Traffic Flow Prediction using LSTM Networks: A Deep Learning Approach for Real-Time Traffic Forecasting

Abstract

Accurate traffic flow prediction is essential for efficient traffic management and optimization in urban areas. In this paper, we propose a deep learning-based approach using Long Short-Term Memory (LSTM) networks to predict real-time traffic flow. LSTM networks are well-suited for modeling sequential data, such as traffic flow, due to their ability to capture long-term dependencies and temporal patterns in time-series data. The proposed model leverages historical traffic data from sensors placed on roads to forecast future traffic conditions, providing valuable insights for real-time traffic control and planning. We focus on the effectiveness of LSTM networks in overcoming challenges like non-linearity, irregularity, and high variability typically observed in traffic data. The performance of the LSTM model is evaluated against traditional time-series forecasting methods, such as ARIMA and simple regression models. Experimental results demonstrate that the LSTM-based approach significantly outperforms other methods in terms of prediction accuracy and robustness. This paper also discusses the impact of various hyperparameters, including the number of layers, batch size, and learning rate, on model performance. The proposed system can be integrated into Intelligent Transportation Systems (ITS) to enhance traffic prediction accuracy, optimize traffic signal timings, and mitigate congestion, ultimately improving urban mobility and reducing environmental impacts.