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Erschienen in:
Optimization of Network Mapping for Screening and Intrusion Sensing Devices Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Secure Design and Implementation of Smart Traffic Light Management System

verfasst von : Anju Shukla, Varun Shukla, Shishir Kumar, Akshat Anand

Erschienen in: Cryptology and Network Security with Machine Learning

Verlag: Springer Nature Singapore

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Abstract

By identifying areas of heavy congestion, smart traffic management systems can then implement measures to alleviate that problem. In order to accomplish this, it analyzes and synchronizes data from sensors in real-time. The input data are used to determine when the traffic signal blinks due to congestion. This method has the potential to lessen air pollution and make roads safer for everyone. In this research, we offer a smart technique for accurate traffic estimate prediction using random forest. In the majority of instances, the approximate traffic estimate is better than the shortest route with respect to overall travel time and fuel cost. Based on our findings, we were able to decrease the average waiting time of vehicles while still accurately forecasting traffic.

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Vorheriges Kapitel Real-Time Authenticated and Secure Monitoring and Control of Low-Cost Sustainable Hydroponic System in Cloud
Nächstes Kapitel Investigation of Sinkhole Attacks and Network Simulation on 6LoWPAN
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Metadaten
Titel
Secure Design and Implementation of Smart Traffic Light Management System
verfasst von
Anju Shukla
Varun Shukla
Shishir Kumar
Akshat Anand
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Cryptology and Network Security with Machine Learning

Print ISBN: 978-981-9706-40-2

Electronic ISBN: 978-981-9706-41-9

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-0641-9_5

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