This article presents an enhanced Internet of Things-based vehicle management system for traffic law enforcement to improve road safety and compliance with the law. The designed system will integrate real-time data collection from various IoT devices such as cameras, speed sensors, and RFID readers and incorporate complex machine learning algorithms to detect offences on the road and control the traffic patterns accordingly. In a three-month prototype application in a small metropolitan area, the system demonstrated a traffic violation detection rate of 92.3% - including speeding and red-light running - with a reduction of false positives compared to traditional methods of 28%. Upon putting the system to use, releases in traffic violations were cut by 30%, and the punctuality of the traffic flow during rush hour was given a 15% boost with evidence of the reduction of average waiting time at major junctions. Furthermore, it enhanced the response time via technology, with responders achieving an 18% cut in the average response time of emergency vehicles compared to the original method. Nevertheless, some problems were observed: first, high dependence on the Internet - the data processing of objectives was slowed down by 12% of regions with a weak Internet connection. In addition, 14 % of users reported concerns about data privacy, showing the necessity of taking steps to improve data protection. The results reveal that while the IoT -based vehicular control system yields benefits for enforcing traffic laws, key challenges relating to connectivity, the privacy of information, and system adaptability must be addressed if it is to be first applied and then effectively implemented in extensive urban environments.


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    Title :

    Revolutionizing Traffic Law Enforcement: IoT-Enabled Vehicle Control Systems for Smart Cities


    Contributors:


    Publication date :

    2024-12-07


    Size :

    505680 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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