Implementing Real-Time Traffic Flow Prediction Using LSTM Networks for Urban Mobility Optimization

Abstract

The rapid urbanization and subsequent traffic congestion are challenging public transportation systems that are responsible for the mobility of populations living in such cities. It is challenging to achieve an accurate and reliable real-time traffic forecasting using existing models such as statistical methods and traditional machine learning methods, because of the dynamic nature, temporal dependency, and spatial heterogeneity of traffic flow. To overcome these shortcomings, this paper presents a real-time traffic flow forecasting model based on LSTM. Long short-term memory (LSTM), a kind of form RNN, has the great capacity to capture the time sequence and abrogate correlation dependency, which is well consistent with traffic flow-pattern behaviours. That being said, the model predicts the traffic flow in a short-term using historical data of volume, speed and condition on road. The study also utilizes various types of data — including GPS data from vehicles, sensors and traffic cameras — to enhance predictive accuracy. Extensive experiments using real-world traffic datasets show that the proposed LSTM-based model significantly outperforms traditional machine learning models, such as ARIMA and Support Vector Machines (SVMs), in terms of both prediction accuracy and response time. The results demonstrate that the model could achieve an accuracy of more than 90% in predicting complaints, suggesting it is a successful approach to urban traffic management systems which can lessen congestion and improve mobility. The solution offers up-to-the-minute traffic flow predictions, enabling real-time route planning, traffic signal optimization and premptive congestion control. The research concludes that by using this predictive type of LSTM networks, it can full-fill a great role in solving the urban mobility problem because if we could have more accurate traffic prediction based on historical data such as training samples so it might turn into significant improvements on decreasing travel delays and enhancing overall traffic efficiency so that's hence why it is a scalable component for city projects.