Learning from the real practices of users of a smart carpooling app

Abstract

Aim: This paper explores the real practices of users of a smart carpooling application that learns their mobility habits and predicts their future trips to propose relevant matches. Method: A combination of usage data and interviews analysis allows us to explore the commuter experience from registration to the first and the next shared rides. Findings: The results highlight the shortcomings associated with human factors in carpooling and with humansmart system interactions. They show that perceptions of practical constraints and poor counterparts are the major reasons for difficulty in incorporating carpooling into daily mobility. Psychosocial barriers take different forms at different steps of the carpooling experience (search for information or guarantees about other users, the necessity of conversing with others, much uncertainty about how to behave). The fact that the service is smart amplifies these problems and reduces the desire to carpool again because it creates new misunderstandings (i.e., the user does not understand what the system vs. the other users do) and discomfort in relation to other riders (no answer, too many refusals, necessity of refusing, negative carpool experience, or concern over proposing a bad carpool). Despite these difficulties, the users perceive carpooling as a good solution and a positive human experience when the matching is accurate. We propose some recommendations to overcome the identified difficulties.

The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges

“…Finally, as for carpooling to the workplace or the place of study, social issues of non-work carpooling are poorly documented. Even if some studies have analysed the social motivations of carpoolers, such as conviviality and enjoyability [8,35] a comprehensive analysis of the social dimensions and impacts of non-work carpooling, especially regarding social integration and cohesion [84], accessibility [85] and also discrimination issues in the choice of a driver or a passenger [86], is still missing [17].…”

“…), or automatically. In other kinds of application, an algorithm does the job of determining the (best) driver-passenger matches [8]. In general, these online applications also include a tool for sharing travel costs, plus a commission payable to the platform if it is operated by a private entity [91].…”

“…Recently, Ref. [8] highlighted how some carpooling apps based on automatic matching of drivers and passengers amplified psychological barriers to carpooling. Several years ago, Refs.…”

“…In this article, the term is used in a more restricted sense since it excludes household carpools and family carpools, which currently account for the majority of carpooling practices [8][9][10]. Therefore, in the remainder of the text, carpooling refers to the sharing of car rides between people who are not members of the same household, for a trip (or a part of a trip) already scheduled by the driver and not for the purposes of profit (although costs of travel may be shared).…”

Human factors of digitalized mobility forms and services

“…The first paper, authored by Adelé and Dionisio [11] provides hands-on experience from a "smart" carpooling app that turns out to be less smart than expected. Based on the analysis of chat protocols, trip refusal information and qualitative interviews with users, practical limitations and psychosocial barriers of the app are identified.…”