Advanced air mobility (AAM) offers transformative opportunities for urban transportation by potentially alleviating traffic congestion and enhancing mobility. However, implementing AAM in densely populated urban corridors poses significant challenges mainly as a result of noise emissions from its unique vertical takeoff, landing, and low-altitude operations. This study investigates the operational noise challenges of AAM and highlights operational strategies to mitigate noise-related issues. First, the current state-of-the-art in AAM, focusing on noise pollution as a critical concern, was assessed. Second, the potential impacts of AAM operational noise on urban communities were examined. Third, strategies to minimize noise exposure, such as route planning and noise zoning, were developed to ensure regulatory compliance and community acceptance. Furthermore, advanced noise reduction technologies, including optimized vehicle designs and propulsion systems, were explored to reduce noise emissions while maintaining operational performance. The results underscore the importance of adopting adaptive noise reduction technologies and integrating noise management into urban AAM planning. This study provides policy decision-makers and planners with a holistic synthesis view of AAM noise sources, types, and challenges and offers practical strategies for AAM integration. The conclusion yields that addressing noise challenges is essential for AAM’s successful and sustainable integration, leading to quieter, more community-friendly urban airspace. Policy recommendations are made to foster stakeholder collaboration, encourage financial investment in AAM infrastructure, and promote further research into AAM noise reduction and integrative urban planning.

This study introduces a novel model for accurately estimating the cuboid of a road vehicle using a monovision sensor and road geometry information. By leveraging object detection models and core vectors, the proposed model overcomes the limitations of multi-sensor setups and provides a cost-effective solution. The model demonstrates promising results in accurately estimating cuboids by utilizing the magnitudes of core vectors and considering the average ratio of distances. This research contributes to the field of intelligent transportation by offering a practical and efficient approach to 3D bounding box estimation using monovision sensors. We validated feasibility and applicability are through real-world road images captured by CCTV cameras.

Recent advances in sensing and artificial intelligence technologies have laid the foundation for proactive pedestrian safety at non-signalized intersections. These technologies enable the prediction of future pedestrian-vehicle conflicts, allowing for timely intervention. Existing studies, however, overlook the uncertainty inherent in future prediction, which mainly stems from behavioral variability and observational inaccuracies. Probabilistic trajectory prediction can capture these uncertainties, yet it has rarely been applied to future conflict evaluation. To improve future conflict evaluation by accounting for uncertainty, this study introduces a novel method that integrates probabilistic trajectory prediction with a newly designed surrogate safety measure, Stochastic Predicted Time to Collision (SPTTC). SPTTC enables accurate conflict prediction, reliable scenario classification and evaluation of conflict severity progression, as demonstrated in quantitative results and case studies. Across different types of non-signalized intersections, SPTTC was separately integrated with four probabilistic trajectory prediction models, achieving F1 scores of 0.77–0.84 and consistently outperforming baseline methods. The proposed method also exhibits strong robustness under high-uncertainty conditions, such as unpredictable pedestrian behavior (e.g., children), low visibility (e.g., nighttime), and missing data. This proposed method can be integrated into proactive pedestrian protection systems, enhancing safety by providing timely alerts to pedestrians or activating vehicle brakes at critical moments.

Addressing pedestrian safety at intersections is one of the paramount concerns in the field of transportation research, driven by the urgency of reducing traffic-related injuries and fatalities. With advances in computer vision technologies and predictive models, the pursuit of developing real-time proactive protection systems is increasingly recognized as vital to improving pedestrian safety at intersections. The core of these protection systems lies in the prediction-based evaluation of pedestrian's potential risks, which plays a significant role in preventing the occurrence of accidents. The major challenges in the current prediction-based potential risk evaluation research can be summarized into three aspects: the inadequate progress in creating a real-time framework for the evaluation of pedestrian's potential risks, the absence of accurate and explainable safety indicators that can represent the potential risk, and the lack of tailor-made evaluation criteria specifically for each category of pedestrians. To address these research challenges, in this study, a framework with computer vision technologies and predictive models is developed to evaluate the potential risk of pedestrians in real time. Integral to this framework is a novel surrogate safety measure, the Predicted Post-Encroachment Time (P-PET), derived from deep learning models capable to predict the arrival time of pedestrians and vehicles at intersections. To further improve the effectiveness and reliability of pedestrian risk evaluation, we classify pedestrians into distinct categories and apply specific evaluation criteria for each group. The results demonstrate the framework's ability to effectively identify potential risks through the use of P-PET, indicating its feasibility for real-time applications and its improved performance in risk evaluation across different categories of pedestrians.