Advanced Traffic Flow Prediction Using LSTM Networks and Big Data Integration for Urban Mobility Optimization

As urbanization expands at a rate faster than ever before we experience traffic jams which call for sophisticated predictive methods in order to improve the traffic systems. Hypothesized from the heuristic analysis of big traffic data, the present paper outlines a new traffic flow prediction model. The proposed methodology uses big real-time traffic data and historical big data with a machine learning approach that can detect and analyse temporal and spatial dependencies. The model incorporates DL concepts like Long Short-Term Memory (LSTM) networks which capture the sequential dependencies and the complex correlation in traffic flows. Moreover, we also have Feature Selection mechanism to filter the traffic data indicators and thus decrease the data dimensions and increase the prediction accuracy. The integration of big data analytics is helpful in real-time adjustments to changes in traffic and is more accurate. By testing the method on different traffic datasets, the authors show that the proposed method outperforms the traditional models. The findings of this research are novel and significant because this framework can be easily applied and implemented for the traffic flow prediction across various cities, helping in management of traffic and thereby limit traffic congested areas.