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Smart Traffic Planning and Analytics, Autonomous Mobility Technologies, and Algorithm-driven Sensing Devices in Urban Transportation Systems

Smart Traffic Planning and Analytics, Autonomous Mobility Technologies, and Algorithm-driven... 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Contemporary Readings in Law and Social Justice Addleton Academic Publishers

Smart Traffic Planning and Analytics, Autonomous Mobility Technologies, and Algorithm-driven Sensing Devices in Urban Transportation Systems

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1948-9137
eISSN
2162-2752
Publisher site
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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

Journal

Contemporary Readings in Law and Social JusticeAddleton Academic Publishers

Published: Jan 1, 2021

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