Generalized ordered logit analysis on the injury severity of traffic crashes by older drivers: the case of South Korea

References

• Abellán, J., Lopez, G., & Oñab, J. D. (2013). Analysis of traffic accident severity using decision rules via decision trees. Expert Systems with Applications, 40(15), 6047–6054.
   Web of Science ®Google Scholar
• Agresti, A. (2002). Categorical data analysis. Hoboken, NJ: John Wiley and Sons.
   Google Scholar
• Aguero-Valverde, J. (2013). Multivariate spatial models of excess crash frequency at area level: Case of Costa Rica. Accident Analysis and Prevention, 59, 365–373.
   PubMed Web of Science ®Google Scholar
• Baker, T. K., Falb, T., Voas, R., & Lacey, J. (2003). Older women drivers: Fatal crashes in good conditions. Journal of Safety Research, 34, 399–405.
   PubMed Web of Science ®Google Scholar
• Bayam, E., Liebowitz, J., & Agresti, W. (2005). Older drivers and accidents: A meta analysis and data mining application on traffic accident data. Expert Systems with Applications, 29, 598–629.
   Web of Science ®Google Scholar
• Best, H., & Wolf, C. (2014). The SAGE handbook of regression analysis and causal inference. Thousand Oaks, CA: Sage.
   Google Scholar
• Böcker, L., Amen, P. V., & Helbich, M. (2017). Elderly travel frequencies and transport mode choices in Greater Rotterdam, the Netherlands. Transportation, 44(4), 831–852.
   Web of Science ®Google Scholar
• Cerrelli, E. C. (1995). Crash data and rates for age–sex groups of drivers. Washington, DC: National Center for Statistics and Analyses, National Highway Administration.
   Google Scholar
• Choi, J., & Kim, J. (2016). A study on older driver safety policy through fatality risk analysis. Anyang, South Korea: Korea Research Institute for Human Settlements.
   Google Scholar
• Collia, D. V., Sharp, J., & Giesbrecht, L. (2003). The 2001 national household travel survey: A look into the travel pattern of older driver Americans. Journal of Safety Research, 34, 461–470.
   PubMed Web of Science ®Google Scholar
• Cunningham, C., Howard, D., Walsh, J., Coakley, D., & O’Neill, D. (2001). The effects of age on accident severity and outcome in Irish road traffic accident patients. Irish Medical Journal, 94(6), 169–171.
   PubMedGoogle Scholar
• Duc-Nghiem, N., Hoang-Tung, N., Kojima, A., & Kubota, H. (2016). Traffic safety of elderly road user: The global trend and the Japanese case. Journal of Japan Society of Civil Engineers D3: Infrastructure Planning and Management, 72(5S), 1249–1264.
   Google Scholar
• Dziak, J. J., Coffman, D. L., Lanza, S. T., & Li, R. (2017). Sensitivity and specificity of information criteria. PeerJ Preprints, 5, e1103. doi:10.7287/peerj.preprints.1103v3
   Google Scholar
• Eby, D. W., & Molnar, L. J. (2012). Has the time come for an older driver vehicle? Ann Arbor, MI: University of Michigan Transportation Research Institute.
   Google Scholar
• Eluru, N., & Yasmin, S. (2015). A note on generalized ordered outcome models. Analytic Methods in Accident Research, 8, 1–6.
   Google Scholar
• Etehad, H., Yousefzadeh-Chabok, S., Davoudi-Kiakalaye, A., Moghadam, D. A., Hemati, H., & Mohtasham-Amiri, Z. (2015). Impact of road traffic accidents on the elderly. Archives of Gerontology and Geriatrics, 61(3), 489–493.
   PubMed Web of Science ®Google Scholar
• Evans, L. (2001). Age and fatality risk from similar severity impacts. Journal of Traffic Medicine, 29, 10–19.
   Google Scholar
• Hakamies-Blomqvist, L., Sirén, A., & Davidse, R. (2004). Older drivers – a review. Linköping, Sweden: Swedish National Road and Transport Research Institute (VTI).
   Google Scholar
• Haleem, K., & Gan, A. (2013). Effect of driver’s age and side of impact on crash severity along urban freeways: A mixed logit approach. Journal of Safety Research, 46, 67–76.
