01647955

Component

https://doi.org/10.3141/2637-07

Studies investigating crash rates by roadway classification are few and far between and even more rare if extended to focus on heavy vehicles. This study explored and compared two advanced econometric methods—random-parameter Tobit regression and latent class Tobit regression—to determine contributing factors for heavy-vehicle crashes per million vehicle miles traveled while accounting for the unobserved heterogeneity present in crash data. The increasing crash rates in Idaho, crash proportion by roadway classification, and available data made an ideal case study. Empirical results show that although the random-parameter Tobit regression model provides better insight into heavy-vehicle crash rates than the fixed-parameter approach, the latent class Tobit regression model is the preferred methodology for the given data set. Traffic volumes, roadway characteristics, and traffic control devices were among the variables found to be statistically significant. Results from this study provide an alternate framework to account for heterogeneity while identifying key factors by roadway classification that influence heavy-vehicle crash rates. The illustrated framework and analysis by roadway classification can provide guidance to transportation agencies and policy makers and prompt future studies to include a latent class analysis, analysis by road classification, or both.

01628862

17-06641

English

pp 56-66

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309441650

Digital/other

Figures; References; Tables

Buses; Crash rates; Econometric models; Heavy vehicles; Highway factors in crashes; Trucks

Idaho

Highways; Motor Carriers; Safety and Human Factors

TRIS, TRB, ATRI

Sep 25 2017 10:34AM

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