Bo Yu

TL;DR: Examining and modeling drivers’ acceptance and behavior when receiving energy- and safety-related speed recommendations through vehicle-to-infrastructure communications can contribute to the optimization of energy-saving algorithms and the improvement of driving safety by using connected vehicle technologies.

TL;DR: In this article, the authors proposed an online weld-quality inspection method for ultrasonic composite welding by combining artificial intelligence (AI) technologies with welding process signatures, where the failure load in a tensile-shear test and the weld quality level (i.e., under weld, normal weld, and over weld) were predicted simultaneously using artificial neural network (ANN) and random forest (RF) models.

TL;DR: An innovative trip-level inference approach is proposed for quantifying the economic benefits of FFBS, leveraging massive FFBS transaction data, the emerging multimodal routing Application Programming Interface from online navigators and travel choice modeling, and the relationships between economic benefits from FFBS and built environment factors in different urban contexts are quantitatively examined.

TL;DR: A speeding prediction model based on quantifying the visual road environment to improve the design of pre-waring systems, which can predict whether drivers are going to speed and provide them with visual or/and audio warnings about their current driving speed and the speed limit prior to the occurrence of speeding behavior is established.

TL;DR: This study will lay a foundation for the improvement of traffic safety on mountain highways based on the quantification of drivers' visual perception, during the phase of both road design and reconstruction, and can also make a contribution to the automatic driving technique.