   PubMed Web of Science ®Google Scholar
• Hosseinzadeh, A., & Kluger, R. (2021). Do EMS times associate with injury severity? Accident Analysis & Prevention, 153, 106053. doi:10.1016/j.aap.2021.106053
   PubMed Web of Science ®Google Scholar
• Huang, H., Abdel-Aty, M. A., & Darwiche, A. L. (2010). County-level crash risk analysis in Florida. Transportation Research Record, 2148, 27–37.
   Web of Science ®Google Scholar
• Hwang, G., Choi, J., Lee, S., Heo, T., Jo, W., & Kim, Y. (2010). Freeway crash frequency model development based on the road section segmentation by using vehicle speeds. Journal of Korean Society of Transportation, 28(2), 151–159.
   Google Scholar
• IRTAD (International Traffic Safety Data and Analysis Group). (2011). Road safety annual report 2010. Paris: Organisation for Economic Co-operation and Development and International Transport Forum.
   Google Scholar
• IRTAD (International Traffic Safety Data and Analysis Group). (2018). Road safety annual report 2018. Paris: Organisation for Economic Co-operation and Development and International Transport Forum.
   Google Scholar
• Jang, J., Choi, J., & Gim, T.-H. T. (2017). Analyzing driving environment effects on severity of elderly driver’s traffic accidents. Journal of Transport Research, 24(1), 1–15.
   Google Scholar
• Karim, M. A., Wahba, M. M., & Sayed, T. (2013). Spatial effects on zone-level collision prediction models. Transportation Research Record, 2398, 50–59.
   Google Scholar
• Keskinen, E., Ota, H., & Katila, A. (1998). Older drivers fail in intersections: Speed discrepancies between older and younger male drivers. Accident Analysis and Prevention, 30(3), 323–330.
   PubMed Web of Science ®Google Scholar
• Khattak, A. J., Pawlovich, M. D., Souleyrette, R. R., & Hallmark, S. L. (2002). Factors related to more severe older driver traffic crash injuries. Journal of Transportation Engineering, 128, 243–249.
   Web of Science ®Google Scholar
• Kim, W., Cho, B., Park, C., & Ryu, J. (2013). A study on improving traffic safety for Chungnam elderly. Gongju, South Korea: ChungNam Institute.
   Google Scholar
• Korea Institute of Construction Technology. (2013). A basic study on road design guidelines for preparing for an aging society. Sejong, South Korea: Korean Ministry of Land, Infrastructure and Transport.
   Google Scholar
• Lee, H., Geum, G., & Son, S. (2011). A study on the factor analysis by grade for highway traffic accident. International Journal of Highway Engineering, 13(3), 157–165.
   Google Scholar
• Lee, J., & Gim, T.-H. T. (2021). Analysing the injury severity characteristics of urban elderly drivers’ traffic accidents through the generalised ordered logit model: A case of Seoul, South Korea. Journal of Transportation Safety & Security, 1–25. doi:10.1080/19439962.2021.1898068
   Web of Science ®Google Scholar
• Lee, J. Y., Chung, J. H., & Son, B. S. (2008a). Analysis of traffic accident severity for Korean highway using structural equations model. Journal of Korean Society of Transportation, 26(2), 17–24.
   Google Scholar
• Lee, J. Y., Chung, J. H., & Son, B. S. (2008b). Analysis of traffic accident size for Korean highway using structural equation models. Accident Analysis and Prevention, 40(6), 1955–1963.
   PubMed Web of Science ®Google Scholar
• Lee, M., & Lee, M. S. (2014). Older driver’s perceived driving ability and driving behavior associated with traffic accident risk. Crisisonomy, 10(12), 279–304.
   Google Scholar
• Lee, Y., Kim, Y., Kim, D., & Lim, K.-W. (2000). Analysis of seasonal variation effect of the traffic accidents on freeway. Journal of Korean Society of Transportation, 18(5), 7–16.
   Google Scholar
• Lim, S., Park, J., Kim, Y., & Kim, T. (2012). Analysis of older drivers’ accident models considering operations and physical characteristics. Journal of Korean Society of Transportation, 30(6), 37–46.
   Google Scholar
• Liu, C., Utter, D., & Chen, C. (2007). Characteristics of crash injuries among young, middle-aged, and older drivers. Washington, DC: National Highway Traffic Safety Administration.
   Google Scholar
• Liu, X., & Koirala, H. (2012). Ordinal regression analysis: Using generalized ordinal logistic regression models to estimate educational data. Journal of Modern Applied Statistical Methods, 11(1), 242–254.
