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Machine learning based IoT system for secure traffic management and accident detection in smart cities.

Saravana Balaji Balasubramanian1, Prasanalakshmi Balaji2, Asmaa Munshi3

  • 1Department of Information Technology, Lebanese French University, Erbil, Iraq.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary

This study introduces an Adaptive Traffic Management (ATM) system with an accident alert sound system (AALS) and secure data transmission (SEE-TREND) to reduce congestion and accidents in smart cities. The ATM system significantly outperforms traditional methods, improving traffic flow and safety.

Keywords:
Accident detectionAdaptive traffic management systemDeep learningInternet of ThingsSecure early traffic-related EveNt detectionSmart citiesTraffic management system

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Area of Science:

  • Intelligent Transportation Systems
  • Smart City Technologies
  • Machine Learning Applications

Background:

  • Rapid urbanization and increased vehicle numbers in smart cities lead to severe traffic congestion, pollution, and accidents.
  • Existing traffic management systems struggle to efficiently handle dynamic traffic conditions and ensure data security.
  • Road accidents result in significant fatalities and long-term impairments annually.

Purpose of the Study:

  • To develop and evaluate an Adaptive Traffic Management (ATM) system integrated with an Accident Alert Sound System (AALS) and secure data transmission.
  • To mitigate traffic congestion, enhance road safety, and ensure secure data transmission in smart city environments.
  • To improve the overall efficiency and experience of urban transportation systems through intelligent solutions.

Main Methods:

  • Implementation of an IoT-based Traffic Management System utilizing sensors in autonomous vehicles and intelligent devices.
  • Development of an Adaptive Traffic Management (ATM) model that dynamically adjusts traffic signal timings based on real-time traffic volume and predicted movements.
  • Integration of a Secure Early Traffic-Related EveNt Detection (SEE-TREND) protocol for secure data transmission and accident detection.

Main Results:

  • The proposed ATM system demonstrated significant improvements in reducing travel time and alleviating traffic congestion compared to traditional methods.
  • The system effectively manages traffic flow by continuously modifying signal timings for seamless transitions.
  • The integrated AALS and SEE-TREND components enhance accident detection and ensure secure data transmission, contributing to a safer travel experience.

Conclusions:

  • The Adaptive Traffic Management (ATM) system, combined with AALS and SEE-TREND, offers a superior solution for smart city transportation planning.
  • This innovative approach significantly decreases traffic jams, reduces vehicle wait times, and lowers accident rates.
  • The system enhances the overall urban travel experience, paving the way for more efficient and secure smart city mobility.