In the mobility sector, a large number of new technologies such as autonomous vehicles (AVs) and services (e.g. carpooling) are emerging. AVs involve not only passengers, but also authorities, manufacturers, public transportation companies, law enforcement officials, drivers, pedestrians and shopkeepers. Applying phenomenology -the description of a phenomenon's live experience [1] -to this case of Mobility as a Service (MaaS) contributes to understanding its complexity and provides insights of users' perception of risk related to the AVs. This new technology brings many opportunities to improve our mobility system. Identified potential risks can affect the efficiency and the perception of the service. In this exploratory research, we have employed a technique called experimental phenomenology to identify these risks. The major advantage of this approach is to take into account the perception of passengers as a driver for design.

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This research project aims to improve the management and reliability of airport security gate procedures by redesigning passenger queues based on human ethology methodologies. While queues have been studied from many angles, a scientific contribution based on a human ethology approach proposing regulation of queue dynamics to improve security effectiveness seems to be novel. Queueing behaviors, observed in previous fieldwork, led to the hypothesis that queue structuring can have a positive impact on wait time perception. This hypothesis was operationalized, according to ethology experiment design, through a passenger queue simulation. The data collected (n=140) confirmed the hypothesis. Average perceived time was lower for passengers who put items to be X-rayed on the belt in a specified order, along with higher personal awareness compared to the usual case where no order is imposed. Although this research is exploratory, we have been able to provide airport security management with some practical insights.

Abstract

A sustainable neighbourhood was built Switzerland by one of the leaders in this field. Half of the 400 apartments have been equipped with smart meters delivering big data on energy consumption (electricity, water, heating…). The company would like to know if it is possible to link socio-demographic typology of residents with energy consumption patterns. To answer this question we present in this article a multimethod approach combining qualitative analysis, frequently used in marketing (multiple correspondence analyses), and quantitative analysis from applied statistics to answer this question. First, we have conducted a survey among the residents of the sustainable neighbourhood to gather socio-demographic data, and then we have proposed a marketing typology of residents. In parallel, we have analysed load curves with statistical models (clustering factors, hermano beta models, coincidence factors, som, expert practice) to see if there are patterns of energy consumption and to determine groups of similar load curves. Then we have compared the discrepancies in the composition of the groups between both methods. This study is based on a single case study generating a new research hypothesis: the typology of residents based on socio-demographic data can be linked to energy consumption pattern of a household.