   Google Scholar
• Long, J. S. (2014). Regression models for nominal and ordinal outcomes. In H. Best, & C. Wolf (Eds.), The SAGE handbook of regression analysis and causal inference (pp. 173–204). Thousand Oaks, CA: Sage.
   Google Scholar
• Lyman, S., Ferguson, S. A., Braver, E. R., & Williams, A. F. (2002). Older driver involvements in police reported crashes and fatal crashes: Trends and projections. Injury Prevention, 8(2), 116–120.
   PubMed Web of Science ®Google Scholar
• Mayhew, D. R., Simpson, H. M., & Ferguson, S. A. (2006). Collisions involving senior drivers: High-risk conditions and locations. Traffic Injury Prevention, 7(2), 117–124.
   PubMedGoogle Scholar
• McGwin, J. G., & Brown, D. B. (1999). Characteristics of traffic crashes among young, middle-aged, and older drivers. Accident Analysis and Prevention, 31(3), 181–198.
   PubMed Web of Science ®Google Scholar
• National Highway Traffic Safety Administration. (2013). Traffic safety for older people–5-year plan.
   Google Scholar
• O’Connell, A. A. (2006). Logistic regression models for ordinal response variables. Thousand Oaks, CA: Sage.
   Google Scholar
• Oh, J.-S., Lee, E.-Y., Ryu, J.-B., & Lee, W.-Y. (2015). An analysis for main vulnerable situations and human errors of older drivers’ traffic accidents. Journal of Transport Research, 22(4), 57–75.
   Google Scholar
• Park, S. J., Kho, S.-Y., & Park, H.-C. (2019). The effects of road geometry on the injury severity of expressway traffic accident depending on weather conditions. Journal of the Korea Institute of Intelligent Transport Systems, 18(2), 12–28. doi:10.12815/kits.2019.18.2.12
   Google Scholar
• Quddus, M. A. (2008). Modeling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data. Accident Analysis and Prevention, 40(4), 1486–1497.
   PubMed Web of Science ®Google Scholar
• Ratanavaraha, V., & Suangka, S. (2014). Impacts of accident severity factors and loss values of crashes on expressways in Thailand. IATSS Research, 37(2), 130–136.
   Google Scholar
• Rezapour, M., Moomen, M., & Ksaibati, K. (2019). Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming. Journal of Safety Research, 68, 107–118. doi:10.1016/j.jsr.2018.12.006
   PubMed Web of Science ®Google Scholar
• Richmond, R., Aldaghlas, T. A., Burke, C., Rizzo, A. G., Griffen, M., & Pullarkat, R. (2011). Age: Is it all in the head? Factors influencing mortality in elderly patients with head injuries. Journal of Trauma, 71, E8–E11.
   PubMedGoogle Scholar
• Wang, C., Quddus, M. A., & Ison, S. G. (2013). The effect of traffic and road characteristics on road safety: A review and future research direction. Safety Science, 57, 264–275.
   Web of Science ®Google Scholar
• Wang, C. C., & Carr, D. B. (2004). Older driver safety: A report from the older drivers project. Journal of the American Geriatrics Society, 52(1), 143–149.
   PubMed Web of Science ®Google Scholar
• Williams, R. (2016). Understanding and interpreting generalized ordered logit models. Journal of Mathematical Sociology, 40(1), 7–20.
   Web of Science ®Google Scholar
• Williams, R. A. (2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. Stata Journal, 6(1), 58–82.
   Web of Science ®Google Scholar
• Won, M., Lee, G., Oh, C., & Kang, K.-W. (2009). A study on the application of accident severity prediction model. Journal of Korean Society of Transportation, 27(4), 167–173.
   Google Scholar
• Yee, W. Y., Cameron, P. A., & Bailey, M. J. (2006). Road traffic injuries in the elderly. Emergency Medicine Journal, 23, 42–46.
   PubMed Web of Science ®Google Scholar
• Yu, J., & Choi, G. (2013). A comparative study on the characteristics of the elderly and non-elderly drivers’ traffic accidents: Focusing on business automobiles. Transportation Technology and Policy, 10(5), 11–25.
   Google Scholar
• Zhang, J., Lindsay, J., Clarke, K., Robbins, G., & Mao, Y. (2000). Factors affecting the severity of motor vehicle traffic crashes involving elderly drivers in Ontario. Accident Analysis and Prevention, 32(1), 117–125.
   PubMed Web of Science ®Google Scholar