Pith. sign in

REVIEW 3 major objections 6 minor 30 references

Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Adherence to the 75 mph interstate speed limit distinguishes senior from younger drivers with 77.8% accuracy in naturalistic driving data.

desk verdict The 77.8% 'accuracy' is not a classification rate—the test set has no young drivers, and the validation/test split is compromised. read the letter →

arxiv 2501.06918 v1 pith:SBAQECWE submitted 2025-01-12 stat.ME cs.CV

Aparna Joshi , Kojo Adugyamfi , Jennifer Merickel , Pujitha Gunaratne , Anuj Sharma This is my paper · ORCID
classification stat.MEcs.CV
keywords seniordriversspeedlimitadherencenaturalisticdrivingdatadecelerationatstopintersectionscumulativedistributionfunctionsadvanceddriverassistancesystemsage-relateddifferencesKolmogorov-Smirnovtest
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a single behavioral metric drawn from real-world driving data—adherence to the 75 mph interstate speed limit—can tell senior drivers apart from younger drivers. Using naturalistic driving data, the authors build baseline cumulative distribution curves for each age group and then compare individual drivers' curves against those baselines. Within the 68th to 95th percentile range, the distance comparison placed 14 of 18 senior drivers closer to the senior baseline, which the paper reports as 77.8% accuracy. The same approach applied to 65 mph adherence and to deceleration at stop intersections performed near chance. If the result holds, a simple speed-based measure could let driver assistance systems adapt their feedback to age-related driving patterns.

What carries the argument

The central device is the baseline cumulative distribution function (CDF) for a key performance index. For speed limit adherence, each driver's speed values on selected interstate segments are turned into a CDF, and a group-level baseline CDF is built for senior and young drivers separately. The machinery also uses the Kolmogorov-Smirnov distance, the maximum vertical gap between two CDFs, to decide whether the age-group baselines differ. To classify a new driver, the empirical CDF is compared against both baselines, and the driver is assigned to the closer one using an optimized percentile range—68th to 95th for the 75 mph metric. That percentile-range distance comparison is what carries the reported accuracy.

What would settle it

Run the same 68th–95th percentile distance rule on a test set that includes both senior and young drivers on the same 75 mph interstate segments, and check whether the rule correctly labels both groups; if the true binary classification rate approaches 50%, the age-differentiation claim would not hold.

Watch

Extended reading notes

Core claim

The central claim is that age-related differences in driving emerge most clearly in how drivers handle high-speed interstate travel. Baseline cumulative distribution functions for speed limit adherence at 75 mph show a statistically significant separation between senior and young drivers (Kolmogorov-Smirnov statistic 0.228, p < 2.2e-16), while the 65 mph comparison does not differ significantly. The authors therefore define a driver's age category by measuring, within the 68th to 95th percentile of the speed-adherence distribution, whether the driver's curve sits closer to the senior baseline or to the young baseline. On a separate set of senior drivers, 14 of 18 were classified as senior by this rule, giving the reported 77.8% accuracy. Deceleration at stop intersections showed a statistically significant baseline difference but only 41.6% accuracy, and 65 mph adherence achieved 44.4%, so the paper singles out 75 mph adherence as the actionable marker.

Load-bearing premise

The load-bearing premise is that measuring only senior drivers and counting how many fall closer to the senior baseline than to the young baseline yields a valid measure of age-group differentiation.

Editorial extensions

If this is right

  • Advanced driver assistance systems could use 75 mph speed-limit adherence as a real-time indicator of age-related driving style and adjust alert frequency or steering and braking assistance accordingly.
  • The 65 mph and stop-intersection deceleration benchmarks should not be used for age screening, because their accuracy falls near chance even though baseline distributions differ statistically.
  • Baseline CDFs provide an anomaly-detection tool: drivers whose speed or deceleration curves depart from the age-matched baseline could be flagged for further assessment.
  • The 68th–95th percentile range defines where the age signal lives; behavior outside that range is not informative for age classification.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves implicit that its 77.8% figure is a senior-similarity rate rather than a two-group classification rate, since no young drivers appeared in the test set; with both age groups present the reported figure would not be directly comparable.
  • A natural extension would be to turn the binary senior/young comparison into a continuous age estimate, since distances from the baselines might correlate with chronological age across the full adult range.
  • The 75 mph result should be retested on roadways with different enforcement levels and traffic densities, because the authors attribute the 65 mph null result to urban enforcement; if enforcement suppresses speed spread, the 75 mph signal might also shrink.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The manuscript proposes a CDF-based benchmark method using naturalistic driving data to compare senior and young drivers on speed-limit adherence (65/70/75 mph) and stop-intersection deceleration. After hand-curating segments and participants, it builds baseline CDFs, runs KS tests, selects an 'optimal' percentile range using a validation set of 5 seniors, and evaluates it on a testing set of 18 seniors. It reports 77.8% accuracy for 75 mph adherence, 44.4% for 65 mph, and 41.6% for deceleration, concluding that 75 mph adherence is the most effective age-differentiating metric for ADAS.

