TY - JOUR
T1 - Estimation of dynamic performance models for transportation infrastructure using panel data
AU - Chu, Chih Yuan
AU - Durango-Cohen, Pablo L.
N1 - Funding Information:
This work was partially supported by the Northwestern University Transportation Center through a Dissertation Year Fellowship award to the first author, and by the National Science Foundation through Grant 0547471 awarded to the second author. The work was done while the first author was a graduate student at Northwestern University.
PY - 2008/1
Y1 - 2008/1
N2 - We present state-space specifications of time series models as a framework to formulate dynamic performance models for transportation facilities, and to estimate them using panel data sets. The framework provides a flexible and rigorous approach to simultaneously capture the effect of serial dependence and of exogenous factors, while controlling for individual heterogeneity when pooling data across the facilities that comprise the panel. Because the information contained in time series and cross-section data are combined in the estimation, the ensuing performance models capture effects that are not identifiable in either pure time series or pure cross-section data. Also, pooling data across facilities leads to improved estimation results. To illustrate the methodology, we consider three classes of models for a panel of asphalt pavements from the AASHO Road Test. The models differ in the assumptions regarding the structure of the underlying mechanisms generating the data sequences. The results indicate that serial dependence is indeed significant, thereby reinforcing the importance of dynamic modeling. We also compare the specifications to assess the poolability of pavement condition data. The results provide evidence that heterogeneity among the facilities is present in the panel. Finally, we highlight features that elude existing performance models developed with static modeling approaches: the ability to estimate maintenance activities as exogenous variables, and the capability of updating forecasts in response to inspections.
AB - We present state-space specifications of time series models as a framework to formulate dynamic performance models for transportation facilities, and to estimate them using panel data sets. The framework provides a flexible and rigorous approach to simultaneously capture the effect of serial dependence and of exogenous factors, while controlling for individual heterogeneity when pooling data across the facilities that comprise the panel. Because the information contained in time series and cross-section data are combined in the estimation, the ensuing performance models capture effects that are not identifiable in either pure time series or pure cross-section data. Also, pooling data across facilities leads to improved estimation results. To illustrate the methodology, we consider three classes of models for a panel of asphalt pavements from the AASHO Road Test. The models differ in the assumptions regarding the structure of the underlying mechanisms generating the data sequences. The results indicate that serial dependence is indeed significant, thereby reinforcing the importance of dynamic modeling. We also compare the specifications to assess the poolability of pavement condition data. The results provide evidence that heterogeneity among the facilities is present in the panel. Finally, we highlight features that elude existing performance models developed with static modeling approaches: the ability to estimate maintenance activities as exogenous variables, and the capability of updating forecasts in response to inspections.
KW - Dynamic models
KW - Heterogeneity
KW - Infrastructure performance modeling
KW - Intervention analysis
KW - Maintenance and rehabilitation
KW - State-space models
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U2 - 10.1016/j.trb.2007.06.004
DO - 10.1016/j.trb.2007.06.004
M3 - Article
AN - SCOPUS:35348938434
SN - 0191-2615
VL - 42
SP - 57
EP - 81
JO - Transportation Research, Series B: Methodological
JF - Transportation Research, Series B: Methodological
IS - 1
ER -