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Method for Obtaining Better Traffic Survey Data

Electronics and Telecommunications Research Institute (ETRI), Daegu 42994, Korea
School of Electronics Engineering, Graduate School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
Author to whom correspondence should be addressed.
Academic Editor: Rashid Mehmood
Electronics 2021, 10(7), 833;
Received: 27 February 2021 / Revised: 25 March 2021 / Accepted: 30 March 2021 / Published: 31 March 2021
(This article belongs to the Special Issue Data Analysis in Intelligent Communication Systems)
Road traffic surveys determine the number and type of vehicles passing by a specific point over a certain period of time. The manual estimation of the number and type of vehicles from images captured by a camera is the most commonly used method. However, this method has the disadvantage of requiring high amounts of manpower and cost. Recently, methods of automating traffic volume surveys using sensors or deep learning have been widely attempted, but there is the disadvantage that a person must finally manually verify the data in order to ensure that they are reliable. In order to address these shortcomings, we propose a method for efficiently conducting road traffic volume surveys and obtaining highly reliable data. The proposed method detects vehicles on the road from CCTV (Closed-circuit television) images and classifies vehicle types using deep learning or a similar method. After that, it automatically informs the user of candidates with a high probability of error and provides a method for efficient verification. The performance of the proposed method was tested using a data set collected by an actual road traffic survey company. As a result, we proved that our method shows better accuracy than the previous method. The proposed method can reduce the labor and cost in road traffic volume surveys, and increase the reliability of the data due to more accurate results. View Full-Text
Keywords: verification method; deep learning; vehicle classification; vehicle count verification method; deep learning; vehicle classification; vehicle count
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MDPI and ACS Style

Kang, M.-S.; Kim, P.-K.; Lim, K.-T.; Cho, Y.-Z. Method for Obtaining Better Traffic Survey Data. Electronics 2021, 10, 833.

AMA Style

Kang M-S, Kim P-K, Lim K-T, Cho Y-Z. Method for Obtaining Better Traffic Survey Data. Electronics. 2021; 10(7):833.

Chicago/Turabian Style

Kang, Mi-Seon, Pyong-Kun Kim, Kil-Taek Lim, and You-Ze Cho. 2021. "Method for Obtaining Better Traffic Survey Data" Electronics 10, no. 7: 833.

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