Analyzing the Relationship Between User Feedback and Traffic Accidents Through Crowdsourced Data

Jinguk Kim, Woohoon Jeon and Seoungbum Kim ()
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Jinguk Kim: Department of Highway and Transportation Research, Korea Institute of Civil Engineering and Building Technology, 283, Goyang-daero, Ilsanseo-gu, Goyang-si 10223, Republic of Korea
Woohoon Jeon: Department of Highway and Transportation Research, Korea Institute of Civil Engineering and Building Technology, 283, Goyang-daero, Ilsanseo-gu, Goyang-si 10223, Republic of Korea
Seoungbum Kim: Department of Urban Engineering, Engineering Research Institute, Gyeongsang National University, 501, Jinju-daero, Jinju-si 52828, Republic of Korea

Sustainability, 2024, vol. 16, issue 22, 1-15

Abstract: Identifying road segments with a high crash incidence is essential for improving road safety. Conventional methods for detecting these segments rely on historical data from various sensors, which may inadequately capture rapidly changing road conditions and emerging hazards. To address these limitations, this study proposes leveraging crowdsourced data alongside historical traffic accident records to identify areas prone to crashes. By integrating real-time public observations and user feedback, the research hypothesizes that traffic accidents are more likely to occur in areas with frequent user-reported feedback. To evaluate this hypothesis, spatial autocorrelation and clustering analyses are conducted on both crowdsourced data and accident records. After defining hotspot areas based on user feedback and fatal accident records, a density analysis is performed on such hotspots. The results indicate that integrating crowdsourced data can complement traditional methods, providing a more dynamic and adaptive framework for identifying and mitigating road-related risks. Furthermore, this study demonstrates that crowdsourced data can serve as a strategic and sustainable resource for enhancing road safety and informing more effective road management practices.

Keywords: driving safety; crowdsourced data; road safety management; environmental factors; hotspot analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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