# Regional Traffic Event Detection Using Data Crowdsourcing  
[PERSON] 1  
1 Department of Big Data, Chungbuk National University, Cheongju 28644, Republic of Korea;  
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[PERSON] 2  
2 School of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea;  
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[EMAIL_ADDRESS] (H.L.) [PERSON] 2  
2 School of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea;  
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[EMAIL_ADDRESS] (H.L.) [PERSON] 3  
3 Department of Computer Engineering, Changwon National University, Changwon-si 51140, Republic of Korea;  
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[PERSON] 2  
2 School of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea;  
[EMAIL_ADDRESS] (H.L.) [PERSON] 4  
4 Department of Artificial Intelligence Convergence, Wonkwang University, Iksan-si 54538, Republic of Korea;  
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[PERSON] 2  
2 School of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea;  
[EMAIL_ADDRESS] (S.S.);  
[EMAIL_ADDRESS] (H.L.) [PERSON] 2  
2 School of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea;  
[EMAIL_ADDRESS] (S.S.);  
[EMAIL_ADDRESS] (H.L.) Footnote 2: email: {jose.com, j.j.choi, j.  
as event identification. These large volumes of data are collected via crowdsourcing for machine learning, which analyzes the information needed to identify events and increases the accuracy of event detection. Crowdsourcing-based traffic event identification uses human intelligence to identify traffic events [5; 6]. This method uses online platforms where ordinary people gather. For example, mobile applications are used to observe and record traffic events, and then the data are uploaded to a central server. These data are provided by human workers, who review them to distinguish events and assign accurate labels. Crowdsourcing-based traffic event identification can be used for various purposes. For example, the authors of [7] used crowdsourcing to design a driver assistance system, and the authors of [8] also used crowdsourcing to predict road traffic conditions. In these ways, crowdsourcing can measure the degree of road traffic congestion and rapidly respond to traffic accidents. Traffic events occur under various circumstances. Manually classifying these events is difficult and costly. For example, it is difficult to manually count and classify all the cars in a traffic jam, where vehicles block a street. Consequently, manual classification cannot be used to accomplish real-time event detection and handling due to its time-consuming nature. Machine learning uses existing events to inform and identify new data based on what the models have learned. For example, the authors of [9] proposed an event detection technique that collects real-time tweets and uses text mining. The authors of [10] suggested collecting traffic-related tweets and preprocessing and analyzing the data using a big data processing platform known as SAP HANA. Traffic events can be quickly and accurately classified through such efforts, and various services such as traffic congestion prediction can be provided. Therefore, machine learning is an essential technology for traffic event identification. However, conventional techniques generally use entire datasets when collecting and classifying data. Since such methodologies use raw social data, they may include unnecessary data, which complicates analysis. In traffic event identification, the characteristics of the data vary greatly according to the region where traffic occurs and the type of event. Therefore, to distinguish events, relevant data and geographical locations must be selected. Currently, studies are being conducted on event detection techniques using the geotag functions provided by social media to extract regional information [11].