A Feature Dimensionality Reduction Method with Lossless Accuracy in Vehicular Network Traffic Detection

Liu, Ziwei; Xie, Ying; Ding, Xuyang; Xu, Yang

doi:10.1109/confluence56041.2023.10048831

2023 13th International Conference on Cloud Computing, Data Science &Amp; Engineering (Confluence) 2023

DOI: 10.1109/confluence56041.2023.10048831

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A Feature Dimensionality Reduction Method with Lossless Accuracy in Vehicular Network Traffic Detection

Ziwei Liu

Ying Xie

Xuyang Ding

et al.

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Cited by 1 publication

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Real-Time Vehicle Identification for Improving the Traffic Management system-A Review

Sanjay S Tippannavar,

Yashwanth S D

2023

JTCSST

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Due to the increasing number of cars on the road and the exponential growth of traffic throughout the globe, regulating traffic has become crucial in the most industrialized countries. The development of technology has led to the current state of traffic management systems that comes with the ability to count, monitor, and predict the speed of vehicles in order to improve the transportation planning. This has also reduced the number of accidents that occur due to worsen traffic conditions. Road traffic surveys have been carried out manually for a long time since automated measures were not often employed due to the difficulty of installation. Machine learning in image processing is widely recognized as a significant approach for real-world applications such as traffic monitoring. The primary benefit of automated vehicle counting is that it allows for the management and evaluation of traffic in the urban transportation system. There are many methods employing distributed acoustic systems on intelligent transportation systems, including YOLO v4 and the Normalized Cross-correlation algorithm, which uses ultrasonic sensors and the algorithms ALPR, YOLO, GDPR, and CNN. The simplest method for identifying a vehicle is to gather information from sensors such as cameras, vibration detectors, ultrasound detectors, or acoustic detectors. These sensors are combined with the proper microcontrollers to determine the amount of traffic using the most recent data and theory. This review article is a quick reference for researchers working on safety-related traffic management systems.

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Real-Time Vehicle Identification for Improving the Traffic Management system-A Review

Sanjay S Tippannavar,

Yashwanth S D

2023

JTCSST

View full text Add to dashboard Cite

show abstract

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A Feature Dimensionality Reduction Method with Lossless Accuracy in Vehicular Network Traffic Detection

Cited by 1 publication

References 16 publications

Real-Time Vehicle Identification for Improving the Traffic Management system-A Review

Real-Time Vehicle Identification for Improving the Traffic Management system-A Review

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