mardi 26 août 2014

CESA abstract : how to encourage dynamic carpooling (VeDeCoM)

In the world of Internet of Things, the car is becoming rapidly one of the main connected element in our every day’s life. Our research topic deals more specifically with drivers who connect their smartphones to carpool with strangers.

In fact, smartphones are able to communicate crucial data for eco-mobility, such as the number of “empty seats travelling” (Nokia Research, 2009), available for potential passengers. Thanks to the GPS, 3/4G networks and “dynamic carpooling” applications, the car stands out as the new “public-private” transport.

Dynamic carpooling develops in a lightning way, through applications like Carma, Covisoft, Djump. It has additional benefits compared with the planned carpooling: more flexibility/immediacy for the drivers and passengers matching, complementarity outside the peak hours, optimization of the urban travel, etc. However, as summarized in our state of the art and through our first empirical study in psychology ergonomics, there are also obstacles in the deployment of dynamic carpooling. Indeed, besides technical issues (real-time aggregation of data, achieving a critical mass), dynamic carpooling raises several psychological and social barriers, which can generate discomfort and mistrust in users experiences.

For example, less time for the online driver’s choice and shorter users’ profile can increase three types of perceived risks that we could observe: interpersonal, organizational or relative to road security. To facilitate the process of trust building and to maintain a certain level of trust throughout the user experience, the following options could be considered: the creation of a “tribe”/a community, in which drivers receive some incentives to share their car, various contents of users’ profiles and available criteria, serious ways for controlling a new driver in the community, etc.

All these statements show the need to document past and actual experimentations of dynamic carpooling and, more precisely in user lived experience, what has worked and what has not?

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