Modeling motorcycle crash frequency on rural multilane segments in Kentucky.

Despite motorcycle crashes accounting for a large percentage of traffic fatalities in the U.S., studies investigating motorcycle crash frequency are relatively limited. This study took the initiative and developed motorcycle crash-specific safety performance functions (SPFs) along rural multilane road segments in Kentucky, separately for the pre-COVID-19 pandemic (2015-2019) and post-COVID-19 pandemic (2020-2022) periods. Eight years of motorcycle crash records (2015 through 2022) and site-specific characteristics (e.g., shoulder width and annual average daily traffic "AADT") were collected and used. Conway-Maxwell-Poisson (CMP) and heterogeneous Conway-Maxwell-Poisson (HTCMP) models were fitted and compared while accounting for motorcycle crash under-dispersion (i.e., when crash variance is less than its mean). The study results showed that, for both pre- and post-pandemic periods, the HTCMP models outperformed their CMP counterparts based on various goodness-of-fit measures (e.g., likelihood ratio test "LRT", Akaike information criterion "AIC", and McFadden pseudo R-squared) and prediction performance measures (i.e., mean absolute deviance "MAD" and mean square prediction error "MSPE"). From the developed SPFs, for the pre-pandemic period, the presence of horizontal curves and undivided roadways were significantly associated with increased motorcycle crash frequency along rural multilane segments, while in the post-pandemic period, wider right shoulders and higher AADT were significantly associated with increased motorcycle crash frequency. The predicted crash frequencies while applying the best-fit models (i.e., the HTCMP models) were then used to identify and rank high-crash rural multilane segments in Kentucky. Based on the study findings, several countermeasures were proposed to improve motorcyclists' safety along Kentucky's rural multilane segments, e.g., adding centerline grooved rumble strips along undivided rural multilane roadways.