be solved to obtain useable data, such as the need of text-mining, excess of data, low data quality, especially concerning geolocalisation.

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Table 1: Example of raw data on trips

Orig. Orig. Orig. Trip id Driver id Orig. Lat. Place Desc. Long. musei 111111 Abcde123 Roma vaticani 41,90 12,45 roma

Dest. Dest. Dest. Desc. Dest. Lat. Date Time Tariff Place Long. Piazzale Luigi Emanuele Milano - - 17/06/15 12:00 31 Corvetto,Milano, MI

Table 2: Example of raw data on drivers

Driver id First name Last name Gender Age Abcde123 Marco C M 52

Table 3: Example of geocoded data

ISTAT ISTAT Municipality Municipality Trip id Driver id Code Code Orig. Dest. Orig. Dest. 111111 Abcde123 58091 Roma 15146 Milano

Distance Internal Date Time Gender Age Tariff Km trip 17/06/15 12:00 M 52 478 0 31

5. Results. Geographic distribution of carpooling in Italy

5.1 Who is the common driver?

A first information coming from our dataset is related to the profile of the common driver of BlaBlaCar. According to our sample, male drivers (83%) are most highly represented than the female ones (17%). However, this datum is in contrast with the official statistics, published by BlaBlaCar and reported by Mulder et al. (2016), where is shown that the proportion between male and female users (drivers + hitchers) is equal to 54% for the first group and 46% for the second. This discrepancy is certainly influenced by the data extraction process. As we described before, our dataset does not include both trips published as “Ladies Only” and any information on hitcher profiles. Despite these discrepancies, it is possible make a first assumption on the attitude of the female component towards carpooling. The imbalance between our results and the official statistics may be read as a strong diffidence of female drivers in offering passages 7

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to unknown male hitchers. This assumption is coherent with the result of the AUDIMOB (2015) research. According to this research, 60.5% of respondents are willing to use carpooling at least 2 or 3 time per week if more information on rides and security are provided. A second aspect is related with the age of drivers. The age pyramid (Figure 1) shows that both the female and the male samples are highly concentrate in the younger age groups (67% of female and 56% of male drivers are in between 21 and 35 years old). However, the decreasing trend between the younger and the older age groups is more gradual for the male sample. For both categories the peak of drivers coincides with the age group 26 – 30 years.

Figure 1: Driver Profile - Age Pyramid Source: Our elaboration on BlaBlaCar.it data.

In summary, the common driver of BlaBlaCar in Italy it is a man aged between 26 and 35 years.

5.2 Where users go?

Another relevant information is related with the top routes served from our sample of five municipalities. Table 1 shows total lifts (generated and attracted) for each top route during the third week of June. Beyond an high and foreseeable variability in the absolute values between medium and big size cities, we can also see that trips related with medium cities (Vicenza and Ancona) are highly polarized on few destinations. Moreover, these trips are mainly taking place along the highway directly linked with these medium size cities (highway A4 in the case of Vicenza and highway A14 in the case of Ancona). On the contrary, the bigger polarity of Rome and Milan, despite a certain level of route concentration (lifts between Milan and Bologna are almost the double of each primary link of the other four cities), interact with a larger territory equal to half of the Italian peninsula.

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In this classification, Naples represents a separate case. In fact, despite this city is comparable with Milan for number of residents, it shows an high concentration of lifts toward a single municipality (Rome) while other routes are marginal. The anomaly of Naples could be read as a different level of penetration of this service between North and South of Italy. This explanation is coherent with official statistics on BlaBlaCar subscribes. According with these data, 60% of subscribes are living in the North of Italy and another 11% in Lazio Region.

