Enhanced Framework for Real-Time Vehicle Detection and Tracking

Urban traffic congestion is a major challenge in modern cities, leading to increased travel times, fuel consumption, and air pollution. To address this issue, DeepTraffic-VTS presents an intelligent hybrid system that integrates Long Short-Term Memory (LSTM) networks for time series forecasting with YOLO (You Only Look Once) for vehicle detection. The LSTM model analyzes historical traffic data—such as vehicle count, average speed, and congestion levels—to predict traffic conditions over upcoming time intervals. Simultaneously, YOLO processes live video feeds to detect and classify vehicle types on the road, including two-wheelers, cars, buses, and emergency vehicles. Based on both predicted and real-time traffic conditions, the system provides adaptive suggestions on the most suitable vehicle types for efficient navigation—for example, recommending two-wheelers in high-congestion zones due to their maneuverability, while advising larger vehicles to reroute or delay travel. This combined approach enables more effective traffic management, emergency response optimization, and smart urban mobility planning. Based on both predicted and real-time traffic conditions, the system provides adaptive suggestions on the most suitable vehicle types for efficient navigation. Specifically, when congestion levels are high, the system recommends small vehicles such as two-wheelers due to their maneuverability; for medium congestion, it suggests medium-sized vehicles like cars; and for low congestion, it supports the use of larger vehicles such as buses and trucks for efficient mass transportation. In addition, emergency vehicles like ambulances and fire trucks can be given priority-based navigation routes, reducing response times in critical situations.