TrafficSense: A Machine Learning - Based Intelligent Traffic Management System
Abstract
Conventional traffic management systems mainly depend on decision-tree and rule-based methods for controlling traffic flow. These methods generally provide only moderate accuracy and are often slow in identifying traffic conditions in real time. In addition, they have limited capability to adapt to changing traffic patterns and face difficulties when processing large volumes of data. To overcome these drawbacks, an Intelligent Traffic Management System can be developed using Artificial Intelligence (AI) and Internet of Things (IoT) technologies. Sensors and cameras installed on roads continuously collect data related to traffic flow, vehicle density, and congestion levels. Based on the collected information, traffic signals can be adjusted dynamically to improve vehicle movement and reduce delays. This helps in lowering fuel consumption and minimizing air pollution caused by traffic congestion. The system can also provide signal priority for emergency vehicles such as ambulances and suggest alternate routes for other vehicles. Further improvement can be achieved by applying deep learning techniques, which offer better vehicle detection accuracy, faster processing, and effective real-time operation. These techniques also support scalability and adaptability in changing traffic environments. As a result, the proposed system can reduce congestion, improve road safety, and contribute to efficient urban transportation management.