Evaluation of smart city paved the way for creating smart transport systems. Smart cities focused on smart traffic management platforms. Routing the vehicles appropriately without affecting the vehicle speed and utilization points. Dynamic traffic management is getting attracted nowadays to ensure the smart vehicle drivers to get routed without getting further delay or traffic wait time. The updates are provided during the run time. Smart congestion management system uses a set of learning model in which the global dataset is utilized. In the proposed system, a real time datasets are collected from publicly available websites named KAGGLE. The dataset consists of traffic data collected from four junctions. The proposed model modified the existing dataset with the information of emergency vehicle to create novelty. The dataset holds the random distribution of emergency vehicle data combined with the existing traffic data. The proposed Deep cross neural network with the preprocessing analysis using Linear discriminated analysis (LDA) model for improved prediction. The model achieves optimized routing strategy and improved performance, with less error rate.

Keywords : Smart Traffic Congestion, Optimized Routing, Deep Neural Networks, Linear Analysis, Internet Of Things, Routing Algorithms, High Speed Networks