Significance. If the 77.8% figure were a genuine out-of-sample classification accuracy, the paper would offer a practically useful, low-cost behavioral marker for tailoring ADAS to older drivers. The use of naturalistic in-vehicle data and expert-informed KPI selection is a strength, as is the transparent reporting of small sample sizes. However, the evaluation protocol does not measure classification accuracy, so the central claim is not established by the evidence presented.

major comments (3)
  1. [Accuracy of Performance Metrics – Checking the metric accuracy with Testing Dataset] The reported 'accuracy' is not a classification rate. The testing step selects only senior participants ('we filtered the validation and testing dataset to include only senior participants'), so no young driver is ever presented as a negative case. The 77.8% figure is the fraction of 18 seniors whose distance to the senior baseline is smaller than their distance to the young baseline within the 68th–95th percentile range. A rule that labels every test case as 'senior' would achieve 100% under this protocol. The paper's central conclusion that 75 mph adherence 'differentiates age-related driving behavior' therefore requires a test set containing both age groups and a proper confusion-matrix evaluation.
  2. [Baseline Curve Validation / Optimization of Percentile Range Selection] The optimal percentile range is selected using 5 senior participants described as drawn from the 'validation and testing dataset' that is later used for the 18-participant test. The manuscript does not state that these 5 participants are excluded from the testing set, so the 77.8% figure may be a resubstitution estimate. Even if they are excluded, the percentile range was tuned to maximize senior-vs-young baseline separation in senior-only data, making the subsequent 'accuracy' a measure of how well seniors match a senior-fitted range rather than an independent test of age discrimination.
  3. [Anomalies Identification and Removal / Baseline Curve Development] The senior 75 mph baseline is constructed from only 3 participants and 2 segments after visually excluding one participant (SSS-DM-084) and two segments. The KS test (Table 4) then compares this hand-curated 3-participant CDF to a 7-participant young CDF. Such tiny, visually pruned baselines cannot support a population-level claim, and the visual exclusion procedure may inflate the separation between age groups. The statistically significant KS statistic does not rescue the accuracy claim, because the accuracy evaluation itself is invalid for the reasons stated above.
minor comments (6)
  1. [Accuracy of Performance Metrics – Checking the metric accuracy with Testing Dataset] The deceleration denominator is inconsistent: the text says 18 participants were selected for the deceleration study, but the result is reported as '5 out of 12 senior participants' (41.6%). Please clarify the actual count.
  2. [Conclusions and Future Avenues] The conclusions restate the central metric as '77% accuracy based on the average distance from baseline curves between the 60th and 95th percentiles,' whereas the Results section reports 77.8% accuracy in the 68th–95th percentile range. Please reconcile the number and the percentile bounds.
  3. [Results – Optimization of Percentile Range Selection] Figure references are mismatched: the text states 'Figure 6 (b) shows the CDFs developed using the validation dataset for 65 mph speed limit,' but Figure 6(b) is described earlier as the scatterplot for 75 mph. Please renumber the panels.
  4. [Data Extraction and Preparation] The age filter is written as 'senior participants (>=65) and young participants (65<)'; the second inequality should be '<65' for readability.
  5. [Methodological Framework] The paper mentions 70 mph speed limits in Table 2 and the data description, but later says a distinct 70 mph baseline was not identifiable. An explicit statement describing how 70 mph data were handled would be helpful.
  6. [Analytical Method] Equation (1) introduces n1 but its definition is incomplete, and the KS test formula is not fully integrated with the later reference to 'test statistic' in Table 4. Please clarify the notation.

Circularity Check

2 steps flagged · score 6.0 of 10

77.8% 'accuracy' is not an independent prediction: the percentile range was fitted on the same validation/testing dataset and the test set contains only senior drivers, making the central age-differentiation claim largely self-confirmatory.

