Improving short‐term traffic forecasts: to combine models or not to combine?

Tselentis, Dimitrios I.; Vlahogianni, Eleni I.; Karlaftis, Matthew G.

This study compares the performance of statistical and Bayesian combination models with classical single time series models for short‐term traffic forecasting. Combinations are based on fractionally integrated autoregressive time series models of travel speed with exogenous variables that consider speed's spatio‐temporal evolution, and volume and weather conditions. Several statistical hypotheses on the effectiveness of combinations compared to the single models are also tested. Results show that, in the specific application, linear regression combination techniques may provide more accurate forecasts than Bayesian combination models. Moreover, combining models with different degrees of spatio‐temporal complexity and exogeneities is most likely to be the best choice in terms of accuracy. Moreover, the risk of combining forecasts is lower than the risk of choosing a single model with increased spatio‐temporal complexity.

TRAFFIC estimation; AUTOREGRESSIVE models; PREDICTION theory; TIME series analysis; MODEL theory

IET Intelligent Transport Systems (Wiley-Blackwell), 2015, Vol 9, Issue 2, p193

1751-956X

Academic Journal

10.1049/iet-its.2013.0191