The transportation control infrastructure serves as the foundation for regional traffic signal control. However, in practice, this infrastructure is often imperfect and complex, characterized by factors such as heterogeneity and uncertainty, which pose significant challenges to existing methods and systems. Therefore, this paper proposes a novel approach to coordinated traffic signal control that emphasizes flexibility. To achieve this flexibility, we combine the flexible model of complex networks with robust fuzzy control methods. This approach enables us to overcome the complexity of the transportation control infrastructure and ensure efficient management of traffic signals. Additionally, to ensure long-term operational ease, we develop a regional traffic signal control system using steam computing technology, which provides high scalability and compatibility. Finally, computational experiments are performed to validate adaptability and performance of our proposed approach.
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http://dx.doi.org/10.3390/s23135796 | DOI Listing |
Sensors (Basel)
January 2025
Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent's decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Civil Engineering Architecture and the Environment, Hubei University of Technology, Wuhan 430068, China.
The green vision rate of rural highway greening landscape is a key factor affecting the driver's visual load. Based on this, this paper uses the eye tracking method to study the visual characteristics of drivers in different green vision environments on rural highways in Xianning County. Based on the HSV color space model, this paper obtains four sections of rural highway with a green vision rate of 10~20%, green vision rate of 20~30%, green vision rate of 30~40%, and green vision rate of 40~50%.
View Article and Find Full Text PDFAfr J Prim Health Care Fam Med
December 2024
Department of Neurology, Faculty of Neurology, Neurology and Neurophysiology Center, Vienna.
N/A.
View Article and Find Full Text PDFFront Neurosci
January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
View Article and Find Full Text PDFExp Neurol
January 2025
Department of Military Traffic Injury Prevention and Control, Daping Hospital, Army Medical University, Chongqing, China. Electronic address:
Traumatic brain injury (TBI), particularly at high altitudes (HA-TBI), is a leading cause of mortality and disability, yet clear diagnostic and treatment protocols are lacking. This study explores the early pathophysiological changes occurring within 24 h following HA-TBI, with a focus on differentially expressed proteins (DEPs) and phosphorylated proteins (DEPPs). Using a low-pressure hypoxic chamber to simulate high-altitude conditions combined with a controllable cortical impact (CCI) model, we established a rat model of HA-TBI.
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