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http://dx.doi.org/10.7470/jkst.2012.30.6.037

Analysis of Elderly Drivers' Accident Models Considering Operations and Physical Characteristics  

Lim, Sam Jin (Korea Railway Association)
Park, Jun Tae (Korea Railway Association)
Kim, Young Il (Graduate School of Urban Studies, Hanyang University)
Kim, Tae Ho (Hyundai Insurance Research Center)
Publication Information
Journal of Korean Society of Transportation / v.30, no.6, 2012 , pp. 37-46 More about this Journal
Abstract
The number of traffic accidents caused by elderly drivers over the age of 65 has surged over the past ten years from 37,000 to 274,000 cases. The proportion of elderly drivers' accidents has jumped 3.1 times from 1.2% to 3.7% out of all traffic accidents, and traffic safety organizations are pursuing diverse measures to address the situation. Above all, connecting safety measures with an in-depth research on behavioral and physical characteristics of elderly drivers will prove vital. This study conducted an empirical research linking the driving characteristics and traffic accidents by elderly drivers based on the Driving Aptitude Test items and traffic accident data, which enabled the measurement of behavioral characteristics of elderly drivers. In developing the Influence Model, we applied the zero-inflated Poisson (ZIP) regression model and selected an accident prediction model based on the Bayesian Influence in regards to the ZIP regression model and the zero-inflated negative binomial (ZINB) regression model. According to the results of the AAE analysis, the ZIP regression model was more appropriate and it was found that three variables? prediction of velocity, diversion, and cognitive ability? had a relation of influence with traffic accidents caused by elderly drivers.
Keywords
Bayes Factors; Driving Test; Elderly Driver; Traffic Accident; Zero-Inflated;
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1 Abdel-Aty M., (2003), Analysis of driver injury severity levels at multiple locations using ordered probit models. Journal of Safety Research 34, pp.597- 603.   DOI   ScienceOn
2 C. J. O'Donnell, et al. (1996), Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice, Accident Analysis & Prevention, Vol.28, pp.739-753.   DOI   ScienceOn
3 Ceccarini Olivia (2008), Does experience rating matter in reducing accident probabilities? A test for moral hazard, University of Pennsylvania.
4 Ciro Caliendo, et al. (2007), A crash-prediction model for multilane roads, Accident Analysis & Prevention, Vol.39, pp.657-670.   DOI   ScienceOn
5 Dobbins D. A., Tiedemann J. G., & Skordahl D. M. (1963), Vigilance under highway driving conditions, perceptual and Motor Skill, pp.16-38.
6 Evans L. (1991), Traffic Safety and the Driver, New York, van nostrand.
7 Kara Maria Kockelman, et al. (2002), Driver injury severity: an application of ordered probit models, Accident Analysis & Prevention, Vol.34, pp.313-321.   DOI   ScienceOn
8 Kelvin K. W. Yau (2004), Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong, Accident Analysis & Prevention, Vol.36, pp.333-340.   DOI   ScienceOn
9 L. Staplin, et al., (1990), Traffic control design elements for accommodation drivers with diminished capacity, Final technical report, US DOT, Federal Highway Administration, Ketron, Inc.
10 Lim A. K., et al., (2006), Bayesian Analysis of a Zeroinflated Poisson Regression Model: An Application to Korean Oral Hygienic Data, The Korean Journal of Applied Statistics, vol.19 No.3 pp.505-519   DOI   ScienceOn
11 Lim C. S., (2009), Factors Affecting Traffic Accident Occurrence Rate PUKYONG NATIONAL University, Ph.. D. Degree
12 Lim S. H., (2009), Analysis on the Running Safety and Investment Effect by Severity Model of Big Traffic Accidents. MOKWON University, Ph.. D. Degree
13 Li-Yen Chang, Hsiu-Wen Wang (2006), Analysis of traffic injury severity: An application of non-parametric classification tree techniques, Accident Analysis & Prevention, Vol.38, pp.1019-1027.   DOI   ScienceOn
14 Malaterre G. (1990), Error analysis and in-depth accident studies, Ergonomics, pp.1403-1421.
15 Maria Staubach (2009), Factors correlated with traffic accidents as a basis for evaluating Advanced Driver Assistance Systems, Accident Analysis & Prevention, Vol.41, Issue 5, pp.1025-1033.   DOI   ScienceOn
16 Myoung M. H., (2008), A Study on the Noncompliance of Traffic Safety Regulatory Policy. SUNGKYUNKWAN University, Ph.. D. Degree`
17 Olson P. L., Sivak M., (1986), Perception-response time to unexpected roadway hazards. Human Factors, 28, pp.96-99.
18 Parasuraman, R., Nestor, P.G., (1991), Attention and Driving Skills in Aging and Alzheimer's Desease, HUMAN FACTORS, 33(5), pp.539-557.
19 Shankar V. N., et al. (1997), Modeling accident frequencies as zero-altered probability processes: An empirical inquiry. Accident Analysis and Prevention, 29(6), pp.829-837.   DOI   ScienceOn
20 Treat, J. R., et al, (1997), Tri-level Study of the Cause of Traffic Accident, Report No. DOT-HS-034-3-535-77 (TAC), Indiana Univ.
21 Yoon B. J., (2006), Development of Traffic Accident Forecasting Models by Interchange Ramp Types of Freeway. YONSEI University, Ph.. D. Degree