# Regional Traffic Event Detection Using Data Crowdsourcing
## 5 Conclusions  
In this paper, we propose a social-media-based machine-learning technique to detect regional traffic events. The proposed technique performs a data refinement process to increase the accuracy of machine learning. We trained various machine-learning models and selected the model with the best performance. We used a binary classifier to extract data relevant to traffic conditions and a multi-class classifier to classify relevant data into six event types that influence traffic conditions: accidents, construction, congestion, weather, special events, etc. To determine where the events in the classified data occurred, we used an entity name recognition API to extract region-identifying keywords from the text. We then used a geocoding API to convert the extracted keywords to administrative districts. Our performance evaluation showed that the linear SVC model had an AUC value of 0.96 and an F-measure of 0.87 in binary classification. Additionally, the linear SVC model had  
Figure 13: F-measures with and without the “others” class.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Extraction Accuracy (\%)**} \\  & **Metropolitan Local** & **Basic Local** & **Non-Autonomous** \\  & **Government** & **Government** & **Region** \\ \hline
1 & 92 & 88 & 80 \\
2 & 92 & 84 & 78 \\
3 & 95 & 84 & 80 \\
4 & 98 & 88 & 85 \\
5 & 95 & 86 & 79 \\ Average & 94.6 & 86 & 80.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Region extraction accuracy.  
an AUC value of 0.98 and an F-measure of 0.83 in multi-class classification. Thus, the linear SVC was judged to be the most suitable model for event classification. Overall, the region extraction accuracy was 80.4%. Revising data with an interpolation technique based on entity name recognition and administrative region conversion values may improve accuracy. The proposed technique provides a fast and accurate traffic service. In future works, grouping will be performed using classified event types and administrative regions to calculate similarities between related events. We will also develop an entity name recognizer that can accurately detect regions. Furthermore, we plan to incorporate data from various social media platforms such as Facebook and Instagram.  
Conceptualization, Y.K., S.S., H.L., D.C., J.L., K.B. and J.Y.; methodology, Y.K., S.S., H.L., D.C., J.L., K.B. and J.Y.; validation, Y.K., S.S., H.L., D.C., J.L. and K.B.; formal analysis, Y.K., S.S., H.L., D.C., J.L. and K.B.; writing\(-\)original draft preparation, Y.K., S.S., H.L. and K.B.; writing\(-\)review and editing. J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2023-00245650), AURI (Korea Association of University, Research Institute and Industry) grant funded by the Korean Government (MSS: Ministry of SMEs and Startups) (No. S3047889, HRD program for 2021), MSIT (Ministry of Science and ICT) under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01462) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation), and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1A2B5B02002456).  
Not applicable.  
The authors declare no conflict of interest.