ABSTRACT. Despite the relevance of smart traffic planning and analytics, autonomous mobility technologies, and algorithm-driven sensing devices in urban transportation systems, only limited research has been conducted on this topic. Using and replicating data from AAA, ANSYS, Atomik Research, AUVSI, Axios, Charles Koch Institute, Deloitte, eMarketer, Future Agenda, HNTB, INRIX, Kennedys, McKinsey, OpinionWay, and Perkins Coie, we performed analyses and made estimates regarding how self-driving cars can carry out accurate localization and can learn to enhance their behaviors through deep learning technologies. Self-driving car perception systems will completely remove human error as their applications monitor vehicle operations and enhance travel safety. The results of a study based on data collected from 6,300 respondents provide support for our research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: algorithm-driven sensing device; urban transportation system; smart traffic planning and analytics; autonomous mobility technology

How to cite: Griffin, K., and Krastev, V. (2021). “Smart Traffic Planning and Analytics, Autonomous Mobility Technologies, and Algorithm-driven Sensing Devices in Urban Transportation Systems,” Contemporary Readings in Law and Social Justice 13(2): 65–78. doi: 10.22381/CRLSJ13220215.

Received 12 June 2021 • Received in revised form 8 November 2021
Accepted 10 November 2021 • Available online 15 November 2021

Karen Griffin
This email address is being protected from spambots. You need JavaScript enabled to view it.
Smart Sustainable Urbanism Research Unit
at CLI, Swansea, Wales
(corresponding author)
Vladislav Krastev
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Faculty of Economics,
The South-West University “Neofit Rilski”,
Blagoevgrad, Bulgaria

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