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Title	Geographic Information Systems And Confidence Interval With Deep Learning Techniques For Traffic Management Systems In Smart Cities
ID_Doc	27910
Authors	Jayanthi P.
Year	2021
Published	Sensor Data Analysis and Management: The Role of Deep Learning
DOI	http://dx.doi.org/10.1002/9781119682806.ch11
Abstract	This chapter shows a few case studies of road accidents caused by various reasons such as environmental factors, drunken driving, poor education, and age. It presents SC models and traffic congestion prediction algorithms. Road accidents often lead to loss of human life. Human errors are often the reason for the occurrence of accidents and crashes. The chapter lists a few: overspeed driving, drinking and driving, distractions to driver, jumping red light signal, etc. Geographic information system has been implemented for analyzing the road accidents in different states of India for the years 2014-2017. The confidence interval is calculated using STATA software. Deep learning techniques can be implemented in traffic management too; this will help smart roads and, thereafter, smart cities too. The chapter concludes that traffic management is essential for the smooth flow of traffic in cities. © 2021 John Wiley & Sons, Ltd. All rights reserved.
Author Keywords	confidence interval; deep learning techniques; geographic information system; road accidents; smart cities; traffic management systems

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