Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach
In contemporary society, the rapid progression of urbanization and technological advancements has led to a substantial increase in the number of vehicles, consequently elevating the rate of traffic accidents. To minimize the human and economic losses resulting from these accidents, extensive researc...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10746398/ |
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| author | Ji-Woong Yang Hyeon-Jin Jung Tae-Wook Kim Han-Jin Lee Ellen J. Hong |
| author_facet | Ji-Woong Yang Hyeon-Jin Jung Tae-Wook Kim Han-Jin Lee Ellen J. Hong |
| author_sort | Ji-Woong Yang |
| collection | DOAJ |
| description | In contemporary society, the rapid progression of urbanization and technological advancements has led to a substantial increase in the number of vehicles, consequently elevating the rate of traffic accidents. To minimize the human and economic losses resulting from these accidents, extensive research has been conducted over the past decades. Recent studies utilizing Grid Maps and time-series methods have shown promising results in identifying factors related to traffic accidents and predicting accident occurrence rates. However, most existing research employs data without thoroughly analyzing its definitive correlation with traffic accidents, focusing solely on an overarching integration process or exclusively considering road conditions, while neglecting environmental factors surrounding the roads. Therefore, this paper focuses on a detailed analysis of factors contributing to traffic accidents in Seoul, the capital city of South Korea, limiting the study period from January 2020 to December 2021. The data encompasses various aspects such as traffic accidents, weather conditions, standard node links, traffic speed, Points of Interest (POI), solar altitude and azimuth, speed bumps, and traffic surveillance cameras. Most of this data is available from national public institutions, with some being computed through specific formulas. Since weather and POI data possess a wide range of features, a Pearson correlation analysis is conducted to extract features relevant to traffic accidents. The extracted features are then compiled and normalized to facilitate the deep learning model’s training, thereby constructing a comprehensive dataset. |
| format | Article |
| id | doaj-art-c0a75eb518d04721a106d19c7a557f92 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c0a75eb518d04721a106d19c7a557f922025-01-29T00:01:17ZengIEEEIEEE Access2169-35362025-01-0113159171592810.1109/ACCESS.2024.349313810746398Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction ApproachJi-Woong Yang0https://orcid.org/0009-0000-2412-2959Hyeon-Jin Jung1https://orcid.org/0009-0007-7560-5767Tae-Wook Kim2https://orcid.org/0009-0000-4422-5488Han-Jin Lee3https://orcid.org/0009-0009-0826-9833Ellen J. Hong4https://orcid.org/0000-0002-0948-9944Department of Artificial Intelligence Semiconductor, Hanyang University, Seoul, South KoreaDepartment of Computer Science, Yonsei University, Wonju, South KoreaDivision of Software, Yonsei University, Wonju, South KoreaDepartment of Computer Science, Yonsei University, Wonju, South KoreaDivision of Software, Yonsei University, Wonju, South KoreaIn contemporary society, the rapid progression of urbanization and technological advancements has led to a substantial increase in the number of vehicles, consequently elevating the rate of traffic accidents. To minimize the human and economic losses resulting from these accidents, extensive research has been conducted over the past decades. Recent studies utilizing Grid Maps and time-series methods have shown promising results in identifying factors related to traffic accidents and predicting accident occurrence rates. However, most existing research employs data without thoroughly analyzing its definitive correlation with traffic accidents, focusing solely on an overarching integration process or exclusively considering road conditions, while neglecting environmental factors surrounding the roads. Therefore, this paper focuses on a detailed analysis of factors contributing to traffic accidents in Seoul, the capital city of South Korea, limiting the study period from January 2020 to December 2021. The data encompasses various aspects such as traffic accidents, weather conditions, standard node links, traffic speed, Points of Interest (POI), solar altitude and azimuth, speed bumps, and traffic surveillance cameras. Most of this data is available from national public institutions, with some being computed through specific formulas. Since weather and POI data possess a wide range of features, a Pearson correlation analysis is conducted to extract features relevant to traffic accidents. The extracted features are then compiled and normalized to facilitate the deep learning model’s training, thereby constructing a comprehensive dataset.https://ieeexplore.ieee.org/document/10746398/Traffic accidentsdataset constructioncorrelation analysisdata preprocessingdeep learning |
| spellingShingle | Ji-Woong Yang Hyeon-Jin Jung Tae-Wook Kim Han-Jin Lee Ellen J. Hong Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach IEEE Access Traffic accidents dataset construction correlation analysis data preprocessing deep learning |
| title | Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach |
| title_full | Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach |
| title_fullStr | Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach |
| title_full_unstemmed | Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach |
| title_short | Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach |
| title_sort | predicting traffic accident risk in seoul metropolitan city a dataset construction approach |
| topic | Traffic accidents dataset construction correlation analysis data preprocessing deep learning |
| url | https://ieeexplore.ieee.org/document/10746398/ |
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