The impact of highway safety regulation enforcement activities on motor vehicle fatalities

This paper analyzes the influence of highway safety regulation enforcement efforts on motor vehicle fatalities. It estimates a regression model in which such deaths depend on these efforts as well as economic conditions, driver characteristics, government regulations, and locational factors, using 1973–2000 annual Ohio data. Statistically significant results indicate that the enforcement measure of drunk driving arrests saves lives. They also suggest that highway deaths increase with the following: better economic conditions, greater alcohol consumption, larger proportions of young and old drivers, higher speed limits, and more driving on rural roads. The trend in deaths is downward.     

Predicting the distribution of households and employment: a seemingly unrelated regression model with two spatial processes

Household and employment counts (by type) are key inputs to models of travel demand and air quality. For a variety of reasons, spatial dependence is very likely present in and across these counts. In order to identify the nature of these unobserved relationships, this study provides the first application of a feasible generalized spatial 3SLS estimation procedure for a seemingly unrelated regression (SUR) model with two spatial processes. Statistical tests reveal that this more generalized model is superior to its constrained versions (e.g., SUR models without spatial components or with just a spatial lag or spatial error process).In the resulting model of Austin, Texas data, local land-use conditions offer substantial predictive power of household and job densities, and transportation access plays a role, as anticipated. The work demonstrates that SUR estimation of land-use intensities from parcel-level data with two types of spatial dependence is feasible and meaningful. Coupled with an upstream model of land-use type, this work offers key inputs for travel demand analyses, with transportation system performance feedback.