A Comparison of Temporal and Spatio-Temporal Methods for Short-Term Traffic Flow Prediction

Abstract

Accurate short-term traffic flow prediction is crucial for effective urban traffic management. However, selecting the most suitable prediction model and relevant features poses a significant challenge. Moreover, predicting traffic flow on a road using only historical data, adjacent roads, or all roads in the study area can compromise the model’s accuracy or execution time. This paper tackles this challenge by proposing an enhanced Vector Auto regression VAR-based prediction method. We suggest selecting relevant roads for the model using spatio-temporal correlation analysis. Subsequently, we conduct a comparative study between our methodology’s results and those obtained from other temporal and spatio-temporal traffic forecasting methods, including historical average, K-nearest neighbors (KNN), support vector machine for regression (SVR), and autoregressive model (AR). Model performance is evaluated by considering both the impact of normal and abnormal traffic conditions, as well as the selected training days: weekdays and weekends. The study utilizes a traffic dataset collected from an area of Xuancheng city in China. The proposed enhanced VAR outperforms the other methods for short-term forecasting horizons ($\approx$ from 5 to 25 minutes), under both normal and abnormal traffic conditions.