To provide an equitable transportation system for the public, it is vital to fully understand underrepresented populations’ active transportation needs and their challenges. This study is designed to identify the active transportation studies that focus on underrepresented populations, evaluate the quantification approaches used in the literature, and uncover barriers to the use of active transportation among underrepresented populations in the United States. We used PRISMA guidelines to systematically review relevant publications. After screening and reviewing the literature, some 60 articles were included in the review. The most frequent results when reviewing the literature include that active travel and daily transport trips decreased from younger to older age groups; bicycling was more popular among men, White, and individuals with higher education, whereas walking was more popular among women; bike usage was found to be low among minorities, and those with lower incomes. Furthermore, communities with poor accessibility to bike infrastructure had a larger concentration of African Americans, Hispanics, Asians, low-wage employees, individuals with lower education, and older adults.

Electric vehicles (EVs) are rapidly gaining popularity due to their environmental benefits, such as reducing greenhouse gas emissions. Considering the sociodemographic factors that influence the adoption of EVs is essential when developing equitable and efficient transportation policies. This article leverages the National Household Travel Survey (NHTS) 2022 data to analyze the sociodemographic factors influencing the adoption of EVs in the U.S. A binary logistic regression model and three machine learning models were employed to predict EV ownership in the U.S. The results of the regression model suggested that the Pacific division leads in EV adoption, most likely due to legislation and improved infrastructure, while regions such as East South Central suffer from lower EV adoption. The findings indicate that higher household income and home ownership significantly correlate with increased EV adoption. In contrast, renters and rural households exhibit lower adoption rates suggesting an increase in charging facilities in these regions can promote EV adoption. The Random Forest model outperforms others with an accuracy of 82.72%, suggesting its robustness in handling complex relationships between variables. Policy implications include the need for financial incentives for low-income households and increased charging infrastructure in rural and underserved urban areas to promote equitable EV adoption.

This study investigates the association between bikeshare program usage and neighborhoods with different socio-demographic information. This study investigated 2023 Citi Bike bikeshare data in New York City. For the analysis, Generalized Linear Regression (GLR) in ArcGIS Pro was used. The results revealed that Citi Bike bikeshare ridership increases in neighborhoods with higher income, more workers, more males, higher density, and lower African American and Hispanic population in New York City.

Intercity bus transportation provides essential public transit service, mainly to long-distance passengers and residents of rural areas. Although the intercity bus system can be beneficial to increase equity by providing such services to low-income people from remote rural areas, it suffers from a problem. The problem is that the socio-demographic characteristics of those disadvantaged individuals are still not crystal clear. This study aims to examine the relationship between access to intercity bus transportation and the socio-demographic characteristics of the region. This study addresses these problems through data integration and multiple regression analysis using a combination of socio-demographic variables across the U.S. The results showed that changes in the percentages of households with zero vehicles and households with an income of less than $50,000 are significantly associated with changes in the access to intercity bus transportation, and different levels of income affect access to intercity bus transportation. Among the significant variables in the model, the number of intercity bus stops can be controlled by policymakers to optimize access to intercity bus transportation. The findings demonstrate the importance of public transit for low-income households and imply that increasing bus access could help shift the spatial distribution of poverty and create more equal and inclusive communities.

As the deployment of Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS-L2) expands, understanding their real-world safety performance becomes essential. This study examines the severity and contributing factors of crashes involving vehicles equipped with ADS and ADAS-L2 technologies using NHTSA data. Using machine learning models on crash datasets from 2021 to 2024, this research identifies patterns and risk factors influencing injury outcomes. After data preprocessing and handling missing values for severity classification, four models were trained: logistic regression, random forest, SVM, and XGBoost. XGBoost outperformed the others for both ADS and ADAS-L2, achieving the highest accuracy and recall. Variable importance analysis showed that for ADS crashes, interactions with other road users and poor lighting were the strongest predictors of injury severity, while for ADAS-L2 crashes, fixed object collisions and low light conditions were most influential. From a policy and engineering perspective, this study highlights the need for standardized crash reporting and improved ADS object detection and pedestrian response. It also emphasizes effective human–machine interface design and driver training for partial automation. Unlike previous research, this study conducts comparative model-based evaluations of both ADS and ADAS-L2 using recent crash reports to inform safety standards and policy frameworks.

Using cell phone blocking apps is an effective way to prevent distracted driving. This study used a high-fidelity driving simulator to examine drivers’ behavior while using a cell phone blocking app. Thirty-five participants drove in a simulated network under four scenarios. Participants also completed pre- and post-survey questionnaires. The results support previous investigations regarding interactions with phones while driving. Results showed that drivers deviated from the center of the road, changed lanes significantly more often, and increased their steering velocity when drivers were interacting with a cell phone. The impacts of cell phone blocking apps were similar to the no distraction scenario while driving. This suggests that using cell phone blocking apps is one of the most effective ways to prevent distracted driving. Survey results indicated that only 23% of drivers used cell phone blocking apps before the experiment. However, 88% of the participants had a positive opinion about using these apps and indicated that they would use such apps after the experiment. These findings support the importance of cell phone blocking apps from a policy perspective and highlight the need to educate drivers about distracted driving prevention technologies.