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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
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.
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.
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