Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model

摘要

This paper introduces a novel model—Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) methodology—into the field of short-term traffic flow forecasting in urban network. Compared to traditional STARIMA, GSTARIMA is a more flexible model class where parameters are designed to vary per location. Having proposed the model, a forecasting experiment based on actual traffic flow data in urban network in Beijing, China is constructed to verify the practicability of GSTARIMA model. After analysis and comparison with the traditional STARIMA model, the prediction results prove meritorious and the application of GSTARIMA improves the performance of urban network modeling.