Joint Research Centre
Digital Earth Lab

The recent diffusion of mobile Internet applications has given a new boost to traditional ridesharing by enabling to connect to online community-based platforms and to readily access to available car seats in real-time.  This research investigates the success factors behind online ridesharing, besides the technological advancements, and the features of the ridesharing community members, on the basis of BlaBlaCar data in Central Europe.

 

Figure 1: Snapshot of the BlaBlaCar rides in and across France.

Energy efficient, safe and environmentally friendly urban mobility is essential for ensuring that economic growth will be accompanied with good quality of life. The increasing awareness of these requirements, along with the recent diffusion of mobile Internet applications, has given a new boost to traditional ridesharing, by enabling to connect to online community-based platforms and to readily access to available car seats in real-time.

In this activity, we focus on the recent success of online ridesharing platforms, in order to infer additional properties of social mobility.  Accordingly, we have been interviewing staff members of BlaBlaCar France and obtained licensed data access for the purposes of this analysis. The analysis of online ridesharing activity will shed light on two strong components of social mobility. The first is the composition of the ridesharing community, their main properties and demographic profile information. By analysing anonymized online profiles of the community members, we aim at identifying the conditions that facilitate generating trust within a community, and how the publication of personal information can catalyse such a process. In this part of the study, we will also focus on the members’ preferences in the publication of personal information, and comment the susceptibility to misuse and unauthorized uses thereof.

Besides community features, this initial stury focuses on the analysis of the rides, the characteristic of the routes, driving preferences and typical trip duration. In this respect, we aim at evaluating the main features of the available rides, and the level of utilization of urban, regional and inter-urban ridesharing services (e.g., drive and ride), to identify promptly the strengths and weaknesses of current inter-modality plans, and assess citizen engagement in such services. The complexity of such estimations increases for dynamic assessments. In this respect, we aim at supporting the planning of efficient inter-modality programs by defining and measuring key performance indicators, such the average trip time, the frequency of use, the most engaged communities or individual profiles, and the geographical distribution of shared rides.

Future activities will consider the integration of existing data sets with additional sources of information (e.g., Via Michelin, traffic feeds), aiming at a more precise characterization of the observed properties. Similarly, the proposed methodology can be validated through comparison in other macro areas in Europe.

 

Figure 2: Traffic flow graph across the 50 most visited cities in France.

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