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Introduction: Driver distraction and inattention (DDI) are major causes of road crashes, especially on rural highways. However, not all instances of distracted or inattentive driving lead to crashes. Previous studies indicate that DDI-related driving behavior is closely associated with low-traffic and less complex driving environments. Nevertheless, it is unclear if these traffic or road environments also increase the likelihood of crashes involving DDI., Method: This study employed machine learning algorithms to identify the factors contributing to DDI-involved crashes on rural highways. This study applied multiple machine learning models including the Light Gradient Boosting Model (LGBM), Random Forest (RF), and Neural Network (NN) to quantify the correlations of DDI-involved crashes related to road and traffic environments. The study leveraged a statewide crash database with unique roadway data that contains variables for median type (e.g., 4-ft flush medians) and roadside access point density. To deal with the extreme imbalance of data, two sampling methods (over and under-sampling) were used to balance the data for machine learning., Results: Modeling results indicated that the road and traffic environments that are strongly linked to DDI-involved crashes in general overlap with the environments that lead to DDI-related driving behavior, except for the truck volumes in traffic. Crashes that involved DDI were more likely to occur in environments with non-traversable medians (compared to 4-ft flush medians), lower-volume traffic, and greater access spacing on roadsides. With regard to truck volumes, a non-linear relationship with the occurrence of DDI-involved crashes was uncovered. Traffic with about 8 to 10% of trucks is associated with the highest likelihood of DDI-involved crashes., Practical Applications: This study provides valuable information for drivers who need to be careful while driving in certain environments with a risk of DDI-involved crashes and for agencies who need to take actions to address the issue of DDI under such environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

This exploratory study is a follow-up to a 2014 study that investigated factors associated with large truck at-fault crash outcomes in Alabama. To assess unobserved temporal changes in the effects of the crash factors, this study re-creates the original crash models developed in the 2014 study using crash data from 2017 to 2019. Four mixed logit models were re-created using the same variables used in the previous study to analyze contributing crash factors to injury severity of single-vehicle (SV) and multi-vehicle-involved (MV) large truck at-fault crashes in urban and rural settings. It was found that there have been temporal changes in how many of the factors influenced crash severity with some of them no longer showing any significant association with crash outcomes, while others remained significant. Further, it was observed that some of the variables that remained significant had different relationships with crash injury severity in the newer severity models. For instance, while factors such as fatigued driver (in rural crashes), clear weather (in urban crashes), single-unit truck (in rural SV crashes), truck rollover (in urban SV crashes) maintained consistent significance over time, the effects of variables such as at-fault male drivers (in urban MV crashes), at-fault female drivers (in urban MV crashes), and hitting fixed object (in rural MV crashes) have changed. One such notable difference is the variable for absence of traffic control which increased the probability of major injury in rural SV crashes by 49.50% in the 2014 model but decreased the probability of recording major injuries by 108.90% using the 2017-2019 data. Considering the temporal changes that were observed in the recreated models, newer models were developed, revealing the emergence of new variables such as truck age that are significantly associated with truck crash severity. The findings of this study provide evidence to suggest that some crash severity factors for at-fault large truck collisions vary over time, with newer ones also emerging over time. These findings can also help trucking companies, transportation engineers, and other industry experts in developing measures to reduce large truck crashes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)