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Smart Parking Systems With Machine Learning: Working Mechanism on Environment Impact Traffic Optimisation
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Author(s): Ashima Jain (Manipal University Jaipur, India), Bhupinder Singh (Sharda University, India)and Anjali Raghav (Sharda University, India)
Copyright: 2025
Pages: 20
Source title:
Machine Learning and Robotics in Urban Planning and Management
Source Author(s)/Editor(s): Kamalesh Ravesangar (Tunku Abdul Rahman University of Management and Technology, Malaysia), Christian Kaunert (Dublin City University, Ireland & University of South Wales, UK), Bhupinder Singh (Sharda University, India), Sahil Lal (Galgotias University, Greater Noida, India)and Manmeet Kaur Arora (Sharda University, India)
DOI: 10.4018/979-8-3693-9410-6.ch010
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Abstract
Traffic congestion has turned into a vital urban challenge throughout the world because it generates notable economic strain and damage to the environment. The rising automobile usage coupled with expanding metropolitan populations has made traffic congestion more severe and regular. A review of the analysis investigates the monetary as well as ecological consequences stemming from urban traffic congestion while presenting possible solutions to reduce its harmful effects. The chapter discusses established traffic management solutions together with contemporary smart parking systems along with accident detection technologies. The research shows that road transportation must become actively involved in congestion management through systematic methods to recognize congestion causes while implementing directed solutions as well as monitoring systems for tracking traffic flow.
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