01624621

Component

This study reviews different modelling approaches used in panel data for performance forecast of transportation infrastructure and the panel data models (PDMs) are highlighted for longitudinal data set. In what follows, state-space specifications of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and estimated using panel data sets. The models could simultaneously capture the heterogeneity and update forecast through inspection. To depict this methodology in detail, three classes of dynamic PDMs are presented in four instances compared with two classes of static PDMs for a panel of composite pavement sections in one of China’s airports. In addition, estimation results obtained by ordinary least square (OLS) estimator and system generalized method of moments (SGMM) are compared for two dynamic instances. The results show that the average root mean square errors of dynamic specifications are all significantly lower than the ones of static counterparts as prediction continues over time and no significant difference of prediction accuracy can be seen between state-space model and curve shifting model in a short time period. In addition, SGMM does not obtain higher prediction accuracy than OLS in this case. And inspection intervals are suggested to be specified as several constants with integer multiple relations.

This paper was sponsored by TRB committee AFD10 Standing Committee on Pavement Management Systems.

01618707

17-03517

English

21p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Airport runways; Data files; Dynamic models; Mathematical prediction; Pavement performance

State-space methods

China

Aviation; Pavements

Transportation Research Board Annual Meeting 2017 Paper #17-03517

TRIS, TRB, ATRI

Dec 8 2016 11:20AM