Citation

Cheng W, Gill GS, Choi S, Zhou J, Jia X, Xie M. Transportmetrica A: Transp. Sci. 2018; 14(7): 615-633.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/23249935.2017.1418458

PMID

unavailable

Abstract

There is relatively little research dedicated to the evaluation of different temporal treatments on modelling performance. This study proposed two new methods which combined the strengths of linear trend and time-varying coefficients with the autoregressive process and compared their performance with seven other temporal models used in the past. All models generated a similar number of statistically significant variables and close variable coefficients, but different modelling performance. For prediction accuracy, the model which accounts only for autoregressive effect illustrated superior performance in terms of cross-validation and typical assessment, which was based on same data used to develop models. Nonetheless, if the penalized criterion was used, both proposed models outperformed other competing models, indicating their capability to yield similar prediction accuracy with relatively smaller effective number of parameters. This suggests further exploration of models that combine various temporal treatments. Finally, the correlations were also observed among the various modelling assessment criteria.

Language: en

Keywords

autoregressive process; Bayesian hierarchical approach; Serial correlations; structural heterogeneities; time-varying coefficients