Data-Driven Urban Traffic Flow Prediction and Optimization of Intelligent Transportation Systems in Hong Kong

This article delves into the optimization of intelligent transportation systems, specifically focusing on the practical impact of a deep learning-based traffic flow forecasting model. The introduction highlights the severity of urban congestion, emphasizes the significance of intelligent systems, and outlines the study's objectives. To this end, the LSTM model is employed for traffic flow prediction, accompanied by a comprehensive experimental design aimed at validating both model accuracy and strategy effectiveness. Throughout the experimentation, extensive traffic data is gathered, the model is refined, and its predictions are integrated into various intelligent transportation aspects, including signal control and route planning. Findings reveal the LSTM model's strong performance in traffic flow forecasting, characterized by high accuracy. At the same time, the optimization strategy based on the prediction results has also achieved remarkable results, effectively reducing traffic congestion. This result proves the great potential of deep learning in intelligent transportation system optimization. This study not only has theoretical value, but also provides useful reference for practical application.