Abstract
This study attempted to identify factors associated with driving behavior of elderly drivers to assess their safety and estimate their risk during naturalistic driving. We performed binomial logistic regression using self-reported past crash involvement as a response variable to identify critical factors and provided an estimation model has 18 variables. However, applying driver category based on crash and near-crash events (CNCs) collected from naturalistic driving study employed on-dash cam instead of self-reported crash involvement to the previous model showed lower predictive performance (0.63 for sensitivity and 0.51 for specificity). This implies that the model based on self-reported crash experiences was difficult to detect for drivers with CNC during naturalistic driving. Then, we performed binomial logistic regression based on CNC involvement and indicated another model, where the predictive performance was improved, with 0.81 for sensitivity and 0.70 for specificity. To predict the number of CNCs as drivers’ risk, this study adopted Poisson regression analysis using nine variables selected from the second model. The analyses showed a plausible model and significant variables for the estimation of CNCs. Mini-Mental State Examination (MMSE) was one of the better predictor putting in this model, and showed the probability that lower performance associated with higher number of CNCs. This model for CNC estimation would be helpful for the development of safety programs for elderly drivers with possible incidents.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Neale LV, Dingus AT, Klauer GS, Sudweeks J, Goodman M (2005) An overview of the 100-car naturalistic study and findings. National highway traffic safety administration paper number 05-0400 (2005)
Guo F, Fang Y (2013) Individual driver risk assessment using naturalistic driving data. Accid Anal Prev 61:3–9
Guo F, Fang Y, Antin FJ (2015) Older driver fitness-to-drive evaluation using naturalistic driving data. J Saf Res 54:49–54
Huisingh C, Levitan BL, Marguerite RI, Maclennan P, Wadley V, Owsley C (2017) Visual sensory and visual-cognitive function and rate of crash and near-crash involvement among older drivers using naturalistic driving data. Invest Ophthalmol Vis Sci 58(7):2959–2967
Anstey JK, Wood J, Lord S, Walker GJ (2005) Cognitive, sensory and physical factors enabling driving safety in older adults. Clin Psychol Rev 25(1):45–65
Suto S, Kumada T (2010) Effects of age-related decline of visual attention, working memory and planning functions on use of IT-equipment. Jpn Psychol Res 52(3): 201–215
TransAnalytics Health & Safety Services. DrivingHealth.com. http://drivinghealth.com/. Accessed 18 Apr 2018
Akamatsu M, Hayama K, Takahashi J, Iwasaki A, Daigo H (2006) Cognitive and physical factors in changes to the automobile driving ability of elderly people and their mobility life: Questionnaire survey in various regions of Japan. IATSS Res 30(1):38–51
Sato T, Akamatsu M, Aoki H, Kanamori H, Yamagishi M (2016) Relations between elderly drivers cognitive functions and their compensatory driving behaviors. Humanist 5th Conference
Stanton AN, Landry S, Di Bucchianico G, Vallicelli, A (2014) Advances in human aspects of transportation. In: Proceedings of the AHFE 2014 International Conference on Human Factors in Transportation
Ball K, Owsley C (1991) Identifying correlated of accident involvement for the older driver. Hum Factor 33(5):583–595
Acknowledgement
This study was conducted with the support of the “Center of Innovation (COI) program,” which is part of the research result institute of the Japan Science and Technology Agency (JST.) We have also listed the members who provided their cooperation with the collection and analysis of the DR data as a way to express our sincere gratitude.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yamagishi, M., Yonekawa, T., Inagami, M., Sato, T., Aakamatsu, M., Aoki, H. (2019). Identifying Factors Related to the Estimation of Near-Crash Events of Elderly Drivers. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 826. Springer, Cham. https://doi.org/10.1007/978-3-319-96065-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-96065-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96064-7
Online ISBN: 978-3-319-96065-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)