  1. fitted input called prediction [Results: Optimization of Percentile Range Selection (75mph Speed Limit)]
    "We aimed to extract senior participants from the validation and testing dataset who traveled on road segments with either a 75mph or 65mph speed limit. This approach resulted in 5 subjects for each speed limit category... Next, the algorithm determined the percentile range that minimizes the distance to the senior baseline CDF while maximizing the distance to the young baseline CDF across all participants."

    The optimal percentile range is selected on the validation/testing dataset to maximize seniors' separation from the young baseline and minimize their distance to the senior baseline. The 77.8% 'accuracy' is then computed on 18 seniors filtered from the same 'validation and testing dataset,' with no statement that the 5 range-selection participants were excluded. The reported accuracy is therefore a resubstitution check of the fitted range, not an out-of-sample prediction; the test statistic is the same closeness-to-baseline criterion that was optimized.

  2. self definitional [Results: Checking the metric accuracy with Testing Dataset]
    "we filtered the validation and testing dataset to include only senior participants who traveled on the same road segments with 65mph or 75mph speed limits. This filtering process resulted in 18 participants being selected for evaluating speed limit adherence at 65mph and 75mph... The results reveal that 14 out of 18 senior participants fall within the senior driver category, as they lie below the red line within the 68th to 95th percentile range. This classification achieves an accuracy of 77.8%."

    The 'accuracy' is measured only on senior drivers; no young drivers appear in the test set to serve as negative cases. A trivial classifier that labels every driver 'senior' would score 100% under this protocol, so the 77.8% figure is not a classification or prediction rate for age differentiation. It only reports the proportion of seniors whose distance to the senior baseline is smaller than their distance to the young baseline within the preselected range—exactly the criterion used to choose that range. The central claim that 75 mph adherence can flag age for ADAS is thus equivalent to saying the fitted senior-similarity rule matches the senior baseline on senior data.

full rationale

The paper's core derivation chain—baseline CDFs, KS tests, percentile selection, and accuracy—contains no self-citation or imported uniqueness theorem. The KS test between the 75 mph senior and young baselines is an independent statistical comparison (though based on only 3 senior participants after anomaly removal), and the low accuracies for 65 mph and deceleration show the authors did not force every result. However, the headline 77.8% accuracy for 75 mph does reduce by construction. The percentile range 68th–95th was selected to maximize seniors' distance from the young baseline and minimize distance to the senior baseline using 5 seniors drawn from the 'validation and testing dataset.' The 'accuracy' is then the fraction of 18 seniors from that same dataset whose distance to the senior baseline is smaller than their distance to the young baseline within the fitted range. Because the test set contains no young drivers, the metric cannot estimate classification accuracy; a constant 'senior' label would achieve 100% on this protocol. Unless the 5 selection participants were excluded, the 77.8% is also a resubstitution estimate. These issues make the central claim substantially self-confirmatory, but they are evaluation-design circularity rather than definitional equivalence of the entire analysis. The acknowledged small-sample limitation and the 60th-vs-68th percentile inconsistency are additional robustness concerns, not circularity per se.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim depends on four fitted percentile ranges and a subjective anomaly exclusion procedure. Each percentile range is fit to a tiny validation set (5 participants), so they are effectively free parameters. The axioms are domain assumptions about data quality and validity of derived measures, plus an ad hoc assumption that visual CDF inspection does not bias the baselines. No new physical or conceptual entities are introduced.

free parameters (4)
  • 75 mph percentile range = 68th to 95th percentile
    Selected by optimizing the distance between 5 validation senior participants and the senior and young baseline CDFs, maximizing distance to young baseline and minimizing distance to senior baseline.
  • 65 mph percentile range = 36th to 52nd percentile
    Selected using the same optimization procedure on 5 validation participants at 65 mph.
  • Deceleration percentile range = 35th to 70th percentile
    Selected using the same optimization procedure on 5 validation participants for deceleration at stop intersections.
  • Anomaly exclusion criteria = Not numeric, subjective
    Segments and participants were excluded if their CDFs visually deviated from the rest, as described in 'Anomalies Identification and Removal'. This is a hand-chosen threshold that affects all baseline curves.
assumptions (4)
  • domain assumption The naturalistic driving data from UNMC registries is representative of typical senior and young driver populations in the study region.
    Used throughout the analysis to justify comparing baseline CDFs between age groups; stated in 'Dataset and Sample Description'.
  • domain assumption GIS-derived speed limits accurately reflect the posted speed limits at each GPS point.
    The dataset initially lacked posted speed limits, so limits were integrated from GIS databases and TIGER files; described in 'Dataset and Sample Description'.
  • domain assumption Deceleration calculated from speed and time is a valid proxy for braking behavior at stop intersections.
    Equation 2 defines deceleration as (v2-v1)/t, and this is used without validation against actual braking events; described in 'Data Extraction and Preparation'.
  • ad hoc to paper Visual inspection of CDFs to identify and remove anomalous segments and participants does not introduce systematic bias.
    Anomaly removal is based on visual assessment of CDFs ('Anomalies Identification and Removal'), and no formal criteria or sensitivity analysis is provided. This is a load-bearing modeling choice.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data." pith.science (2026). https://pith.science/paper/SBAQECWE

