Driver License Renewal for the Elderly: A Case Study

Stamatiadis, Nikiforos; Agent, Kenneth R.; Ridgeway, Michael

doi:10.1177/0733464802250044

Cited by 14 publications

(11 citation statements)

References 15 publications

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“…Interestingly, a past study found nearly 25% of healthy controls failed a written test of driving laws, suggesting that even healthy older drivers may be at risk for reduced knowledge and potentially reduced driving performance [ 20 ]. When drivers reach a certain age in the United States, shortened valid licensing periods [ 26 , 27 ], in-person renewals, and vision tests are required [ 28 ]. Written knowledge tests are not yet routine and are often only administered in specific circumstances (i.e., post-traffic violations or medical reports) [ 26 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Impaired Knowledge of Driving Laws Is Associated with Recommended Driving Cessation in Cognitively Impaired Older Adults

Alosco

Ott

Cleveland

et al. 2011

Dement Geriatr Cogn Disord Extra

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Background/Aims: The present study examined if knowledge of driving laws independently predicts on-the-road driving performance among cognitively impaired older adults. Methods: The current study consisted of retrospective observational analyses on 55 cognitively impaired older adults (77.9 ± 6.4 years) that completed an on-the-road driving evaluation, a 20-item knowledge test of driving laws, and a brief cognitive test battery. Results: Logistic regression found poorer performance on the knowledge test was significantly associated with greater likelihood of recommended driving cessation beyond important demographic and cognitive variables (p < 0.05). Conclusion: Cognitively impaired patients’ ability to drive may be related to their knowledge regarding common driving laws, in addition to their current level of cognitive functioning.

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Section: Discussionmentioning

confidence: 99%

Impaired Knowledge of Driving Laws Is Associated with Recommended Driving Cessation in Cognitively Impaired Older Adults

Alosco

Ott

Cleveland

et al. 2011

Dement Geriatr Cogn Disord Extra

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“…Policies to renewal and suspend driving licenses for the elderly 8 studies in the United States, Ireland, Australia, New Zealand, and Canada referred to the process of renewing an older driver's license among the 24 included studies [8,21,30,[32][33][34][35][36]. Two studies referred to complete the driver's license renewal application form as the rst stage in the renewal process [32,34].…”

Section: General Charactristics Of Studiesmentioning

confidence: 99%

Assessing and Improving Fitness to Drive in the Elderly People: a Scoping Review of Policies and Guidelines

Mohammaddokht¹,

Saadati²,

Kashefimehr³

et al. 2021

Preprint

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Background: Several countries in the world have distinct policies and frameworks to assessment and promote fitness to drive in the elderly. Accordingly, this study has been conducted to analyze the processes, models, and policies to assessment fitness to drive and improving driving in the elderly in the world.Method: Expected data were collected using keywords older, old, elderly, aging, ageing, senior, polic*, process, programme, plan, guideline, framework, driving capacity, driving eligibility, safe drive, fitness to drive, renewal driving license, commercial driver, driving qualification, driving ability, driving evaluation, driving assessment and their Persian equivalents from various English-language databases included: PubMed, Web of Science, Scopus, Embase, and Persian-language databases Included: IranDoc, SID, and MagIran. In this study, the Arkesy and O’Malley frameworks were employed.Results: Eventually, 24 papers and reports were included in the study. Vision assessment was the main item to judge the fitness to drive in the elderly, followed by driving assessment, cognitive assessment, and motor assessment. The in-person, postal, and online renewal of license were the most common method of renewal. General practitioners were the principal performers to measure driving fitness. In most all reports and papers, there were policies on empowering the elderly driver, including providing safe driving tips, optional driving skills tests, holding retraining courses, and so on. The most significant supporting policies included introducing alternative transportation to preserve the independence of the elderly. This study explained that clinical assessment is normally performed in drivers older than 75 years old to diagnose disorders and treat and prevent them in distinct time periods.Conclusion: Most high-income countries have worthwhile experience in measuring and improving the fitness to drive in the elderly and enforce different laws according to environmental, social, and political conditions. Utilizing these experiences by considering economic and social differences can be useful and practical for middle and low-income countries.

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“…Although some claimed that they were not as effective as expected [24], it should be known that some also found the relationship between violation records and driver risk [19,22,25]. For example, Stamatiadis et al [26] found that 2.1% of licensed drivers who are charged with six or more points in the past two years accounted for 5.3% of all crashes. Additionally, traffic violations were largely found for Chinese drivers [27].…”

Section: Model Development and Analysismentioning

confidence: 99%

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework

Wang

Liu²,

et al. 2019

IJERPH

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The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subsequent two years. Using a one-year rolling time window, prediction models were developed for four consecutive time periods: 2013–2014, 2014–2015, 2015–2016, and 2016–2017. Four tree-based ensemble learning techniques were attempted, including random forest (RF), Adaboost with decision tree, gradient boosting decision tree (GBDT), and extreme gradient boosting decision tree (XGboost). A temporal transferability test and a follow-up study were applied to validate the trained models. The best scenario defining driving risk was multi-dimensional, encompassing crash recurrence, severity, and fault commitment. GBDT appeared to be the best model choice across all time periods, with an acceptable average precision (AP) of 0.68 on the most recent datasets (i.e., 2016–2017). Seven of nine top features were related to risky driving behaviors, which presented non-linear relationships with driving risk. Model transferability held within relatively short time intervals (1–2 years). Appropriate risk definition, complicated violation/crash features, and advanced machine learning techniques need to be considered for risk prediction task. The proposed machine learning approach is promising, so that safety interventions can be launched more effectively.

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Driver License Renewal for the Elderly: A Case Study

Cited by 14 publications

References 15 publications

Impaired Knowledge of Driving Laws Is Associated with Recommended Driving Cessation in Cognitively Impaired Older Adults

Impaired Knowledge of Driving Laws Is Associated with Recommended Driving Cessation in Cognitively Impaired Older Adults

Assessing and Improving Fitness to Drive in the Elderly People: a Scoping Review of Policies and Guidelines

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework

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