Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities.

Area of Science:

• Intelligent Transportation Systems
• Smart City Technologies
• Urban Planning

Background:

• Rapid urbanization and vehicle growth cause significant traffic congestion, pollution, and logistical delays in metropolitan areas.
• Existing traffic management systems struggle with efficiency, leading to congestion, delays, and high accident rates.
• Internet of Things (IoT) and Machine Learning (ML) offer innovative solutions for automated and intelligent management systems.

Purpose of the Study:

• To design and implement an Adaptive Traffic-management (ATM) system leveraging IoT and ML.
• To address challenges in current transport management, aiming to reduce congestion, delays, and accidents.
• To create a dynamic system that optimizes traffic flow based on real-time data.

Main Methods:

• Developed an ATM system integrating IoT sensors and ML algorithms.
• Designed the system around three core entities: vehicle, infrastructure, and events.
• Utilized DBSCAN clustering for anomaly detection and adaptive traffic signal control based on traffic volume and movement predictions.

Main Results:

• The proposed ATM system significantly outperformed conventional traffic management strategies.
• Demonstrated substantial reductions in vehicle waiting times and overall traffic congestion.
• Showcased a decrease in road accidents and an improved urban journey experience.

Conclusions:

• The ATM system, powered by IoT and ML, offers a superior approach to traffic management.
• This adaptive model is a promising solution for future transportation planning in smart cities.
• The system effectively minimizes delays, congestion, and accidents, enhancing urban mobility.