Applications of Measurement Error Correction Approaches in Statistical Road Safety Modeling

Abstract

Road safety modelers frequently use average annual daily traffic (AADT) as a measure of exposure in regression models of expected crash frequency for road segments and intersections. Recorded AADT values at most locations are estimated by state and local transportation agencies with significant uncertainty, often by extrapolating short-term traffic counts over time and space. This uncertainty in the traffic volume estimates, often termed in a modeling context as measurement error in right-hand-side variables, can have serious effects on model estimation, including: 1) biased regression coefficient estimates; and 2) increases in dispersion. The structure and magnitude of measurement error in AADT estimates are not clearly understood by researchers or practitioners, leading to difficulties in explicitly accounting for this error in statistical road safety models, and ultimately in finding solutions for its correction. This study explores the impacts of measurement error in traffic volume estimates on statistical road safety models by employing measurement error correction approaches, including regression calibration and simulation extrapolation. The concept is demonstrated using crash, traffic, and roadway data from rural, two-lane horizontal curves in the State of Washington. The overall results show that the regression coefficient estimates with a positive coefficient were larger and those with a negative coefficient were smaller (i.e., more negative) when the measurement error correction methods were applied to the regression models of expected crash frequency. Future directions in applications of measurement error correction approaches to road safety research are provided.

Method for Estimating the Monetary Benefit of Improving Annual Average Daily Traffic Accuracy in the Context of Road Safety Network Screening

“…Following the same approach adopted by Musunuru and Porter, in this module, the AADT for each site is modified to include the measurement error ( ϵ ) given the measurement error variance σ 2 as follows ( 27 ):…”

“…The MENB method outperforms (i.e., better goodness-of-fit results) the conventional negative binomial approach in the case of large AADT measurement errors. In a more recent study, Musunuru and Porter simulated AADT estimates considering a normally distributed measurement error and compared the corresponding SPF models with the SPF model developed on the true AADT values ( 27 ). They found that, when measurement error is not included in road safety regression modeling, regression coefficient estimates are biased toward zero.…”