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

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

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

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Author(s): Dimitrios I. Tselentis ¹ ; Eleni I. Vlahogianni ² ; Matthew G. Karlaftis ²
- Affiliations: 1: Civil Engineering and Built Environment, Science and Engineering Faculty, Queensland University of Technology, 2 George Street, GPO Box 2434, Brisbane, QLD 4001, Australia;
  2: National Technical University of Athens, 5 Iroon Polytechniou Str, Zografou Campus, 157 73, Athens, Greece
Source: Volume 9, Issue 2, March 2015, p. 193 – 201
DOI: 10.1049/iet-its.2013.0191 , Print ISSN 1751-956X, Online ISSN 1751-9578

Received 18/11/2013, Accepted 04/06/2014, Revised 05/05/2014, Published 22/07/2014

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.

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

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