Predictive Control Algorithms, Real-World Connected Vehicle Data, and Smart Mobility Technologies in Intelligent Transportation Planning and Engineering
Predictive Control Algorithms, Real-World Connected Vehicle Data, and Smart Mobility Technologies in Intelligent Transportation Planning and Engineering

Summary/Abstract: Employing recent research results covering predictive control algorithms, real-world connected vehicle data, and smart mobility technologies in intelligent transportation planning and engineering, and building our argument by drawing on data collected from Brookings, Capgemini, Ipsos, Jones Day, Kennedys, KPMG, MRCagney, and Pew Research Center, we performed analyses and made estimates regarding how road anomaly detection, motion planning, and tracking control algorithms shape behavioral intention to use autonomous vehicles, optimizing smart and sustainable urban mobility by reducing traffic congestion and motor vehicle collisions. By use of visual environmental perception and route planning algorithms, autonomous driving technologies will reduce preventable road crashes and injuries, resulting in self-driving car acceptance and adoption. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

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