Table 4: Top routes for each sample city

Vicenza % Rome % Milan % Verona 183 37% Naples 266 7% Bologna 471 9% Milan 73 15% Florence 265 7% Genoa 342 6% Total 489 Caserta 197 5% Turin 314 6% Bari 195 5% Padua 232 4% Naples % Milan 161 4% Florence 198 4% Rome 266 31% Bologna 125 3% Verona 184 3% Florence 48 6% Salerno 123 3% Bergamo 181 3% Bari 36 4% L’Aquila 111 3% Parma 171 3% Salerno 36 4% Perugia 87 2% Rome 161 3% Avellino 24 3% Frosinone 84 2% Venice 121 2% Lecce 17 2% Lecce 72 2% Trento 100 2% Total 850 Benevento 65 2% Piacenza 97 2% Rome 56 2% Brescia 96 2% Ancona % Grosseto 54 1% Rimini 95 2% Pescara 174 19% Total 3.729 Total 5.436 Rimini 155 17% Bologna 129 14% Total 924

Source: Our elaboration on BlaBlaCar.it data.

Results obtained from Herfindahl–Hirschman index (HHI)8 are a further proof of a different level of trips concentration between medium and big cities. The index for the cities of Ancona, Vicenza and Naples is respectively 0.09, 0.18 and 0.11. These values indicate a moderate level of trips concentration toward few destinations. On the contrary, Milan and Rome, with a common value equal to 0.03, are characterize by an high level of trips dispersion. This reflects the fact that the catchment area of the largest cities is wider than that of the medium ones, which instead are more dependent on a limited number of neighboring centres.

8 The Herfindahl–Hirschman index is a measure of the size of firms in relation to the industry and an indicator of the amount of competition among them. It is defined as the sum of the squares of the market shares of the firms within the industry. In our case we assume as market share the number of trips related with a single destination and the total amount of trips as the industry. 9

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In order to better understand these differences we show in Figure 2, Figure 3 and Figure 4 lifts distribution of the two borderline case of Milan and Vicenza and the outlier case of Naples.

Figure 2: Lifts from/to Milan aggregated on a weekly basis (15-21 June 2015) Source: Our elaboration on BlaBlaCar.it data.

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Figure 3: Lifts from/to Vicenza aggregated on a weekly basis (15-21 June 2015) Source: Our elaboration on BlaBlaCar.it data.

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Figure 4: Lifts from/to Naples aggregated on a weekly basis (15-21 June 2015) Source: Our elaboration on BlaBlaCar.it data.

Different are the average distance traveled by users in relation to the sample cities from which or to which lifts take place. In the case of Milan the distribution curve shows that most of lifts are within a distance of 300 km (230 km as average value) and significantly collapses after this threshold. Rome, although having an average value similar to that of Milan (about 260 km), has a distribution of lifts that exeed the threshold of 400 km.

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With Naples (280 km approximately) the phenomenon of routes lengthening is further amplified, while the weight of short-haul or local routes decrease. Ancona and Vicenza on the contrary, with an average distance respectively of 180 and 130 km, are much more oriented toward local routes while longer links are marginal.

Figure 5: Lifts distribution on classes of distance (crow flies km) Source: Our elaboration on BlaBlaCar.it data.

The distances measured from our sample confirms the value declared by Blablacar on average distances of lifts.

5.3 How often occur departures and arrivals?

The sample cities show also a great variability as regards the lifts distribution over the week. In Figure 6 and Figure 7 are again presented the borderline cases of Milan and Vicenza. In the first case it is evident that the day with the highest number of departures from Milan is Friday (32% of total weekly departures from Milan) and the peak of arrivals is distributed among Sunday (24%), Monday (14%) and Tuesday (16 %). This distribution confirms the role of Milan as a city affected by weekly commuting flows of workers and students, that return to visit their families over the weekend. The same trend is also visible in the case of Rome and, with a lesser extent, in that one of Ancona. In the second case of Vicenza is evident that this city is affected by an opposit weekly trend. Consequently the city is classifiable, for the weekly commuting, as a center of traffic generator in favor of large centers. Due to the high volumes of exchanges that occur between the capital of Campania Region and Rome, Naples belongs to this second category too.

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Figure 6: Lifts from/to Milan – weekly distribution Source: Our elaboration on BlaBlaCar.it data.

Figure 7: Lifts from/to Vicenza – weekly distribution Source: Our elaboration on BlaBlaCar.it data.

