Investigating the Effect of Microscopic Real-Time Weather Data on Commercial Motor Vehicle Crash Injury Severity in Kentucky

Abstract

This study comprehensively investigated the factors affecting crash injury severity associated with commercial motor vehicles (CMVs), that is, large trucks and buses, on Interstate-65 (I-65) in the state of Kentucky. Unconventionally explored microscopic real-time weather variables (i.e., air temperature, relative humidity, solar radiation, wind speed, and precipitation) were extracted from 80 mesonet stations in Kentucky and used in the analysis. Other variables explored included hourly traffic volume and speed and driver-, roadway-, vehicle-, and environment-related predictors. Recent crashes within a 5-year, 4-month period (January 1, 2016, through April 30, 2021) along I-65 were used. The association rules mining (ARM) technique was used to uncover associations between real-time weather and CMV-related severe injuries (KA). The ARM analysis showed that severe CMV-related crashes were associated with the co-occurrence of the following real-time weather conditions: solar radiation ≤ 5 W/m², relative humidity 65% to 90%, and air temperature ≤ 50°F. Furthermore, the correlated mixed logit with heterogeneity in means (CMXLHM) model was applied to identify significant factors affecting CMV-related crash severity while accounting for unobserved heterogeneity and correlations among the random parameters. The CMXLHM model results showed that solar radiation ≤ 5 W/m² and air temperature ≤ 50°F increased the probability of severe CMV crash injury outcome by 46% and 26.89%, respectively. Furthermore, middle-age drivers (31 to 59 years old), speeding, distracted driving, and weekend-related crashes were associated with increased CMV crash injury outcomes. Safety recommendations are proposed to enhance CMV safety. One example is feeding specific real-time weather states to dynamic message signs for safety alerts.