Real-time Traffic Jam Detection and Congestion Reduction Using Streaming Graph Analytics

Abbas, Zainab; Sottovia, Paolo; Hassan, Mohamad Al Hajj; Foron, Daniele; Bortoli, Stefano

doi:10.1109/bigdata50022.2020.9378068

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Abnormal vehicle running has negative impacts on public transportation. For one thing, it may result in road traffic congestion which would probably make it less efficient for people to travel [1]. For another, it will put a financial strain on society.…”

Section: ⅰ Introductionmentioning

confidence: 99%

An R–A dual network detection model for abnormal behavior of running vehicles

Liu,

Sun,

Fan

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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Detection of abnormal behavior of running vehicles plays an essential role in road traffic safety. In this paper, the R-A (ResNet-Adaboost) dual network detection algorithm to cope with the problem that the existing detection algorithm of abnormal running behavior cannot detect different kinds of abnormal running behavior and cannot adapt to different detection scenarios. Firstly, this paper utilizes the YOLOV5-DEEPSORT algorithm to collect the spatial and temporal information of the target vehicle. In this way, various kinds of abnormal running behavior can be detected efficiently. Secondly, based on the information matrix, the entropy method is used to determine the dynamic weight of various abnormal running information, and then to determine the type of information input to the judgment model so as to obtain the information more accurately. Finally, the accurate vehicle running information is input into the R-A detection model. In this paper, an algorithm to estimate the abnormal running information is established as the information classification basis of the R-A detection model. The algorithm calculates the difference between the input vehicle running information matrix and the normal one to judge whether the information matrix is abnormal. Moreover, it is proved by the field experiments and NGSSIM datasets that the R-A detection model is able to detect different abnormal running behavior vehicle in different scenarios. The experimental results show that the R-A model with an accuracy of 90%-95% is better than the existing detection model. Additionally, it can more accurately detect various abnormal behavior of running vehicles.

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Section: ⅰ Introductionmentioning

confidence: 99%

An R–A dual network detection model for abnormal behavior of running vehicles

Liu,

Sun,

Fan

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Currently, UAVs have a wide range of applications in areas such as reconnaissance, attack, aerial photography, 4 forestry, 5 transportation, 6 disaster relie, 7 ), communication relay, 8 wildlife tracking, urban stability, mapping, power inspection, 10,9 agriculture, 11 geological survey, 12 and border control. Traffic congestion is a problem that large cities must face, 13 due to the limitations of current transportation tools. There is an urgent need for new types of transportation to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

An investigation on the structural vibrations of multi-rotor passenger drones

Chen,

Meng,

Wang

et al. 2023

International Journal of Micro Air Vehicles

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In order to solve the structural damage problem of the first generation of large multi-rotor manned drones, the present work has designed to study the structural vibration problems of multi-rotor drones. On a small multi-rotor drone, the laser vibration meter verified the reliability of acceleration sensor measurement of vibration and found that circular shape carbon fiber arms have strong damping abilities, with the strongest vibration in the Z-axis direction. To improve the design of the second generation of large multi-rotor manned drones, elliptical shape carbon fiber arms were employed instead of circular arms. Experiments showed that the main vibrations of the large multi-rotor manned drone’s arm are low-frequency vibrations below 200Hz, producing mainly torsional and bending modes, and the elliptical carbon fiber arms significantly reduce vibrations in the Z-axis direction. This study provides experimental data support for multi-rotor manned drones and further presents an improvement strategy for suppressing the vibrations of the multi-rotor manned drones.

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