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 Sensing Devices in Urban Transportation Systems

Summary/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.

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