Using living labs to investigate the transition towards electric mobility: the e-mobiliTI experiment in Southern Switzerland

Cellina, Francesca and Corani, Giorgio and Rizzoli, Andrea Emilio and Bonesana, Claudio and Bettini, Albedo and Baldassari, Andrea and Cavadini, Pasqualina and Soldini, Emiliano and Rudel, Roman (2015) Using living labs to investigate the transition towards electric mobility: the e-mobiliTI experiment in Southern Switzerland. In: Proceedings of the 15th Swiss Transport Research Conference STRC 2015, 15-17 April, 2015, Monte Verità Ascona.

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The diffusion of electric vehicles is one of the most promising opportunities to reduce dependency on fossil fuels and to pave the way for the transition to a more sustainable mobility. However, apart for the main barrier still represented by the purchase cost, the adoption of electric vehicles is still hindered by other barriers, such as autonomy, recharge time any general performance. Therefore, fostering a change in the present mobility patterns requires to go beyond the traditional technological approach and to explicitly address consumers perceptions and behaviour. In 2012 we launched the e-mobiliTI project to get a deeper understanding of the factors favouring or opposing the transition to e-mobility. This project builds upon the living lab approach, focusing on a small sample of families located in Southern Switzerland. Family members accepted to be monitored in all their trips, in exchange for the availability, for a period of three months, of electric cars and bicycles, public transport seasonal tickets and car and bike-sharing subscriptions. In Spring 2013 a first three-months monitoring phase allowed us to identify their present mobility patterns and styles, while in Spring 2014, during a second three-months monitoring phase, the participants experienced the new mobility options in real-world settings. In order to monitor travel behaviour, we relied on both quantitative automatic data-gathering techniques and on qualitative focus groups and interviews. Automatic data-gathering was performed thanks to a specifically developed smartphone application that relied on GPS tracks. To identify the significant variations of mobility patterns between the two monitoring phases, we developed a data mining approach based on regression trees. In this paper we present the results of the e-mobiliTI project and conclude with a critical analysis of our approach, especially regarding the problems in automatic data gathering and mobility profiling and the limited representativeness of our results, due to the small size of our sample and the short duration of the testing period.

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