Epidemiology of older driver crashes – Identifying older driver risk factors and exposure patterns

Langford, James William; Koppel, Sjaan

doi:10.1016/j.trf.2006.03.005

Cited by 163 publications

(94 citation statements)

References 16 publications

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“…Logistic regression modeling was used to compute the likelihood of an injury versus impact severity. Several predictor variables that have been identified as influencing injury risk were included, such as total delta-V (Kononen et al 2011;Kusano and Gabler 2012), seat belt use (Durbin et al 2003(Durbin et al , 2005, driver age (Langford and Koppel 2006;McCoy et al 1989), driver gender (Beck et al 2007;Mock et al 2002;Yau 2004), and whether or not the vehicle was equipped with side airbags (Arbelaez et al 2002;Kuppa et al 2003). The computation for calculating system effectiveness (i.e., the proportion of intersection crashes/injuries that could have potentially been prevented) can be found in the Appendix.…”

Section: Injury Benefits Estimatesmentioning

confidence: 99%

Injury mitigation estimates for an intersection driver assistance system in straight crossing path crashes in the United States

Scanlon

Sherony

Gabler

2017

Traffic Injury Prevention

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Objective: Accounting for one fifth of all crashes and one sixth of all fatal crashes in the United States, intersection crashes are among the most frequent and fatal crash modes. Intersection advanced driver assistance systems (I-ADAS) are emerging vehicle-based active safety systems that aim to help drivers safely navigate intersections. The objective of this study was to estimate the number of crashes and number of vehicles with a seriously injured driver (Maximum Abbreviated Injury Scale [MAIS] 3+) that could be prevented or reduced if, for every straight crossing path (SCP) intersection crash, one of the vehicles had been equipped with an I-ADAS. Methods: This study retrospectively simulated 448 U.S. SCP crashes as if one of the vehicles had been equipped with I-ADAS. Crashes were reconstructed to determine the path and speeds traveled by the vehicles. Cases were then simulated with I-ADAS. A total of 30 variations of I-ADAS were considered in this study. These variations consisted of 5 separate activation timing thresholds, 3 separate computational latency times, and 2 different I-ADAS response modalities (i.e., a warning or autonomous braking). The likelihood of a serious driver injury was computed for every vehicle in every crash using impact delta-V. The results were then compiled across all crashes in order to estimate system effectiveness. Results: The model predicted that an I-ADAS that delivers an alert to the driver has the potential to prevent 0-23% of SCP crashes and 0-25% of vehicles with a seriously injured driver. Conversely, an I-ADAS that autonomously brakes was found to have the potential to prevent 25-59% of crashes and 38-79% of vehicles with a seriously injured driver. I-ADAS effectiveness is a strong function of design. Increasing computational latency time from 0 to 0.5 s was found to reduce crash and injury prevention estimates by approximately one third. For an I-ADAS that delivers an alert, crash/injury prevention effectiveness was found to be very sensitive to changes in activation timing (warning delivered 1.0 to 3.0 s prior to impact). If autonomous braking was used, system effectiveness was found to largely plateau for activation timings greater than 1.5 s prior to impact. In general, the results of this study suggest that I-ADAS will be 2-3 times more effective if an autonomous braking system is utilized over a warning-based system. Conclusions: This study highlights the potential effectiveness of I-ADAS in the U.S. vehicle fleet, while also indicating the sensitivity of system effectiveness to design specifications. The results of this study should be considered by designers of I-ADAS and evaluators of this technology considering a future I-ADAS safety test.

