Intelligent Traffic Management and Accident Prediction Using IoT and Deep Learning

Abstract

- Rapid urbanization, increasing vehicle ownership, and the growing complexity of urban transportation networks have intensified challenges related to traffic congestion, travel delays, fuel consumption, environmental pollution, and road traffic accidents. Conventional traffic management systems, which primarily rely on fixed-time traffic signal control and manual monitoring, often fail to respond effectively to dynamic traffic conditions and accident-prone situations. The integration of the Internet of Things (IoT) with deep learning has emerged as a promising approach for developing intelligent transportation systems capable of real-time monitoring, adaptive traffic control, and proactive accident prediction. This paper proposes an Intelligent Traffic Management and Accident Prediction Framework Using IoT and Deep Learning that integrates heterogeneous IoT devices, edge computing, cloud computing, and hybrid deep learning models to improve traffic efficiency and road safety. The proposed framework employs smart cameras, Global Positioning System (GPS) devices, roadside units, Radio Frequency Identification (RFID) sensors, connected vehicles, and environmental sensors to collect real-time traffic data. Data are preprocessed at the edge to reduce latency before being transmitted to cloud platforms for large-scale storage and deep learning analysis. A hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is adopted to capture both spatial and temporal traffic characteristics for congestion forecasting and accident risk prediction. The framework supports adaptive traffic signal control, intelligent route optimization, and early warning generation for traffic management authorities and road users. A comprehensive review of recent studies demonstrates that integrating IoT with deep learning significantly improves prediction accuracy, decision-making speed, and transportation efficiency compared with conventional approaches. The research also identifies key implementation challenges, including data heterogeneity, cyber security, privacy preservation, model explain ability, and scalability. The proposed research framework provides a scalable and intelligent solution for next-generation smart transportation systems and offers a foundation for future research on explainable artificial intelligence, federated learning, digital twins, and autonomous connected vehicles within intelligent transportation environments.