Development of an Advanced Traffic Demand Prediction System Optimized Three-Phase Deep Neural Network

Predicting traffic demand is essential to current ITS because it helps reduce road traffic congestion and improves utilisation. Efficient traffic prediction models can contribute to a considerably decreased number of disruptions and optimal control of the traffic within the city. This paper proposes an improved three-phase deep neural network (DNN) model for traffic demand forecast at the macro level. The proposed model is an addition to the existing methods using a standalone multi layered neural network architecture and optimisation algorithms that improve the predictive abilities and reduce computational time. The three-phase design now involves feature extraction, a deep learning model phase, and an optimisation phase to adjust the parameters within the model further. The identified originality of this approach is in integrating all these phases while enhancing the interpreta tion of traffic patterns and dynamically estimating traffic demand in the future, informed by prior and present traffic data. To show how the proposed model per forms compared with standard predictive models, the performance assessment of the study employs a traffic dataset available to the public. The studies used to eval uate the proposed framework demonstrate that the three-phase DNN model opti mised for deep architectures is also more accurate, less sensitive to degradation, and possesses better scalability. Research proves that the elaborated approach might help enhance traffic control in large cities, thus providing more competent decisions and accurate planning of resources. Furthermore, the proposed method ology can be generalised for other domains where it is necessary to forecast the values in time-series data.