Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China

In transportation studies, variables of interest are often influenced by similar factors and have correlated latent terms (errors). In such cases, a seemingly unrelated regression (SUR) model is normally used. However, most studies ignore the potential temporal and spatial autocorrelations across observations, which may lead to inaccurate conclusions. In contrast, the SUR model proposed in this study also considers these correlations, making the model more 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; much lower than one. The techniques illustrated in this study should contribute to future studies requiring multiple equations in the presence of temporal and spatial effects.

Kara M. Kockelman (Corresponding author)Email:

---

Ms. Xiaokun Wang   is a doctoral student in the Department of Civil, Architectural and Environmental Engineering at the University of Texas at Austin. She received her B.S. and M.S. degrees at Tsinghua University, China. Her research topics range from travel demand modeling and integrated land use-transportation planning, to spatial econometrics, network analysis, and traffic safety analysis. She is a fellow of the International Road Federation. Dr. Kara Kockelman   is a Associate Professor of Civil, Architectural & Environmental Engineering and the William J. Murray Jr. Fellow at the University of Texas, Austin. She holds a PhD, MS, and BS in Civil Engineering, a Masters of City Planning, and a minor in Economics from the University of California at Berkeley. She is Chair of the Transportation Research Board’s Committee on Travel Survey Methods. Her primary research interests include the statistical modeling of urban systems (including models of travel behavior, trade, and location choice), economic impacts of transport policy, crash occurrence and consequences, and transport policy-making.