Elderly pedestrian safety in a rapidly aging society—Commonality and diversity between the younger-old and older-old

References

• Abay KA. Examining pedestrian-injury severity using alternative disaggregate models. Res Transp Econ. 2013;43:123–136.
   Web of Science ®Google Scholar
• Adler MW, Ahrend R. Traffic Safety in Korea: Understanding the Vulnerability of Elderly Pedestrians. Paris, France: OECD Publishing; 2017. No. 2017/3.
   Google Scholar
• Adler MW, van Ommeren JN. Does public transit reduce car travel externalities? Quasi-natural experiments’ evidence from transit strikes. J Urban Econ. 2016;92:106–119.
   Web of Science ®Google Scholar
• Aziz HA, Ukkusuri SV, Hasan S. Exploring the determinants of pedestrian–vehicle crash severity in New York City. Accid Anal Prev. 2013;50:1298–1309.
   PubMed Web of Science ®Google Scholar
• Canadian Institute for Health Information. Health Care in Canada, 2011: A Focus on Seniors and Aging. Ottawa, Canada: CIHI; 2011.
   Google Scholar
• Center for Traffic Accident Analysis. Comparison for Traffic Accident in OECD Countries. Korea Road Traffic Authority; 2017. Report No. 2017-0216-043.
   Google Scholar
• Choi S, Lee H, Choo H, Kim S. Development of severity model for elderly pedestrian accidents considering urban facility factor. Journal of the Korean Society of Safety. 2015;30:94–103.
   Google Scholar
• Daigneault G, Joly P, Frigon JY. Executive functions in the evaluation of accident risk of older drivers. J Clin Exp Neuropsychol. 2015;4:221–238.
   Google Scholar
• Dommes A, Cavallo V, Vienne F, Aillerie I. Age-related differences in street-crossing safety before and after training of older pedestrian. Accid Anal Prev. 2012;44:42–47.
   PubMed Web of Science ®Google Scholar
• Eluru N, Bhat CR, Hensher DA. A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes. Accid Anal Prev. 2008;40:1033–1054.
   PubMed Web of Science ®Google Scholar
• Haleem K, Gan A. Effect of driver’s age and side of impact on crash severity along urban freeways: a mixed logit approach. J Safety Res. 2013;46:67–76.
   PubMed Web of Science ®Google Scholar
• Holland C, Hill R. Gender differences in factors predicting unsafe crossing decisions in adult pedestrians across the lifespan: a simulation study. Accid Anal Prev. 2010;42:1097–1106.
   PubMed Web of Science ®Google Scholar
• Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: Wiley-Interscience; 2000.
   Google Scholar
• Huang C, Shields TG. Interpretation of interaction effects in logit and probit analyses. Am Polit Rec. 2000;28:80–95.
   Google Scholar
• Kim JK, Ulfarsson GF, Shankar VN, Kim S. Age and pedestrian injury severity in motor-vehicle crashes: a heteroskedastic logit analysis. Accid Anal Prev. 2008;40:1695–1702.
   PubMed Web of Science ®Google Scholar
• Kim M, Kho SY, Kim DK. Hierarchical ordered model for injury severity of pedestrian crashes in South Korea. J Safety Res. 2017;61:33–40.
   PubMed Web of Science ®Google Scholar
• Kockelman KM, Kweon YJ. Driver injury severity: an application of ordered probit models. Accid Anal Prev. 2002;34:313–321.
   PubMed Web of Science ®Google Scholar
• Korea Road Traffic Authority. Traffic Accident Statistical Database. 2016. Available at: http://taas.koroad.or.kr/sta/acs/exs/typical.do/menuId=WEB_KMP_STA_UAS_ASA. Accessed February 1, 2018.
   Google Scholar
• Lee C, Abdel-Aty M. Comprehensive analysis of vehicle–pedestrian crashes at intersections in Florida. Accid Anal Prev. 2005;37:775–786.
   PubMed Web of Science ®Google Scholar
• Li G, Braver ER, Chen LH. Fragility versus excessive crash involvement as determinants of high death rates per vehicle-mile of travel among older drivers. Accid Anal Prev. 2003;35:227–235.
   PubMed Web of Science ®Google Scholar
• Mitchell RJ, Bamback MR, Foster K, Curtis K. Risk factors associated with the severity of injury outcome for paediatric road trauma. Injury, 2015;46:874–882.
