DEMAND FORECASTING IN TRANSPORTATION NETWORKS USING DEEP LEARNING

Abstract

Accurate demand forecasting in transportation networks is essential for efficient resource allocation, effective traffic management, and sustainable urban planning. With the continuous growth of urban populations and increasing vehicle usage, transportation systems generate vast amounts of complex and dynamic data. Traditional statistical models, such as linear regression and ARIMA, often struggle to capture nonlinear relationships and sudden fluctuations in traffic flow and passenger demand. These limitations reduce their effectiveness in real-time forecasting, especially in large-scale metropolitan networks where demand patterns are highly variable and influenced by multiple interacting factors. To overcome these challenges, this study proposes a deep learning-based framework for transportation demand prediction. Specifically, Long Short-Term Memory (LSTM) networks are employed to capture temporal dependencies in sequential traffic data, as they are capable of learning long-term patterns and handling time-series dynamics effectively. In addition, Convolutional Neural Networks (CNNs) are utilized to extract spatial features from geographically distributed traffic and GPS data. By integrating both temporal and spatial learning mechanisms, the proposed model can better understand complex traffic behaviors and passenger movement trends across different regions of the network. Experimental results indicate that deep learning models significantly outperform conventional time-series approaches in both short-term and long-term forecasting tasks. The hybrid CNN-LSTM architecture demonstrates improved predictive accuracy, lower error rates, and greater robustness in handling peak-hour demand variations. These findings highlight the practical value of deep learning in intelligent transportation systems, enabling transit authorities to optimize routes, reduce congestion, improve service reliability, and enhance overall commuter satisfaction.