Transportation Research

Transportation research currently focuses on improving the accuracy and reliability of traffic data analysis and modeling, particularly addressing biases in large-scale datasets and enhancing the detection of anomalous events like accidents. Researchers are employing advanced machine learning techniques, including hybrid neural networks (CNN-RNNs), graph neural networks, and Bayesian methods, to analyze diverse data sources (physiological signals, driving behavior, sensor data) and improve model calibration and validation. These advancements aim to enhance traffic safety, optimize transportation system performance, and inform evidence-based policy decisions by providing more accurate and reliable insights into traffic flow, driver behavior, and system-wide anomalies.