Amin Mohammadnazar

TL;DR: A framework that harnesses Basic Safety Messages generated by connected vehicles to quantify instantaneous driving behavior and classify driving styles in different spatial contexts using unsupervised machine learning methods is developed.

TL;DR: In this article, the authors investigated the effects of AVs on the behavior of a following human-driver in mixed traffic streams and found that a driver that follows an AV exhibits lower driving volatility in terms of speed and acceleration, which represents more stable traffic flow behavior and lower crash risk.

TL;DR: The statistical model indicates that that Waze speed data are more accurate in peak hours than in night hours, and the traffic speed, traffic volume, and segment length have a significant association on the accuracy of Waze data on surface streets.

TL;DR: In this article, the authors applied a new methodology to capture variation in crashes in both space and time by using Geographically and Temporally Weighted Regression (GTWR) models for the localization of SPFs.

TL;DR: In this paper, the authors focus on safety aspects of connected and automated vehicles (CAVs) using advanced data mining and thematic text analytics tools, the goal is to systematically synthesize studies related to Big Data for safety monitoring and improvement.