Rainy weather conditions can have significant impact on the severity and frequency of traffic crashes. This study investigated factors that influence the severity of vehicle crashes during rainy weather in California. Data from 23,242 rain-related crashes in California were taken from the Highway Safety Information System (HSIS) database. The data was divided into 12 groups based on driver gender (male and female) and crash type (six categories: rear-end, hit object, sideswipe, overturned, head-on, and broadside). Each group was assigned a logistic regression model for crash severity (Property Damage Only (PDO) vs. injuries or fatalities (NotPDO)) yielding 12 models for various combinations of driver gender and crash types. Results indicate that factors such as the number of vehicles involved, vehicle manufacturing year, annual average daily traffic (AADT), road topography, season of crash, number of lanes, and driver age group all significantly influenced crash severity across various scenarios. These findings provide detailed insights into how various factors contribute to crash severity in different scenarios, allowing policymakers to develop targeted interventions. Policymakers can utilize the findings of this study to implement targeted measures in areas with high frequencies of specific crash types, particularly during adverse environmental conditions.

This study investigates the factors that influence household trip generation across the United States using the National Household Travel Survey (NHTS) dataset. Recognizing the limits of a one-size-fits-all modeling approach, we conduct a two-stage analysis to investigate spatial heterogeneity within travel behavior. Stage one creates a benchmark analysis, comparing advanced machine learning models (CatBoost and random forest) to a traditional linear regression model. Contrary to prevailing trends in predictive modeling, the results reveal that linear regression not only delivers competitive overall performance but also emerges as the best performing model in the majority of states. Providing optimal balance between predictive accuracy and interpretability. Building on these findings, the second stage applies state specific linear models to uncover geographic differences in trip generation drivers. The findings highlight extensive spatial heterogeneity: while core demographic variables like household size and the presence of young children show consistent effects across the US, the influence of socio-economic factors such as income and vehicle ownership are highly context-dependent and spatially volatile. These findings highlight the importance of moving beyond black box modeling and instead implementing place based, context sensitive techniques in the promotion of more effective and equitable transportation plans.

Freeze–thaw (FT) cycles and moisture susceptibility are important factors influencing the geotechnical characteristics of soil–aggregates. Given the lack of published information on the behaviour of base course materials stabilised with styrene butadiene emulsions (SBE) and cement–SBE-treated base (CSBETB) under environmental conditions, especially freezing and thawing, this study investigated the effects of these additives on the CSBETB performance. The primary goal was to evaluate the resistance of CSBETB to moisture damage by performing FT, Marshall conditioning and AASHTO T-283 tests and to evaluate the long-term stripping susceptibility of CSBETB while also predicting the liquid antistripping additives to assess the mixture’s durability and workability. Specimens were stabilised with Portland cement, SBE and a Portland cement–SBE mixture and cured for 7 days, and their short- and long-term performances were studied. Test evaluation results show that the additions of additives increase the resistance of the mixtures to moisture damage. Results of durability tests performed for determining the resistance of compacted specimens to repeated FT cycles indicate that the specimen with the 4% cement–8% SBE mixture significantly improves water absorption, volume changes and weight losses. This indicates the effectiveness of this additive as a road base stabiliser with excellent engineering properties.

Traffic safety assessments rely on Surrogate Safety Measures (SSMs), yet their diversity hinders understanding and selection. This paper proposes a novel conceptual framework to systematically categorize SSMs through what we term Motion Scenario Mapping, an approach inspired by queuing theory notation and the concept of short-term behavioral storytelling. The framework explicitly defines interaction stories between a following and leading vehicle to reveal hidden assumptions within each SSM, achieved through a combined coding system. Examining ten common SSMs, the research demonstrates that the framework effectively exposes underlying assumptions, enabling critical evaluation of their contextual validity. By emphasizing short-term risk dynamics, this approach offers a structured understanding of interaction mechanisms and provides a systematic foundation for comparing existing SSMs, identifying research gaps, and guiding future development. This structured ontology has the potential to enhance the analysis and design of safety measures for future transportation systems.

The dynamic modulus (| E ^∗ |) is the key parameter to characterize the viscoelastic behavior of asphalt mixtures, making it an essential component of the mechanistic‐empirical pavement design guide (MEPDG). However, direct laboratory measurement of | E ^∗ | is time‐consuming and costly, motivating the development of reliable predictive models. This goal is usually achieved by developing and | E ^∗ | master curve for each asphalt mixture, covering a vast spectrum of temperatures and loading frequencies. Therefore, if the asphalt mix type and the production technology of asphalt mixture change, different master curves must be developed. This study proposes a hybrid framework that integrates multivariate adaptive regression splines (MARS) with the Newton–Raphson–based optimizer (NRBO) to propose a unique formula to predict | E ^∗ | of two types of asphalt mixture (i.e., asphalt concrete (AC) and stone matrix asphalt (SMA) produced by two technology, namely, hot mix asphalt (HMA) and warm mix asphalt (WMA). Four key input variables, namely, mixture type, production technology, loading frequency, and temperature, were used as predictors. The NRBO algorithm was employed to tune critical MARS hyperparameters, including maximum basis functions, penalty parameter, threshold, and maximum interaction terms, thereby enhancing predictive accuracy while mitigating overfitting. Model performance was assessed using various statistical indexes. Results demonstrate that the optimized MARS model achieved outstanding accuracy, with R ² = 0.9889, NSE = 0.98, a20‐index = 0.9791, and RMSE = 130.198 across the full dataset. The relative importance analysis identifies temperature as the most influential input for predicting | E ^∗ |. A parametric study further highlighted the dominant influence of temperature and frequency on | E ^∗ |, while SMA‐WMA mixtures consistently exhibited the highest modulus.