   PubMed Web of Science ®Google Scholar
• Mohamed MG, Saunier N, Miranda-Moreno LF, Ukkusuri SV. A clustering regression approach: a comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US, and Montreal, Canada. Saf Sci. 2013;54:27–37.
   Web of Science ®Google Scholar
• Montella A, Aria M, D’Ambrosio A, Mauriello F. Data-mining techniques for exploratory analysis of pedestrian crashes. Transp Res Rec. 2011;2237:107–116.
   Web of Science ®Google Scholar
• Neyens DM, Boyle LN. influence of driver distraction on the severity of injuries sustained by teenage drivers and their passengers. Accid Anal Prev. 2008;40:254–259.
   PubMed Web of Science ®Google Scholar
• Noh Y, Yoon Y. Elderly road collision injury outcomes associated with seat positions and seatbelt use in a rapidly aging society—a case study in South Korea. PLoS ONE. 2017;12(8):e0183043.
   PubMed Web of Science ®Google Scholar
• Norton EC, Wang H, Ai C. Computing interaction effects and standard errors in logit and probit models. Stata J. 2004;4(2):154–167.
   Web of Science ®Google Scholar
• Organization for Economic Cooperation and Development. The OECD Statistics. 2017a. Available at: http://stats.oecd.org. Accessed December 11, 2017.
   Google Scholar
• Organization for Economic Cooperation and Development. Pensions at a Glance 2017: OECD and G20 Indicators. Paris, France: OECD Publishing; 2017b.
   Google Scholar
• Park S, Jang K, Park SH, Kim DK, Chon KS. Analysis of injury severity in traffic crashes: a case study of Korean expressways. KSCE Journal of Civil Engineering. 2012;16:1280–1288.
   Web of Science ®Google Scholar
• Pour-Rouholamin M, Zhou H. Investigating the risk factors associated with pedestrian injury severity in Illinois. Jf Safety Res. 2016;57:9–17.
   PubMed Web of Science ®Google Scholar
• Savolainen P, Mannering F. Probabilistic models of motorcyclists’ injury severities in single- and multi-vehicle crashes. Accid Anal Prev. 2007;39:955–963.
   PubMed Web of Science ®Google Scholar
• Singleton M, Qin H, Luan J. Factors associated with higher levels of injury severity in occupants of motor vehicles that were severely damaged in traffic crashes in Kentucky, 2000–2001. Traffic Inj Prev. 2004;5(2):144–150.
   PubMedGoogle Scholar
• Siram SM, Sonaike V, Bolorunduro OB, et al. Does the pattern of injury in elderly pedestrian trauma mirror that of the younger pedestrian? J Surg Res. 2011;167:14–18.
   PubMed Web of Science ®Google Scholar
• Sze NN, Wong SC. Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accid Anal Prev. 2007;39:1267–1278.
   PubMed Web of Science ®Google Scholar
• Tay R, Choi J, Kattan L, Khan A. A multinomial logit model of pedestrian–vehicle crash severity. Int J Sustain Transp. 2011;5(4):233–249.
   Web of Science ®Google Scholar
• Ulfarsson G, Mannering F. Difference in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents. Accid Anal Prev. 2004;36(2):135–147.
   PubMed Web of Science ®Google Scholar
• United Nations, Department of Economic and Social Affairs, Population Division. World Population Ageing 2015. New York, NY: United Nations; 2015. ST/ESA/SER.A/390.
   Google Scholar
• Wang Y, Haque MM, Chin HC. Elderly pedestrian injuries in Singapore. J Transp Saf Secur. 2017;9(3):273–300.
   Web of Science ®Google Scholar
• Washington SP, Karlaftis MG, Mannering F. Statistical and Econometric Methods for Transportation Data Analysis. Boca Raton, FL: CRC Press; 2010.
   Google Scholar
• World Health Organization. World Report on Ageing and Health. Geneva, Switzerland: Author; 2015.
   Google Scholar
• Yamamoto T, Hashiji J, Shankar VN. Underreporting in traffic accident data, bias in parameters and the structure of injury severity models. Accid Anal Prev. 2008;40:1320–1329.
   PubMed Web of Science ®Google Scholar
• Zhang Z. Model building strategy for logistic regression: purposeful selection. Ann Transl Med. 2016;4(6):111.
   PubMed Web of Science ®Google Scholar