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.

Keywords: connected vehicle data; intelligent transportation planning and engineering; smart mobility technology; predictive control algorithm

How to cite: Wallace, S., and Lăzăroiu, G. (2021). “Predictive Control Algorithms, Real-World Connected Vehicle Data, and Smart Mobility Technologies in Intelligent Transportation Planning and Engineering,” Contemporary Readings in Law and Social Justice 13(2): 79–92. doi: 10.22381/CRLSJ13220216.

Received 18 June 2021 • Received in revised form 12 November 2021
Accepted 14 November 2021 • Available online 15 November 2021

Susan Wallace
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Autonomous Vehicle Interaction Control
Software Laboratory at ISBDA, Dundee, Scotland
George Lăzăroiu
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The Institute of Smart Big Data Analytics,
New York City, NY, USA;
Spiru Haret University, Bucharest, Romania
(corresponding author)

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