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.

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.

Promoting cycling is an essential component of sustainable urban transportation, particularly in developing countries where bicycle use remains limited. This study proposes a two-step machine-learning framework to identify the predictors associated with bicycle use and cycling popularity in Tehran, Iran. A questionnaire survey was conducted among 1027 respondents, of whom 971 valid responses were retained for analysis. In the first step, the entire sample, including cyclists and non-cyclists, was analyzed using Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) models to predict bicycle use. In the second step, the analysis focused exclusively on existing cyclists. A Cycling Popularity Index (CPI) was developed and classified into three levels, and ten machine-learning algorithms were evaluated. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was incorporated into model development. The results showed that Random Forest achieved the highest predictive performance in the first step, with bicycle-sharing availability, access to private cars, traffic congestion, occupation, proximity to public transportation, and social norms identified as the strongest predictors of bicycle use. In the second step, KNN-SMOTE achieved the best overall predictive performance, while Permutation Feature Importance identified occupation, age, social norms, perceived cycling safety, and physical condition as the most influential predictors of cycling popularity. The proposed framework demonstrates the value of combining multiple machine-learning algorithms with class-balancing techniques for analyzing imbalanced transportation datasets and provides a transferable methodology for similar studies in developing cities.