Big Data applications have transformed Intelligent Transport Systems (ITS), enabling improvements in traffic management, safety, and efficiency. This study presents a bibliometric analysis and review of the recent advancements of big data applications in ITS. For this bibliometric analysis, the Scopus database was utilized due to its extensive resources. Various tools, such as RStudio, VOSviewer, Excel, and Python, were used to analyze data and identify trends, patterns, and relationships in the selected articles through performance analysis and science mapping. The study examined 447 articles published between 2014 and 2023. The analysis indicates that research in this field has experienced exponential growth annually, although it experienced setbacks in 2020 due to the global pandemic before regaining momentum. While the number of research publications has risen sharply, the slower growth in citations highlights the need for a greater focus on producing higher-quality research. Our investigation revealed that the most significant research efforts focused on traffic flow prediction, traffic anomaly prediction, traffic safety, the integration of big data with the Internet of Things (IoT) and the Internet of Vehicles (IoV). China, United States, and Canada were the primary contributors to this field, with China conducting the majority of studies. We summarized and critically reviewed the most cited papers, as well as those that present the most significant innovations in this field. We found that ethical, privacy, and security concerns related to the use of Big Data in ITS have received limited attention. This work aims to serve as a valuable resource for researchers and practitioners, encouraging innovation and the development of more effective and sustainable transportation solutions.

The integration of machine learning (ML) into intelligent transport systems (ITS) has significantly enhanced the efficiency, security, and sustainability of urban transportation. Bibliometric analysis can identify existing research hotspots and the areas of interest and predict development trends, which can help to identify the current advancements in the role of ML in ITS. The Scopus database was used for the analysis because of its extensive collection of scholarly publications. This study examines a comprehensive set of 534 papers published between 2001 and 2023, utilizing various tools, including RStudio, VOSviewer, Excel, Publish & Perish, and Python to analyze data. The publication trend indicates that research in this area has grown exponentially each year. The keyword analysis indicated that key research interests within ML applications in ITS focus on network and communication technologies, urban mobility and infrastructure, safety and efficiency enhancements, and emerging topics such as demand-responsive transport and integrated decision-making. Europe leads in publication count, while East Asia, particularly China, contributes significantly, and research from regions such as North America, the Middle East, and Oceania, despite lower output, shows higher citation averages. This study also provides a comprehensive review of the top 50 most-cited publications, identifying six key focus areas and demonstrating the significant advancements in this field. However, this study is limited by keyword and tool biases, potential scope oversight, and reliance on a single database. Future research should expand keywords, use diverse tools, and include more databases. Overall, this study aims to serve as a valuable resource for researchers and practitioners, fostering innovation and developing more effective and sustainable transportation solutions.

Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action).

Public transportation systems, particularly bus services, are essential for urban mobility, significantly influencing environmental sustainability and easing traffic congestion. This study explores the impact of weather conditions, special events, and holidays on bus ridership, using data from Kuala Lumpur's Rapid Bus KL service. A range of machine learning models—Linear Regression, ARIMA, Random Forest, and XGBoost—were applied to predict ridership and identify key influencing factors. The study found that advanced models, especially Random Forest achieved the highest performance, with an value of 0.9656, demonstrating its ability to explain 96.56% of the variance in ridership. Key features, such as weekdays versus weekends, temperature, and festivals, were identified as significant predictors of ridership patterns. The findings highlight the importance of incorporating these variables into operational planning to improve public transit service delivery and meet fluctuating demand, especially during peak times. The study concludes with suggestions for further research into enhancing prediction accuracy through refined model development and data integration.