Regional Traffic Event Detection Using Data Crowdsourcing
Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/16/9422 |
| _version_ | 1797585609559113728 |
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| author | Yuna Kim Sangho Song Hyeonbyeong Lee Dojin Choi Jongtae Lim Kyoungsoo Bok Jaesoo Yoo |
| author_facet | Yuna Kim Sangho Song Hyeonbyeong Lee Dojin Choi Jongtae Lim Kyoungsoo Bok Jaesoo Yoo |
| author_sort | Yuna Kim |
| collection | DOAJ |
| description | Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic information using mobile devices to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time and cost. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data for detecting events that influence traffic. The proposed technique uses various machine-learning methods to accurately identify events and location information. Therefore, it can resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Performance evaluations also demonstrated its validity and effectiveness. |
| first_indexed | 2024-03-11T00:08:22Z |
| format | Article |
| id | doaj.art-562a896c9e604a2f8070d6d51ec1b122 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:08:22Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-562a896c9e604a2f8070d6d51ec1b1222023-11-19T00:09:33ZengMDPI AGApplied Sciences2076-34172023-08-011316942210.3390/app13169422Regional Traffic Event Detection Using Data CrowdsourcingYuna Kim0Sangho Song1Hyeonbyeong Lee2Dojin Choi3Jongtae Lim4Kyoungsoo Bok5Jaesoo Yoo6Department of Big Data, Chungbuk National University, Cheongju 28644, Republic of KoreaSchool of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of KoreaSchool of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of KoreaDepartment of Computer Engineering, Changwon National University, Changwon-si 51140, Republic of KoreaSchool of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of KoreaDepartment of Artificial Intelligence Convergence, Wonkwang University, Iksan-si 54538, Republic of KoreaSchool of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of KoreaAccurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic information using mobile devices to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time and cost. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data for detecting events that influence traffic. The proposed technique uses various machine-learning methods to accurately identify events and location information. Therefore, it can resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Performance evaluations also demonstrated its validity and effectiveness.https://www.mdpi.com/2076-3417/13/16/9422machine learningcrowdsourcingevent detectiontransportation systems |
| spellingShingle | Yuna Kim Sangho Song Hyeonbyeong Lee Dojin Choi Jongtae Lim Kyoungsoo Bok Jaesoo Yoo Regional Traffic Event Detection Using Data Crowdsourcing Applied Sciences machine learning crowdsourcing event detection transportation systems |
| title | Regional Traffic Event Detection Using Data Crowdsourcing |
| title_full | Regional Traffic Event Detection Using Data Crowdsourcing |
| title_fullStr | Regional Traffic Event Detection Using Data Crowdsourcing |
| title_full_unstemmed | Regional Traffic Event Detection Using Data Crowdsourcing |
| title_short | Regional Traffic Event Detection Using Data Crowdsourcing |
| title_sort | regional traffic event detection using data crowdsourcing |
| topic | machine learning crowdsourcing event detection transportation systems |
| url | https://www.mdpi.com/2076-3417/13/16/9422 |
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