Automated driving will trigger disruptive changes in the transportation system. Automated sharing and pooling options instead of private ownership are broadly discussed because of their possible contributions to climate change mitigation and sustainability. Despite the growing amount of literature on the adoption of these alternatives, little empirical evidence is available on the potential drivers of adoption, such as individuals socioeconomic background, mobility characteristics, attitudes, and values. To address this gap, we utilize the results of an online choice experiment involving 709 participants from Switzerland, which tested future mode choices considering automated cars, automated pooled-use taxis, and automated public transport shuttles, both for short- and long-term mobility decisions. Exploratory regression analysis explains the experiment outcome with a broad set of underlying data predicting willingness to use. Our findings illustrate that automated cars and automated public transport often appeal to the user groups of their traditional non-automated counterparts. However, this does not seem to be the case for automated pooled-use taxis, which we find to be associated with higher-income groups. Attributes on current mobility characteristics and values cannot be significantly associated with automated pooled-use taxis. We also demonstrate that short- and long-term mobility decisions are worth studying together in AV adoption studies.

First-Mile/Last-Mile is one of the burdened urban transportation problems limiting the effectiveness of public transit. Recently, the new emerged shared micro-mobility, e-scooter, came to fill this network gap and gained its popularity across the world. For this specific short-range trip mode, the demand is highly volatile from time to time, so demand and volatility prediction should be incorporated. For this reason, Box-Cox transformation, seasonal ARIMA (SARIMA), and family of GARCH models were used to predict its hourly demand and volatility, using hourly e-scooter demand from 23 Jan to 30 Apr 2019 in Thammasat University, Thailand. The combination of these models provides a very important understanding of the demand pattern that is necessary for operational planning.