Specification and Estimation of a Spatially and Temporally Autocorrelated Seemingly Unrelated Regression Model: Application to Crash Rates in China

Many variables relate in transportation studies and other regional analyses. These variables are often influenced by similar factors and have correlated latent terms. These variables can be correlated in their regression error terms, using a seemingly unrelated regression (SUR) model. The SUR model proposed in this study considers potential temporal and spatial autocorrelations across observations, making the model behaviorally convincing and applicable to circumstances where a three-dimensional correlation exists, across time, space, and equations. An example of crash rates in Chinese cities is used. The results show that incorporation of spatial and temporal effects significantly improves the model. Moreover, investment in transportation infrastructure is estimated to have statistically significant effects on reducing severe crash rates, but with an elasticity of only −0.078. It is also observed that, while vehicle ownership is associated with higher per capita crash rates, elasticities for severe and non-severe crashes are just 0.13 and 0.18, respectively.

  • Availability:
  • Authors:
    • Wang, Xiaokun
    • Kockelman, Kara M
  • Publication Date: 2007-5


  • English

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  • Accession Number: 01052017
  • Record Type: Publication
  • Files: TRIS, ATRI
  • Created Date: Jun 22 2007 3:38PM