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  • Mobility and traffic research  (5)
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  • Mobility and traffic research  (5)
  • 1
    Online Resource
    Online Resource
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications
    Abstract: Recently, piezoelectric transducers have gained significant attention for energy recovery applications in road engineering. However, in laboratory tests, vehicle loads are often simplified using vertical vibration loads, thus ignoring the role of vehicle tangential loads. In this study, a power output model of a piezoelectric transducer under vertical and tangential loads was proposed through theoretical analysis. A bidirectional cyclic dynamic load test was conducted on the piezoelectric-concrete specimens in combination with a large dynamic and static straight shear instrument, and the power output law of the piezoelectric transducer under vertical and horizontal shear was investigated. The results revealed that the vertical vibration load was the main factor affecting the output performance of the piezoelectric transducer; however, with the addition of the tangential load, the electric energy output of piezoelectric pavement increased with an increase in the horizontal shear rate and displacement. When vibrating vertically (200 kPa and 4 Hz), the electric energy output of piezoelectric pavement was 4.979  μW. However, under the action of vertical vibration and horizontal shear with the working conditions of 200 kPa and 4 Hz, and 0.1 mm and 1 Hz, the electric energy output of piezoelectric pavement was 21.04  μW, which was 3.2 times that of the vertical vibration load alone. Therefore, by considering the influence of vehicle tangential loads, the power output of piezoelectric transducers can be calculated more accurately, which provides a reference for actual installed capacities in real engineering applications.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 2403378-9
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  • 2
    Online Resource
    Online Resource
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications
    Abstract: Traffic forecasting plays an important role in urban planning. Deep learning methods outperform traditional traffic flow forecasting models because of their ability to capture spatiotemporal characteristics of traffic conditions. However, these methods require high-quality historical traffic data, which can be both difficult to acquire and non-comprehensive, making it hard to predict traffic flows at the city scale. To resolve this problem, we implemented a deep learning method, SceneGCN, to forecast traffic speed at the city scale. The model involves two steps: firstly, scene features are extracted from Google Street View (GSV) images for each road segment using pretrained Resnet18 models. Then, the extracted features are entered into a graph convolutional neural network to predict traffic speed at different hours of the day. Our results show that the accuracy of the model can reach up to 86.5% and the Resnet18 model pretrained by Places365 is the best choice to extract scene features for traffic forecasting tasks. Finally, we conclude that the proposed model can predict traffic speed efficiently at the city scale and GSV images have the potential to capture information about human activities.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 2403378-9
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  • 3
    Online Resource
    Online Resource
    SAGE Publications ; 2021
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2675, No. 11 ( 2021-11), p. 373-383
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2675, No. 11 ( 2021-11), p. 373-383
    Abstract: Air traffic administration requires evidence when promoting new technology or a new concept of operation. Therefore, when decision support tools are applied, it is necessary to analyze the costs and benefits quantitatively. This paper focuses on the evaluation of Key Performance Indicators (KPIs) correlated with the improvement of arrival operations after the implementation of the Arrival Management (AMAN) system and Wake Turbulence Re-categorization in China (RECAT-CN). Firstly, we give an overview of the implementation of the AMAN system and RECAT in China. Secondly, the KPIs related to the arrival operation are established according to the characteristics of AMAN and RECAT-CN, based on the existing KPI systems in the field of Air Traffic Management (ATM). The proposed KPIs are: airport acceptance rate; final approach interval; flight time within the terminal area (TMA); and taxi-in time. Thirdly, arrival operation within the TMA around Guangzhou International Airport is used as an example to carry out the quantitative analysis. The region and time range were defined for the performance comparison, and external factors were also examined. Finally, using descriptive and inferential statistics, the proposed KPIs’ comparison results are presented and visualized. Such results indicate a significant improvement in arrival operation with the AMAN system and RECAT-CN at Guangzhou International Airport.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 2403378-9
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  • 4
    Online Resource
    Online Resource
    SAGE Publications ; 2022
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2676, No. 7 ( 2022-07), p. 55-65
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 7 ( 2022-07), p. 55-65
    Abstract: Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of background occlusion. The result shows that the proposed algorithm can identify traffic flow and turning events at a root mean square error (RMSE) of 10. The result shows that a Kalman filter with an intersection of union (IOU)-based tracker performs well at the condition of background occlusion. Also, the proposed algorithm can detect and track vehicles at different optical conditions. Bad weather and night-time will influence the detecting and tracking process in areas far from traffic cameras. The traffic flow extracted from traffic videos contains road information, so it can not only help with single intersection control, but also provides information for a road network. The temporal characteristic of observed traffic flow gives the potential to predict traffic flow based on detected traffic flow, which will make the traffic light control more efficient.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2403378-9
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  • 5
    Online Resource
    Online Resource
    SAGE Publications ; 2022
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2676, No. 7 ( 2022-07), p. 786-798
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 7 ( 2022-07), p. 786-798
    Abstract: In recent years, with the development of air traffic in China, airspace resources cannot keep up with the growth of traffic demand. Therefore, enhancement of runway capacity by reducing wake turbulence separation has become a research hotspot in the air traffic management field. As there are gaps between theory and practice, many scholars are doing practical operation-based evaluations, focusing on the final separations (intervals) between two successive aircraft. However, few studies have considered the impact of different controllers and the evolution of separations over time. This paper is dedicated to analyzing the final separations between leading and trailing aircraft based on the aircraft trajectories. This study carries out the final separation analysis from static and dynamic perspectives, considering the final separations under different traffic pressures, by different controllers, and in the different segregated and mixed operations. Guangzhou Baiyun International Airport (ZGGG) is taken as an experimental case. The results indicate that the final separation buffer decreases over time. Moreover, under the same high traffic pressures, the final separations could be used to compare the effectiveness of different controllers.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2403378-9
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