DefinePK

Abstract

The rapid increase in urban population and vehicle numbers has led to severe traffic congestion, accidents, and fatalities, particularly due to human errors and lack of real-time traffic data. This paper proposes a novel Multi-tasking Convolutional Neural Network (MCNN) model to address key challenges in road safety and traffic management. The MCNN model detects traffic signs, assesses vehicle characteristics (e.g., position, speed, and vibration), and monitors driver behavior, including fatigue or intoxication. It leverages real-time webcam input to track patterns of traffic and vehicles attributes, and integrates embedded systems to take corrective actions, such as slowing down or stopping the vehicle when abnormal behavior is detected. Furthermore, the system can dynamically adjust traffic signal patterns based on vehicle density, enabling enhanced traffic flow and reducing congestion. By incorporating predictive analytics, the MCNN model offers early warnings for potential accidents, thereby improving road safety and reducing fatalities. This system also enables the integration of smart infrastructure with vehicles, fostering a sustainable, safe, and efficient transportation ecosystem. The efficiency of the model is demonstrated through real-time implementation, and its potential for broader urban mobility applications is discussed.