This study aims to compare the effectiveness of a robust ensemble model with the state-of-the-art ONE-PEACE Large Language Model (LLM) for accurate detection of sidewalks. Accurate sidewalk detection is crucial in improving road safety and urban planning. The study evaluated the model’s performance on Cityscapes, Ade20k, and the Boston Dataset. The results showed that the ensemble model performed better than the individual models, achieving mean Intersection Over Union (mIOU) scores of 93.1%, 90.3%, and 90.6% on these datasets under ideal conditions. Additionally, the ensemble model maintained a consistent level of performance even in challenging conditions such as Salt-and-Pepper and Speckle noise, with only a gradual decrease in efficiency observed. On the other hand, the ONE-PEACE LLM performed slightly better than the ensemble model in ideal scenarios but experienced a significant decline in performance under noisy conditions. These findings demonstrate the robustness and reliability of the ensemble model, making it a valuable asset for improving urban infrastructure related to road safety and curb space management. This study contributes positively to the broader context of urban health and mobility.

Distracted driving remains a major concern on highways, with it contributing to severe and fatal crashes, particularly on high-speed routes, prompting numerous states to implement targeted initiatives aimed at combating traffic violations that significantly contribute to fatal and injury-inducing crashes. Among these initiatives is the highway safety corridor program, a collaborative endeavor between the state departments of transportation and law enforcement agencies. Highway safety corridors employ a combination of engineering interventions and heightened law enforcement presence to address risky driver behavior and mitigate the occurrence of crashes. Despite the longstanding existence of safety corridors, research on their effectiveness remains relatively limited, with existing studies indicating only moderate success rates. This study is dedicated to evaluating the effectiveness of ten highway safety corridors in Ohio, where the state recently launched its inaugural highway safety corridor program targeting distracted driving. Utilizing 2023 crash data, this Empirical Bayes’ before-and-after study seeks to gauge the impact of these safety corridors on enhancing roadway transportation safety. Upon assessing all crash types within Ohio’s distracted driving safety corridors that provided sufficient data for a before–after study, it was determined that the adoption of safety corridors generally led to a reduction in crashes ranging from 2% to 49%. The significance and magnitude of crash reduction may vary if specific crash types or severity levels are considered.

Roadways classified as state routes in both rural and urban settings are tree-lined, creating a canopy over the pavement surface. Although trees are beneficial to the environment, they pose a hazard to road safety in a variety of ways. For example, they offer reduced skid resistance due to fallen leaves; restrict direct sunlight on pavement surface, causing the formation of black ice and fog; and increase the risk of entire trees/branches/fruits falling on passing vehicles or blocking traffic lanes. State agencies have undertaken the task of eliminating tree canopies, inviting widespread criticism. Currently, there is a gap in the literature about the impact of tree canopy on traffic safety in the form of scientific research. This paper addresses this gap by presenting insight into the benefits of tree-trimming and/or removal operations along state routes in Ohio. Adopting the Empirical Bayesian approach, this study evaluates crashes to quantify the safety benefits. Additionally, a surrogate safety assessment was conducted to evaluate driver behaviors in the presence/absence of tree canopy. The results indicated that roadside tree trimming and pruning showed site-specific safety benefits at most locations, though no consistent project-level crash reduction or conclusive effects on surrogate safety measures, such as reduced speeds and hard braking, were observed.

Animal–vehicle collisions (AVCs) present serious safety, economic, and ecological concerns, particularly in areas where wildlife habitats intersect with expanding road networks. While previous studies have examined land cover or traffic volume in relation to AVCs, many relied on coarse spatial scales, treated species as a single group, and omitted key roadway or operational variables. This study addresses these gaps by investigating the spatial determinants of terrestrial AVCs across Iowa using an integrated ecological and infrastructural modeling framework. Crash data from 2020 to 2024 were analyzed using a natural language processing (NLP) model to extract animal-related incidents and infer species types from narrative reports. Land cover, road characteristics (e.g., classification, AADT, speed limits), operational measures (e.g., incident clearance time), and population data were aggregated to Iowa’s 896 census tracts (2020 boundaries). A Negative Binomial (NB) regression model identified forest coverage, population density, speed limits, and road network complexity (e.g., length of Interstates and arterials) as significant predictors of higher AVC risk, while urban land cover was negatively associated. Stepwise AIC selection and 10-fold cross-validation enhanced model performance. Spatial clustering in residuals (local Moran’s I = 0.42, p < 0.01) led to a Spatial Error Model (SEM), which further improved fit and reduced spatial bias. This tract-level framework offers a scalable, data-driven approach for transportation and wildlife agencies to identify high-risk areas and implement localized mitigation strategies, such as fencing, signage, or speed control, tailored to both ecological and infrastructural conditions.