Fusing Temporal and Contextual Features for Enhanced Traffic Volume Prediction

Abstract

Time-series analysis plays a crucial role in extracting meaningful patterns from sequential data, serving to gain insights into temporal trends. Specifically, the burgeoning urbanization trend accentuates the urgency of traffic forecasting in modern cities due to its socio-economic and environmental impact. While Deep Learning techniques, notably Long Short-Term Memory (LSTM) networks, have shown promise in predicting traffic, they often overlook contextual factors like weather and holidays. To address this challenge, this paper proposes a hybrid model combining LSTM with categorical and continuous data to forecast traffic volume on the Interstate 94 American highway. Incorporating weather, temporal patterns, and holidays, this study explores the performance of a model that includes contextual factors against standalone LSTM. The results show superior predictive accuracy for the proposed hybrid model. SHapley Additive exPlanations (SHAP) analysis reveals the influence of diverse features, emphasizing the significance of contextual attributes in enhancing traffic prediction.