Aggressive driving behavior prediction considering driver's intention based on multivariate-temporal feature data.

Aggressive driving behavior is mainly motivated by the intention of the driver; therefore, the underlying intention of behavior should be considered in investigating aggressive driving behavior. However, existing aggressive driving behavior prediction methods are not advanced in a compelling of characterizing the driver's intention among a large set of attributes and describing the random process among time-varying transversion. To address this, this paper proposes a prediction method, which is structured with a Hidden Markov Model (HMM) and attention-based Long Short-Term Memory (LSTM) Network.

Shared mobility in post-COVID era: New challenges and opportunities.

This study is aimed at exploring the challenges and opportunities that the COVID-19 outbreak presents to the sustainability of shared mobility. To date, the sustainability of shared mobility has received little attention in the literature, and this study determines its central constructs that are critical to the sustainability of shared mobility. We accordingly conducted a three-phase Delphi approach composed of both qualitative and quantitative methods.