Finally it is possible to deduce some information on the type of mobility that is associated with this carpooling service. Thanks to the aggregation of rides per drivers for each sample municipality (Table 2) we are able to affirm that, during the analised week, almost all of the drivers has offered a maximum of 2 lifts (from 87% of users to Ancona 96% of that of Naples). These rides, for example, correspond to a round trip on the same route. According to this we can say that the majority of users are refferring to carpooling as an occasional service. The number of users with a more systematic behavior (more than 2 rides per week), is much lower, while marginal is the number of users who offer a lift per day or more.

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There is therefore no evidence that the platform is improperly used by drivers who try to obtain a stable profit. This occurrence it is also incompatible with the revenues per ride imposed by the tariff scheme of BlaBlaCar.

Table 5: Number of rides per driver for each sample municipality

N° Rides per Vicenza % Naples % Ancona % Rome % Milan % Driver 1 153 55,4% 355 63,5% 211 42,4% 1.594 64,7% 1.981 59,6% 2 93 33,7% 181 32,4% 222 44,6% 743 30,2% 1.114 33,4% 3 10 3,6% 7 1,3% 32 6,4% 67 2,7% 108 3,2% 4 14 5,1% 7 1,3% 28 5,6% 36 1,5% 77 2,3% 5 1 0,4% 3 0,5% 2 0,4% 10 0,4% 20 0,6% 6 2 0,7% 2 0,4% 2 0,4% 4 0,2% 20 0,6% 7 ------6 0,2% 8 - - - - 1 0,2% 4 0,2% 2 0,1% 9 - - 1 0,2% 1 0,0% 1 0,0% 10 3 1,1% 3 0,5% 2 0,1% 6 0,2% 11 1 0,0% 12 1 0,0% 13 - - 14 - - 15 - - 16 - - 17 - - 18 - - 19 - - 20 1 0,0%

Source: Our elaboration on BlaBlaCar.it data.

6. Conclusions and further analyses In this work we have collected, restructured and used a large database of observations concerning individual trips offered by drivers on the web-platform Blablacar. The database has been used to derive some basic statistics, but in particular to map in a detailed way how the trips are spread in space and time. Despite being a still limited application, we can derive some general trends describing the geography of ridesharing in Italy and, as we will comment in a while, also on long-distance occasional mobility in general: a. big cities generate trips towards larger areas with respect to mid-sized towns; b. the dimension of the city is not the only relevant dimension: there are geographical differences whose explanation is not directly obvious. For example, why the inhabitants of Naples go only to Rome? Why romans prefer to go to the seaside in Apulia region instead of Calabria? The typical answer to this question deals with history, preferences, habits and cannot be proxied by measures such as number of beds in turistic facilities or with the dimension of the attractor and ultimately cannot be modelled in a conventional way; c. also time patterns are complex. Peak days are Friday and Sunday, suggesting a large use for weekly commuters, but the hours of the day vary across cities;

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d. the trips offered are clearly occasional. There is no evidence of a professional use of the consulted platform and long-distance mobility is periodic just for a negligible share of travellers, mostly concentrated into main cities. Further analyses can be imagined: i. to consider all Italy and not only five cities ii. to map travels, per capita travels, and seasonality iii. try econometric analyses to explain the differences observed in the use, such as the effectiveness or not of public transport iv. to analyse also lifts and not only car trips In conclusion, as already underlined, we are not interested to ridesharing trips only, but also to the use of this kind of data as a source of structured and high-quality information for general demand estimations. Typically, origin-destinaiton matrices are available for commuter demand only and usually are derived from census or small surveys. National- scale demand is instead barely known, also because of the complexity and cost to collect it. Alternative data sources, such as this one, could be effectively used to have a picture of “warps” to be applied to conventionally generated origin-destination matrices to take into account unponderable aspects such as antique immigration patterns, university mobility, health mobility, etc.

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Acknowledgements The research is part of the project “QUAINT”, supported by the Italian Ministry of Education University and Research (MIUR), within the SIR programme (D.D. n. 197 del 23 gennaio 2014).

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