Comprehensive Investigation of Commercial Motor Vehicle Crashes along Roadway Segments in Kentucky

Abstract

The negative binomial (NB) model, traditionally used for safety performance function (SPF) development, suffers from a fixed over-dispersion parameter and is only valid for over-dispersed data (i.e., data exhibiting greater variance than the mean). A more flexible approach that handles over-dispersed data, under-dispersed data, and excess zero counts, in addition to exhibiting varying dispersion parameter as a function of site-specific characteristics, is the zero-inflated heterogeneous Conway–Maxwell–Poisson (ZI-HTCMP) model, which is an extension of Conway–Maxwell–Poisson (CMP)-based models. This study develops fatal + injury (FI) commercial motor vehicle (CMV) crash-specific SPFs along four roadway segment facilities in Kentucky, U.S., (urban multilane, rural multilane, urban two-lane, and rural two-lane segments). The traditional NB and newly introduced CMP-based models—ZI-HTCMP, zero-inflated Conway–Maxwell–Poisson (ZI-CMP), heterogeneous Conway–Maxwell–Poisson (HTCMP), and CMP—were compared using 14,967 CMV-related crashes on Kentucky’s road segments (between 2015 and 2019) and various roadway variables, for example, shoulder width, annual average daily traffic (AADT), and heavy vehicle percentage (HVP). From the developed SPFs, AADT and HVP >10% significantly increased FI CMV-related crashes on all four segment facilities. Various goodness-of-fit (GOF) statistics, including Akaike information criterion (AIC), mean absolute deviance (MAD), and mean square prediction error (MSPE), were used for model assessment and selection. For all four roadway facilities, CMP-based models showed better model fitting and prediction performance than the NB models. Furthermore, ZI-HTCMP was the best-fit model for urban multilane segments, which had high representation of zero-crash sites. CMP-family models could be used for effectively predicting FI CMV-related crashes (with excess zeros) on road segments.