Citation

Anastasopoulos PC, Mannering FL, Shankar VN, Haddock JE. Accid. Anal. Prev. 2012; 45: 628-633.

Affiliation

Visiting Assistant Professor, School of Civil Engineering, Indiana Local Technical Assistance Program, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907-2051, USA.

Copyright

(Copyright © 2012, Elsevier Publishing)

DOI

10.1016/j.aap.2011.09.015

PMID

22269550

Abstract

A large body of previous literature has used a variety of count-data modeling techniques to study factors that affect the frequency of highway accidents over some time period on roadway segments of a specified length. An alternative approach to this problem views vehicle accident rates (accidents per mile driven) directly instead of their frequencies. Viewing the problem as continuous data instead of count data creates a problem in that roadway segments that do not have any observed accidents over the identified time period create continuous data that are left-censored at zero. Past research has appropriately applied a tobit regression model to address this censoring problem, but this research has been limited in accounting for unobserved heterogeneity because it has been assumed that the parameter estimates are fixed over roadway-segment observations. Using 9-year data from urban interstates in Indiana, this paper employs a random-parameters tobit regression to account for unobserved heterogeneity in the study of motor-vehicle accident rates. The empirical results show that the random-parameters tobit model outperforms its fixed-parameters counterpart and has the potential to provide a fuller understanding of the factors determining accident rates on specific roadway segments.

Language: en