@misc{pith2026250106918,
  author       = {Pith},
  title        = {Pith review of: Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SBAQECWE}},
  note         = {Machine review of arXiv:2501.06918}
}
read the original abstract

By 2030, the senior population aged 65 and older is expected to increase by over 50%, significantly raising the number of older drivers on the road. Drivers over 70 face higher crash death rates compared to those in their forties and fifties, underscoring the importance of developing more effective safety interventions for this demographic. Although the impact of aging on driving behavior has been studied, there is limited research on how these behaviors translate into real-world driving scenarios. This study addresses this need by leveraging Naturalistic Driving Data (NDD) to analyze driving performance measures - specifically, speed limit adherence on interstates and deceleration at stop intersections, both of which may be influenced by age-related declines. Using NDD, we developed Cumulative Distribution Functions (CDFs) to establish benchmarks for key driving behaviors among senior and young drivers. Our analysis, which included anomaly detection, benchmark comparisons, and accuracy evaluations, revealed significant differences in driving patterns primarily related to speed limit adherence at 75mph. While our approach shows promising potential for enhancing Advanced Driver Assistance Systems (ADAS) by providing tailored interventions based on age-specific adherence to speed limit driving patterns, we recognize the need for additional data to refine and validate metrics for other driving behaviors. By establishing precise benchmarks for various driving performance metrics, ADAS can effectively identify anomalies, such as abrupt deceleration, which may indicate impaired driving or other safety concerns. This study lays a strong foundation for future research aimed at improving safety interventions through detailed driving behavior analysis.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 29 canonical work pages

  1. [1]

    The Impact of Cognitive Functioning on Driving Performance of Older Persons in Comparison to Younger Age Groups: A Systematic Review

    Depestele, S., Ross, V., Verstraelen, S., Brijs, K., Brijs, T., Dun, K.V., and Meesen , R.L. The Impact of Cognitive Functioning on Driving Performance of Older Persons in Comparison to Younger Age Groups: A Systematic Review. Transportation Research Part F: Traffic Psychology and Behaviour, 2020, 73: 433-452

    work page 2020

  2. [2]

    Traffic Safety Facts – Older Population 2015 Data

    National Highway Traffic Safety Administration. Traffic Safety Facts – Older Population 2015 Data. U.S. Department of Transportation, Washington, D.C., 2017

    work page 2015

  3. [3]

    Validation of a Screening Batter y to Predict Driving Fitness in People with Parkinson's Disease

    Devos, H., Vandenberghe, W., Nieuwboer, A., Tant, M., De Weerdt, W., Dawson, J.D., and Uc, E.Y. Validation of a Screening Batter y to Predict Driving Fitness in People with Parkinson's Disease. Movement Disorders, 2013, 28(5): 671-674

    work page 2013

  4. [4]

    Impaired Visual Search in Drivers with Parkinson's Disease

    Uc, E.Y., Rizzo, M., Anderson, S.W., Sparks, J., Rodnitzky, R.L., and Dawson, J.D. Impaired Visual Search in Drivers with Parkinson's Disease. Annals of Neurology, 2006, 60(4): 407-413

    work page 2006

  5. [5]

    Simulated Car Crashes at Intersections in Drivers with Alzheimer Disease

    Rizzo, M., McGehee, D.V., Dawson, J.D., and Anderson, S.N. Simulated Car Crashes at Intersections in Drivers with Alzheimer Disease. Alzheimer Disease & Associated Disorders, 2001, 15(1): 10-20

    work page 2001

  6. [6]

    Continued Trends in Older Driver Crash Involvement Rates in the United States: Data Through 2017–2018

    Cox, A.E., and Cicchino, J.B. Continued Trends in Older Driver Crash Involvement Rates in the United States: Data Through 2017–2018. Journal of Safety Research, 2021, 77: 288-295

    work page 2017

  7. [7]

