As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as inclement weather, is especially challenging for older drivers due to their sensitivity to glare and reduced visibility. As a result, older drivers may adjust their behavior during adverse weather. This paper explores the differential impacts of weather on older drivers with cognitive decline compared to older drivers with normal cognitive function. Data were from a naturalistic driving study of older drivers in Omaha, Nebraska. Driver speed and weather data were extracted and the correlation between speed compliance, road weather conditions, and the cognitive/neurological status of the drivers was examined. Speed compliance was used as the surrogate safety measure since driving at lower speeds can indicate that the driver is challenged by roadway or environmental conditions and can therefore indicate a risk. The percentage of time during a trip when drivers were 16.1 kph under the speed limit was modeled as the dependent variable using beta regression. The variables that resulted in the best fit model were mild cognitive impairment (MCI), age group, traffic density, and weather. Results indicated that the youngest group of older drivers (young-old) spent less time driving at impeding speeds and had the least variability compared to the other two age groups. The middle group of older drivers (middle-old) had the highest amount of time driving at impeding speeds and had more variability than young-old drivers. The oldest group of older drivers (old-old) were the most likely to drive at impeding speeds and had the most variability. In general, older drivers were more likely to drive at impeding speeds during peak hours than during non-peak hours. Additionally, in most cases, older drivers spent less time below the speed limit when the weather was clear than in adverse conditions. Results indicate that older drivers are impacted by weather conditions, and distinct patterns were noted between older drivers who were cognitively impaired compared to drivers with normal cognition.
References
[1]
Owsley, C. and McGwin, G. (2016) Vision-Targeted Health Related Quality of Life in Older Adults: Patient-Reported Visibility Problems in Low Luminance Activities Are More Likely to Decline than Daytime Activities. BMC Ophthalmology, 16, Article No. 92. https://doi.org/10.1186/s12886-016-0274-5
[2]
Merickel, J., High, R., Dawson, J. and Rizzo, M. (2019) Real-World Risk Exposure in Older Drivers with Cognitive and Visual Dysfunction. Traffic Injury Prevention, 20, S110-S115. https://doi.org/10.1080/15389588.2019.1688794
[3]
FHWA (2024) How Do Weather Events Impact Roads. Federal Highway Administration. https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm#:~:text=By%20crash%20type%20(not%20shown,from%202%20to%2021%20percent
[4]
IPRC (2019) Older Adult Drivers. Injury Prevention Research Center. https://iprc.public-health.uiowa.edu/2019/02/25/older-adult-drivers/
[5]
Sabback, F. and Mann, W. (2005) The Influence of Climate and Road Conditions on Driving Patterns in the Elderly Population. Physical & Occupational Therapy in Geriatrics, 23, 64-74. https://doi.org/10.1300/j148v23n02_04
[6]
Braitman, K.A. and McCartt, A.T. (2008) Characteristics of Older Drivers Who Self-Limit Their Driving. Annals of Advances in Automotive Medicine, 52, 245-254.
[7]
Clarke, P.J., Yan, T., Keusch, F. and Gallagher, N.A. (2015) The Impact of Weather on Mobility and Participation in Older US Adults. American Journal of Public Health, 105, 1489-1494. https://doi.org/10.2105/ajph.2015.302582
[8]
Allen, H.K., Beck, K.H. and Zanjani, F. (2019) Driving Concerns among Older Adults: Associations with Driving Skill, Behaviors, and Experiences. Traffic Injury Prevention, 20, 45-51. https://doi.org/10.1080/15389588.2018.1528358
[9]
Karali, S., Gyi, D.E. and Mansfield, N.J. (2016) Driving a Better Driving Experience: A Questionnaire Survey of Older Compared with Younger Drivers. Ergonomics, 60, 533-540. https://doi.org/10.1080/00140139.2016.1182648
[10]
Myers, A.M., Trang, A. and Crizzle, A.M. (2011) Naturalistic Study of Winter Driving Practices by Older Men and Women: Examination of Weather, Road Conditions, Trip Purposes, and Comfort. Canadian Journal on Aging/La Revue canadienne du vieillissement, 30, 577-589. https://doi.org/10.1017/s0714980811000481
[11]
Smith, G.A., Porter, M.M., Cull, A.W., Mazer, B.L., Myers, A.M., Naglie, G., et al. (2016) Seasonal and Weather Effects on Older Drivers’ Trip Distances. Canadian Journal on Aging/La Revue canadienne du vieillissement, 35, 59-68. https://doi.org/10.1017/s0714980816000040
[12]
Crizzle, A.M. and Myers, A.M. (2013) Examination of Naturalistic Driving Practices in Drivers with Parkinson’s Disease Compared to Age and Gender-Matched Controls. Accident Analysis & Prevention, 50, 724-731. https://doi.org/10.1016/j.aap.2012.06.025
[13]
Ferri, C.P., Prince, M., Brayne, C., Brodaty, H., Fratiglioni, L., Ganguli, M., et al. (2005) Global Prevalence of Dementia: A Delphi Consensus Study. The Lancet, 366, 2112-2117. https://doi.org/10.1016/s0140-6736(05)67889-0
[14]
