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Carpooling for Long-Distance Transport in Italy: First Insights on Users, Usage and Geography

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Carpooling for Long-Distance Transport in Italy: First Insights on Users, Usage and Geography XVIII RIUNIONE SCIENTIFICA DELLA SOCIETÀ ITALIANA DI ECONOMIA DEI TRASPORTI E DELLA LOGISTICA, GENOVA, 4-5 LUGLIO 2016. Carpooling for long-distance transport in Italy: 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 (carsharing, 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 Uber or Lyft, adopt a totally 2 XVIII RIUNIONE SCIENTIFICA DELLA SOCIETÀ ITALIANA DI ECONOMIA DEI TRASPORTI E DELLA LOGISTICA, GENOVA, 4-5 LUGLIO 2016. 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.
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