    Assessing the Driving Performance of O lder Adult Drivers: On-Road Versus Simulated Driving

    Lee, H.C., Cameron, D., and Lee, A.H. Assessing the Driving Performance of O lder Adult Drivers: On-Road Versus Simulated Driving. Accident Analysis & Prevention, 2003, 35(5), 797-803

    work page 2003

  8. [8]

    Neuropsychological Predictors of Driving Errors in Older Adults

    Dawson, J.D., Uc, E.Y., Anderson, S.W., Johnson, A.M., and Rizzo, M. Neuropsychological Predictors of Driving Errors in Older Adults. Journal of the American Geriatrics Society, 2010, 58(6): 1090-1096

    work page 2010

Show all 30 references
  1. [9]

    Effect of Aging on Driving Performance

    Mouloua, M., Rinalducci, E., Smither, J., and Brill, J. Effect of Aging on Driving Performance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2004, 48: 253-257

    2004

  2. [10]

    Exploring the Impact of Driver Adherence to Speed Limits and the Interdependence of Roadside Collisions in an Urban Environment: An Agent -Based Modelling Approach

    Olmez, S., Doug las-Mann, L., Manley, E., Suchak, K., Heppenstall, A., Birks, D., and Whipp, A. Exploring the Impact of Driver Adherence to Speed Limits and the Interdependence of Roadside Collisions in an Urban Environment: An Agent -Based Modelling Approach. Applied Scie nce...

    2021

  3. [11]

    Speed and Acceleration Patterns of Younger and Older Drivers

    Nakagawa, S., Kriellaars, D., Blais, C., Montufar, J., and Porter, M.M. Speed and Acceleration Patterns of Younger and Older Drivers. Canadian Multidisciplinary Road Safety Conference, 2006

    2006

  4. [12]

    Driving Performance Comparing Older Versus Younger Drivers

    Doroudgar, S., Chuang, H.M., Pe rry, P.J., Thomas, K., Bohnert, K., and Canedo, J. Driving Performance Comparing Older Versus Younger Drivers. Traffic Injury Prevention, 2017, 18(1): 41-46

    2017

  5. [13]

    The Effect of Age on Driving Skills

    Carr, D., Jackson, T.W., Madden, D.J., and Cohen, H.J. The Effect of Age on Driving Skills. Journa l of the American Geriatrics Society, 1992, 40(6): 567-573

    1992

  6. [14]

    Effects of Age and Auditory and Visual Dual Tasks on Closed-Road Driving Performance

    Chaparro, A., Wood, J.M., and Carberry, T. Effects of Age and Auditory and Visual Dual Tasks on Closed-Road Driving Performance. Optometry and Vision Science, 2005, 82(8): 747-754

    2005

  7. [15]

    Relative Effects of Age and Compromised Vision on Driving Performance

    Szlyk, J.P., Seiple, W., and Viana, M. Relative Effects of Age and Compromised Vision on Driving Performance. Human factors, 1995, 28(1): 430-436. Joshi et al. 20

    1995

  8. [16]

    Hazard and Risk Perception Among Young Novice Drivers

    Deery, H.A. Hazard and Risk Perception Among Young Novice Drivers. Journal of Safety Research, 1999, 30(4): 225-236

    1999

  9. [17]

    Multidimensional Traffic Locus of Control Scale (T-LOC): Factor Structure and Relationship to Risky Driving

    Özkan, T., and Lajunen, T. Multidimensional Traffic Locus of Control Scale (T-LOC): Factor Structure and Relationship to Risky Driving. Personality and Individual Differences, 2005, 38(3): 533-545

    2005

  10. [18]

    Problem Driving Maneuvers of Elderly Drivers

    Chandraratna, S., and Stamatiadis, N. Problem Driving Maneuvers of Elderly Drivers. Transportation Research Record, 2003, 1843: 89-95

    2003

  11. [19]

    Investigating Lane Change Behaviors and Difficulties for Senior Drivers Using Naturalistic Driving Data

    Antin, J.F., Wotring, B., Perez, M.A., and Glaser, D. Investigating Lane Change Behaviors and Difficulties for Senior Drivers Using Naturalistic Driving Data. Journal of Safety Research, 2020, 74: 81-87. DOI: 10.1016/j.jsr.2020.04.008

    2020 doi

  12. [20]

