Acceptability modeling of autonomous mobility on-demand services with on-board ride sharing using interpretable Machine Learning

Abstract

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.