Title:
Smart Traffic Management System Using IoT and Machine Learning
Abstract
Background:
With the growing urban population, traffic congestion has become a significant problem in cities worldwide. Traditional traffic management systems are often inefficient, leading to delays, fuel wastage, and increased emissions. Smart technologies can be used to develop more efficient solutions for traffic control.
Objective:
The goal of this project is to design and implement a smart traffic management system that utilizes IoT sensors and machine learning algorithms to optimize traffic flow in real-time, reducing congestion and improving overall traffic efficiency.
Methods:
The system employs IoT sensors installed at key traffic intersections to collect real-time traffic data such as vehicle count, speed, and density. Machine learning algorithms are then applied to predict traffic patterns and dynamically adjust traffic signals based on real-time data. A prototype was developed and tested in a simulated environment to evaluate its performance.
Results:
The simulation results showed a 25% reduction in average vehicle wait times and a 20% improvement in traffic flow efficiency compared to traditional traffic management systems. The machine learning model demonstrated high accuracy in predicting traffic patterns, allowing for real-time adjustments that minimized congestion.
Conclusion:
The smart traffic management system effectively addresses traffic congestion by leveraging IoT and machine learning technologies. The proposed system can significantly improve traffic flow and reduce delays, making it a viable solution for future smart city infrastructures.