A Risk-Based Systematic Method for Identifying Fog-Related Crash Prone Locations
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Fog is one of the most influential factors in fatal crashes because of reduced visibility. This study aims to propose a systematic safety analysis framework for selecting fog-crash-prone areas on freeways. To achieve these goals, the spatial analysis in ArcGIS was combined with the latent class cluster-based crash severity estimation models. Nine latent class cluster-based crash severity estimation models were built. Fog events led to a statistically significant increase in the likelihood of fatal crashes in two of the nine models. Comparing the ArcGIS spatial clusters of fog-related exposure with the fatal crash-prone freeway segments, 28 freeway segments were found to be fog-crash-prone areas where safety improvements are required, particularly in foggy weather. Based on the spatial patterns of the fog-crash-prone freeway segments, this study concludes that the current standard for fog-crash-prone area selection should be modified to apply spatially different standards over the Korean freeway network. This study is the first data-driven study to comprehensively examine the effects of fog visibility levels and frequencies on fatal crashes in the entire Korean freeway system. The findings provide meaningful insights to the policy decision making for fog-related policy changes, highway safety enhancement and active traffic management strategies.
KeywordsFog Visibility Safety analysis framework Spatial analysis Latent class cluster Policy decision making
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [NRF-2016R1D1A1B03930700].
Compliance with Ethical Standards
Conflict of Interest
Author Soyoung Jung declares that she has no conflict of interest.
Author Xiao Qin declares that he has no conflict of interest.
Author Cheol Oh declares that he has no conflict of interest.
- Balke, K., Songchitruksa, P., Liu, H., Brydia, R., Jasek, D., & Benz, R. (2007). Concepts for managing freeway operations during weather events. FHWA, report no. FHWA/TX-07/05278–1. (Available: https://static.tti.tamu.edu/tti.tamu.edu/documents/0-5278-1.pdf). Accessed 12 July 2018
- Hosmer, D., & Lemeshow, S. (2000). Applied logistic regression, (2nd edn). John Wiley & Sons, Inc.Google Scholar
- Huang, H., Abdel-Aty, M., Ekram, A., Oloufa, A., Chen, Y., & Morrow, R. (2010). Fog- and smoke-related crashes in Florida: Identifying crash characteristics, spatial distribution, and injury severity. Transportation Research Board 89th Annual Meeting, Paper No. 10–1323.Google Scholar
- Korea Expressway Corportation. (2013). Expressway Construction. (Available: http://www.ex.co.kr/site/com/pageProcess.do).
- Korea Ministry of Land, Infrastructure, and Transport (KMLIT). (2015). Road Safety Management Strategies in Fog-prone Areas. (Available: http://www.molit.go.kr/USR/NEWS/m_71/dtl.jsp?id=95075431).
- Korea Road Traffic Authority. (2016). KoROAD Statistics (Available: http://news.koroad.or.kr/articleview.php?idx=527).
- Magidson, J., & Vermunt, J. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research, 20, 7–44.Google Scholar
- McCann, K., & Fontaine, M. (2016). Examination of the safety impacts of varying fog densities: A case study of I-77 in Virginia. Transportation Research Board 95th Annual Meeting, Paper No. 16–1867.Google Scholar
- Mehler, B., Reimer, B., Lavalliere, M., Dobres, J., & Coughlin, J. (2014). Evaluating technologies relevant to the enhancement of driver safety. Washington, DC: AAA Foundation for Traffic Safety.Google Scholar
- Perry, A.H., & Symons, L.L. (2003). Highway Meteorology, Taylor & Francis Books, Inc.Google Scholar
- Qin, X., Han, J., & Zhu, J. (2009). Spatial analysis of road weather safety data using a Bayesian hierarchical modeling approach. Advances in Transportation Studies, 18, 69–84.Google Scholar
- Ray, P., Du, X., Rivard, J. (2013). Analysis of prospective systems for fog warning. Florida Department of Transportation, Report No. BDK82 977–10, 1–78. (Available: http://www.fdot.gov/research/completed_proj/summary_te/fdot-bdk83-977-19-rpt.pdf). Accessed 12 July 2018
- Srivastava, S., Sharma, A., & Sachdeva, K. (2016). A ground observation based climatology of winter fog: Atudy over the indo-Gangetic plains, India. International Journal of Environmental, Chemical, Ecological, Geological, and Geophysical Engineering, 10(7), 678–689.Google Scholar
- Tefft, B. (2016). Motor vehicle crashes, injuries, and deaths in relation to weather conditions, United States, 2010–2014, AAA Foundation for Traffic Safety. (Available: http://www.aaafoundation.org).
- Yamamoto, A. (2002). Climatology of the traffic accident in Japan on the expressway with dense fog and a case study, 11th International Road Weather Conference (Available: http://www.sirwec.org).