Georgeta-Madalina Oprea’s scientific contributions

New mobility-on-demand services together with the emerging technology of autonomous vehicles (AV) aim to revolutionize urban transportation systems, by introducing autonomous driving and sophisticated sharing and routing schemes for efficiently serving individual’s needs and requirements. On the other hand, the COVID-19 pandemic has disrupted travel patterns due to the emerging trends of social distancing and teleworking. In this paper, we aim at investigating users’ perception on autonomous vehicles, mobility on demand schemes as well as on the future transportation landscape using data collected through a questionnaire survey in the Metropolitan Area of Athens, Greece conducted after the first COVID-19 pandemic wave. First, a statistical analysis of the responses is performed and, then, a clustering approach is followed to identify user profiles based on daily mobility patterns and attitudes towards autonomous vehicles. Subsequently, the identified profiles are exploited in the development of a Bayesian Network to reveal interrelations between user profiling, attitudes and perceptions for future mobility services. Regarding the acceptance of Autonomous Mobility on Demand (AMoD) services, as well as travelers’ level of happiness concerning future scenarios of urban transportation, results have shown that the majority of travelers in Athens will be more than happy in the case where the entire transportation system is served with AMoD services.

Research on the factors that may affect the future uptake of autonomous Mobiliyt on Demand (MoD) services are today more than ever relevant. In this paper, we attempt to investigate all aspects of acceptability of a proposed Autonomous-MoD service (AMoD). More specifically, we develop mode choice models and identify the factors that affect it in both sunny and rainy weather conditions, using state-of-the-art Machine Learning models and interpretation techniques, such as the permutation feature importance and partial dependence. Furthermore, we estimate the willingness of the service’s potential users to pay for reduced travel time and propose an on-board negotiation scheme of the travel time and cost for sharing one’s ride. For the above purposes, we conducted a questionnaire survey with 1600 participants in the city of Athens, Greece just after alleviation of the lockdown and measures related with the COVID-19 first wave. The models developed are capable of predicting the mode choice and acceptability of the negotiation scheme with an accuracy of over 80%. Except for the cost, travel and walking time of each alternative mode, the users’ mobility profile, attitude towards autonomous vehicles and demographic characteristics are identified as the most important factors affecting the respondents’ choices. Moreover, the willingness to pay for reduced travel time varies from 0.18 to 0.62€, depending on the mode and about 0.53€ for the on-board negotiation.