# Regional Traffic Event Detection Using Data Crowdsourcing
## 4 Performance Evaluation  
We verified the superiority of the proposed regional traffic event detection technique by comparing its performance to conventional techniques. We used the sklearn and tensorflow libraries for machine learning and an entity name recognition API provided by ETRI for entity name recognition [28]. The geocoding APIs provided by Google and Kakao were used for region extraction [29; 30]. Data provided by agencies such as TBN Korea Traffic Broadcast, Twitter, Korea Expressway Corporation, etc., were used for this evaluation [31]. TBN Korea Traffic Broadcast is one of South Korea's terrestrial broadcasting stations and airs programs covering to 24 h traffic information. Table 3 shows the characteristics of the collected data. For the performance evaluations, we used data from 5 March 2021 to 30 September 2021, excluding data for July, as the training dataset, whereas data from 1 July 2021 to 31 July 2021 were used as the test dataset. We used TBN Korea Transportation Broadcasting data, which includes data from Twitter, broadcast stations and so on, without collecting data directly from external sources such as Twitter and broadcast stations. We collected and managedTBN Korea Transportation Broadcasting data through web crawling. The TBN data set consists of data from external agencies such as Twitter and broadcasting stations, data from public agencies such as Korea Expressway Corporation and Central Traffic Information Center, and data from direct data providers such as avid viewers and citizens. Figure 5 shows the distribution of the TBN data set. The size of data from external agencies such as Twitter and broadcasting stations is 2588. The size of data from public agencies such as Korea Expressway Corporation and Central Traffic Information Center is 22,837. Finally, the size of data from direct data providers such as avid viewers and citizens is 79,182. The collected traffic data include the region, ID, report data, and content. The regions are divided into all regions: Busan, Gwangju, Daegu, Daejeon, Gyeongin, Gangwon, Jeonbuk, Ulsan, Gyeongnam, Gyeongbuk, Jeju, and Chungbuk. The IDs include the reporter's name or social media name. In the case of data reported by a public organization, the public organization's name is the ID. The report date includes the date and time at which the data were reported as well as the year, month, day, hour, and minute. The content indicates traffic conditions such as accidents, construction, etc., and text. Table 4 shows an example of collected data. Since the original data were in Korean, they were translated into English for easier understanding. Figure 5: Distribution of the TBN data set. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & \multicolumn{2}{c}{**Content**} \\ \hline \multirow{3}{*}{Learning data set} & data collection period: 1 & \\  & March–30 September 2021 & 96,815 \\  & (excluding data for July) & \\ \hline \multirow{3}{*}{Test data set} & data collection period: & \\  & 2021/07/01–2021/07/31 & 11,379 \\ \hline \multirow{3}{*}{TBN data set} & external agency & Twitter, Broadcasting stations, \\  & & Radio, etc. \\ \cline{2-3}  & & Korea Expressway \\ \cline{2-3}  & public agency & Corporation, Central Traffic \\ \cline{2-3}  & & Information Center \\ \cline{2-3}  & direct data provider & avid viewers, citizens, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of the collected data. The performance evaluation of the proposed regional traffic event detection technique consisted of an event type classification and event occurrence region extraction. The event classification performance evaluation compared the models' performance and determined the most suitable model. To demonstrate the importance of binary classifiers, we verified the proposed technique by comparing the results of using binary classifiers and multi-class classifiers to the results of using only multi-class classifiers without binary classifiers.