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Section: Injury Benefits Estimatesmentioning

confidence: 99%

Injury mitigation estimates for an intersection driver assistance system in straight crossing path crashes in the United States

Scanlon

Sherony

Gabler

2017

Traffic Injury Prevention

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“…These situations are well documented in the literature. For instance, older drivers are over-represented in crashes occurring while turning off at intersections, where typically the older driver turns against oncoming traffic with right of way on the main road (Hakamies-Blomqvist, 1993;Zhang et al, 1998), gap acceptance while turning left at an intersection (Langford and Koppel, 2006;Yan et al, 2007) and response to signs, signals and road hazards (Bao and Boyle, 2008;Horswill et al, 2010). A detailed description of the driving scenario is mentioned in Cuenen et al (submitted).…”

Section: Scenariomentioning

confidence: 99%

“…By 2030, one in every 8 of the earth's inhabitants will age above 65 (U.S. National Institute on Aging, 2007). The increase in the population of elderly coincides with an increase in older drivers (Langford and Koppel, 2006). At the same time, the safety and mobility of older drivers are challenged by several age-related changes, including sensory, motor and cognitive abilities and a decline in those aspects affect the ability to drive safely.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Overall Driving Performance of Older Drivers

Babaee¹,

Shen²,

Hermans³

et al. 2014

Cictp 2014

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This study investigates the relative performance of older drivers at the individual level, based on specific measures of functional and driving abilities. To do so, 55 participants aged 70 years and above completed tests of an assessment battery of psychological and physical aspects as well as knowledge of road signs; moreover, they took a driving simulator test in which specific driving situations that are known to cause difficulties for older drivers were included. To evaluate the overall performance of each driver, all the above information was combined by using the concept of composite indicators, and the technique of data envelopment analysis, which is an optimization model for measuring the relative performance of a set of units, or drivers in this study, was employed. The model output distinguishes the best-performers from those underperforming drivers, and helps in guiding future development of training interventions tailored to each individual by specifically targeting those functions that are (mostly) impaired.

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Section: Introductionmentioning

confidence: 99%

“…Although older drivers are currently involved in few crashes in terms of absolute numbers, they display some of the highest rates of morbidity or mortality per number of drivers and per distance traveled (Langford & Koppel, 2006). Specifically, in analyses of Australian 1996-1999 fatal crash data, Langford and Koppel (2006) found that drivers over the age of 65 accounted for a disproportionate number of serious injuries and crash fatalities, compared to younger drivers. Other empirical studies of older drivers also identify that declines in attention, reaction time, vision and physical and executive functioning with age are significantly associated with driving outcomes (Anstey et al, 2005); namely, driving is a demanding task that requires a high level of visual, cognitive and motor/somatosensory skills.…”

Section: Introductionmentioning

confidence: 99%

The impact of subclinical sleep problems on self-reported driving patterns and perceived driving abilities in a cohort of active older drivers

Hickey¹,

Weegar²,

Kadulina³

et al. 2013

Accident Analysis & Prevention

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The present study sought to investigate the influence of subclinical sleep disturbances on driving practices and driver perceptions in a large cohort of healthy older drivers. Participants from the Candrive II prospective cohort study were investigated. Self-reported measures of sleep problems were used to determine the influence of sleep disturbance on self-reported driving practices and perceived driving abilities, as measured by the Situational Driving Frequency, Situational Driving Avoidance, and Perceived Driving Abilities scales. Hierarchical regression analyses were used to estimate whether mild self-reported sleep problems were predictive of driving restrictions and perceived abilities, while controlling for a variety of health-related factors and demographic variables known to mediate sleep problems or to impact driving.Cross-sectional analysis of baseline data from the Candrive II study suggests that subclinical sleep problems do not significantly influence self-reported driving patterns or perceived driving abilities in older drivers once control variables are considered. The relationship between sleep problems, driving frequency, avoidance and perceived abilities is better explained by mediating demographic, health, and cognitive factors. Further research examining sleep disturbances and driving should include objective measures of driving practices (exposure, patterns) and outcomes (crashes, violations) and should take in consideration the severity of sleep problems.

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Epidemiology of older driver crashes – Identifying older driver risk factors and exposure patterns

Cited by 163 publications

References 16 publications

Injury mitigation estimates for an intersection driver assistance system in straight crossing path crashes in the United States

Injury mitigation estimates for an intersection driver assistance system in straight crossing path crashes in the United States

Assessing the Overall Driving Performance of Older Drivers

The impact of subclinical sleep problems on self-reported driving patterns and perceived driving abilities in a cohort of active older drivers

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