    Characteristics of Driving Reaction Time of Elderly Drivers in the Brake Pedal Task

    Shin, H., and Lee, H. Characteristics of Driving Reaction Time of Elderly Drivers in the Brake Pedal Task. Journal of Physical Therapy Science, 2012, 24: 567-570

    2012

  13. [21]

    Collisions Involving Senior Drivers: High -Risk Conditions and Locations

    Mayhew, D.R., Simpson, H.M., and Ferguson, S.A. Collisions Involving Senior Drivers: High -Risk Conditions and Locations. Traffic Injury Prevention , 2006, 7(2): 117 -124. DOI: 10.1080/15389580600636724

    2006 doi

  14. [22]

    Examining the Environmental, Vehicle, and Driver Factors Associated with Crossing Crashes of Elderly Drivers Using Association Rules Mining

    Yang, J., Higuchi, K., Ando, R., and Nishihori, Y. Examining the Environmental, Vehicle, and Driver Factors Associated with Crossing Crashes of Elderly Drivers Using Association Rules Mining. Journal of Advanced Transportation, 2020, 2019. DOI: 10.1155/2020/2593410

    2020 doi

  15. [23]

    Comparison of Older and Middle -Aged Drivers' Driving Performance in a Naturalistic Setting

    Mazer, B., Chen, Y.T., Vrkljan, B., Marshall, S.C., Charlton, J.L., Koppel, S., and Gél inas, I. Comparison of Older and Middle -Aged Drivers' Driving Performance in a Naturalistic Setting. Accident Analysis & Prevention, 2021, 161: 106343. DOI: 10.1016/j.aap.2021.106343

    2021

  16. [24]

    Driving Characteristics of Older Drivers and Their Relationship to the Useful Field of View Test

    Dukic Willstrand, T., Broberg, T., and Selander, H. Driving Characteristics of Older Drivers and Their Relationship to the Useful Field of View Test. Gerontology, 2017, 63(2): 180 -188. DOI: 10.1159/000448281

    2017 doi

  17. [25]

    Distracted Driving in Elderly and Middle-Aged Drivers

    Thompson, K.R., Johnson, A.M., Emerson, J.L., Dawson, J.D., Boe r, E.R., and Rizzo, M. Distracted Driving in Elderly and Middle-Aged Drivers. Accident Analysis & Prevention, 2012, 45(2): 711-717. DOI: 10.1016/j.aap.2011.09.040

    2012 doi

  18. [26]

    The Reaction Times of Drivers Aged 20 to 80 During a Divided Attention Driving

    Svetina, M. The Reaction Times of Drivers Aged 20 to 80 During a Divided Attention Driving. Traffic Injury Prevention, 2016, 17(8): 810-814. DOI: 10.1080/15389588.2016.1157590

    2016

  19. [27]

    Naturalistic Distraction and Driving Safety in Older Drivers

    Aksan, N., Dawson, J.D., Emerson, J.L., Yu, L., Uc, E.Y., Anderson, S.W., and Rizzo, M. Naturalistic Distraction and Driving Safety in Older Drivers. Human Factors, 2013, 55(4): 841 -853. DOI: 10.1177/0018720812465769

    2013 doi

  20. [28]

    Older Drivers’ Visual Search Behaviour at Intersections

    Dukic, T., Broberg, T., and Broberg, T. Older Drivers’ Visual Search Behaviour at Intersections. Transportation Research Part F: Traffic Psychology and Behaviour, 2012, 15: 462-470

    2012

  21. [29]

    Older Drivers and Rapid Deceleration Events: Salisbury Eye Evaluation Driving Study

    Keay, L.J., Munoz, B., Dunc an, D.D., Hahn, D.V., Baldwin, K.C., Turano, K.A., Munro, C.A., Bandeen-Roche, K., and West, S.K. Older Drivers and Rapid Deceleration Events: Salisbury Eye Evaluation Driving Study. Accident Analysis & Prevention, 2013, 58: 279-285. Joshi et al. 21

    2013

  22. [30]

    Potentially Inappropriate Medication Use and Hard Braking Events in Older Drivers

    Xue, Y., Chihuri, S., Andrews, H.F., Betz, M.E., DiGuiseppi, C., Eby, D.W., Hill, L.L., Jones, V., Mielenz, T.J., Molnar, L.J., Strogatz, D., Lang, B.H., Kelley -Baker, T., and Li, G. Potentially Inappropriate Medication Use and Hard Braking Events in Older Drivers. Geriatrics...

    2021 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.