Petersen, R.C., Roberts, R.O., Knopman, D.S., Geda, Y.E., Cha, R.H., Pankratz, V.S., et al. (2010) Prevalence of Mild Cognitive Impairment Is Higher in Men. Neurology, 75, 889-897. https://doi.org/10.1212/wnl.0b013e3181f11d85
[15]
Dawson, J.D., Uc, E.Y., Anderson, S.W., Johnson, A.M. and Rizzo, M. (2010) Neuropsychological Predictors of Driving Errors in Older Adults. Journal of the American Geriatrics Society, 58, 1090-1096. https://doi.org/10.1111/j.1532-5415.2010.02872.x
[16]
Uc, E.Y. (2005) Driver Landmark and Traffic Sign Identification in Early Alzheimer's Disease. Journal of Neurology, Neurosurgery & Psychiatry, 76, 764-768. https://doi.org/10.1136/jnnp.2004.049338
[17]
Aksan, N., Dawson, J.D., Emerson, J.L., Yu, L., Uc, E.Y., Anderson, S.W., et al. (2012) Naturalistic Distraction and Driving Safety in Older Drivers. Human Factors: The Journal of the Human Factors and Ergonomics Society, 55, 841-853. https://doi.org/10.1177/0018720812465769
[18]
Dawson, J.D., Anderson, S.W., Uc, E.Y., Dastrup, E. and Rizzo, M. (2009) Predictors of Driving Safety in Early Alzheimer Disease. Neurology, 72, 521-527. https://doi.org/10.1212/01.wnl.0000341931.35870.49
[19]
Freitas, S., Simões, M.R., Alves, L. and Santana, I. (2013) Montreal Cognitive Assessment: Validation Study for Mild Cognitive Impairment and Alzheimer Disease. Alzheimer Disease & Associated Disorders, 27, 37-43. https://doi.org/10.1097/wad.0b013e3182420bfe
[20]
He, K., Zhang, X., Ren, S. and Sun, J. (2016) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 770-778. https://doi.org/10.1109/cvpr.2016.90
[21]
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., et al. (2021) An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv: 2010.11929. https://doi.org/10.48550/arXiv.2010.11929
[22]
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L. and Polosukhin, I. (2017) Attention Is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, 4-9 December 2017, 6000-6010.
[23]
Touvron, H., Matthieu, C., Douze, M., Massa, F., Sablay-Rolles, A. and Jegou, H. (2021) Training Data-Efficient Image Transformers and Amp; Distillation Through Attention. Proceedings of the 38th International Conference on Machine Learning, 10347-10357.
[24]
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., et al. (2021). Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, 10-17 October 2021, 538-547. https://doi.org/10.1109/iccv48922.2021.00060
[25]
Foret, P., Kleiner, A., Mobahi, H. and Neyshabur, B. (2021) Sharpness-Aware Minimization for Efficiently Improving Generalization. International Conference on Learning Representations, Vienna, 4 May 2021.
[26]
Kingma, D. and Ba, J. (2014) Adam: A Method for Stochastic Optimization. International Conference on Learning Representations, San Diego, 7-9 May 2015, 1-13.
[27]
Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., and Feng, J. (2017) Dual Path Networks. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, 4-9 December 2017, 4470-4478.
[28]
Ruder, S. (2017) An Overview of Gradient Descent Optimization Algorithms. arXiv: 1609.04747. https://arxiv.org/abs/1609.04747
[29]
Wang, S., Sharma, A., Dawson, J., Rizzo, M. and Merickel, J. (2021) Visual and Cognitive Impairments Differentially Affect Speed Limit Compliance in Older Drivers. Journal of the American Geriatrics Society, 69, 1300-1308. https://doi.org/10.1111/jgs.17008
[30]
Feng, Y.R., Meuleners, L., Stevenson, M., Heyworth, J., Murray, K., Fraser, M., et al. (2021) The Impact of Cognition and Gender on Speeding Behaviour in Older Drivers with and without Suspected Mild Cognitive Impairment. Clinical Interventions in Aging, 16, 1473-1483. https://doi.org/10.2147/cia.s319129
[31]
Lee, S.B., Oh, J.H., Park, J.H., Choi, S.P. and Wee, J.H. (2018) Differences in Youngest-Old, Middle-Old, and Oldest-Old Patients Who Visit the Emergency Department. Clinical and Experimental Emergency Medicine, 5, 249-255. https://doi.org/10.15441/ceem.17.261
[32]
Milani, S.A., Marsiske, M., Cottler, L.B., Chen, X. and Striley, C.W. (2018) Optimal Cutoffs for the Montreal Cognitive Assessment Vary by Race and Ethnicity. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10, 773-781. https://doi.org/10.1016/j.dadm.2018.09.003
[33]
Doroudgar, S., Chuang, H.M., Perry, P.J., Thomas, K., Bohnert, K. and Canedo, J. (2016) Driving Performance Comparing Older versus Younger Drivers. Traffic Injury Prevention, 18, 41-46. https://doi.org/10.1080/15389588.2016.1194980
[34]
Horberry, T., Hartley, L., Gobetti, K., Walker, F., Johnson, B., Gersbach, S., et al. (2004) Speed Choice by Drivers: The Issue of Driving Too Slowly. Ergonomics, 47, 1561-1570. https://doi.org/10.1080/00140130412331290925
[35]
Joanisse, M., Gagnon, S. and Voloaca, M. (2012) Overly Cautious and Dangerous: An Empirical Evidence of the Older Driver Stereotypes. Accident Analysis & Prevention, 45, 802-810. https://doi.org/10.1016/j.aap.2011.11.002
[36]
Wood, S.N. (2017) Generalized Additive Models: An Introduction with R. Chapman and Hall.
[37]
The R Core Team (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
[38]
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., et al. (2019) Welcome to the Tidyverse. Journal of Open Source Software, 4, Article 1686. https://doi.org/10.21105/joss.01686
