Integrated Smart Accident Management System for Urban Traffic Congestion Alleviation and Improved Road Safety

Abstract

Global urbanization and increased population mobility have led to a surge in vehicular traffic, posing challenges in effective traffic management, and resulting in congestion, accidents, and pollution. Despite advancements, accidents persist as a major cause of mortality, highlighting the need for a unified accident management system. Real-time route planning offers promise but faces challenges in accounting for diverse driver preferences. This proposed technique leverages smart technology to enable real-time communication among vehicles, ambulances, hospitals, roadside units, and central servers, optimizing road network utilization and reducing vehicle congestion. This paper introduces an accident detection system utilizing fuzzy logic to identify accidents, enhancing traffic flow management, thereby improving road safety and potentially saving lives.

Motivation: The motivation behind this work stems from the urgent need to address the escalating challenges posed by increasing vehicular traffic in urban areas. With the expansion of cities and rising population mobility, traffic congestion, accidents, and pollution have become significant issues. Despite advancements in traffic management systems and vehicle technologies, road accidents continue to be a leading cause of mortality. Hence, there is a critical need for an integrated accident management system that can effectively manage road traffic and enhance road safety.

Novelty: The novelty of this work lies in proposing an innovative approach to accident management and traffic flow optimization using smart technology and fuzzy logic. While previous studies have explored various techniques for automatic accident detection, the integration of fuzzy logic into the accident detection system represents a novel approach. Fuzzy logic allows for the incorporation of linguistic variables, membership functions, and rule sets, enabling more nuanced decision-making in real-time traffic management. Additionally, the proposed system facilitates communication among vehicles, ambulances, hospitals, roadside units, and central servers, creating a comprehensive framework for accident management and traffic flow optimization.

Findings: Through the implementation of the proposed accident management system, the study finds significant improvements in road safety and traffic congestion mitigation. By leveraging real-time communication and fuzzy logic-based decision-making, the system effectively detects accidents along roads and facilitates traffic flow optimization. The findings demonstrate the potential of smart technology and fuzzy logic in enhancing road safety, reducing travel costs, and potentially saving lives. Moreover, the study highlights the importance of integrating advanced technologies into traffic management systems to address the challenges posed by urban traffic congestion and road accidents effectively.