Identifying effects of driving and secondary task demands, passenger presence, and driver characteristics on driving errors and traffic violations – Using naturalistic driving data segments preceding both safety critical events and matched baselines

Precht, Lisa; Keinath, Andreas; Krems, Josef F.

doi:10.1016/j.trf.2017.09.003

Cited by 22 publications

(7 citation statements)

References 28 publications

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“…For example, the challenges facing older drivers when performing driving tasks as they move along the interchange could reduce the effect of distractions on recognition errors more than on decision errors. Moreover, distracted drivers tend to engage in compensatory behaviors when in an attempt to avoid potential crashes ( 14 , 17 , 45 ). Previous research established distraction as the leading reason for all driving errors among older drivers ( 44 – 46 ).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, distracted drivers tend to engage in compensatory behaviors when in an attempt to avoid potential crashes ( 14 , 17 , 45 ). Previous research established distraction as the leading reason for all driving errors among older drivers ( 44 – 46 ). More emphasis on education and enforcement could potentially reduce crashes resulting from decision errors caused by distracted driving.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

Monyo

Haule

Kitali

et al. 2021

Transportation Research Record

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Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

Monyo

Haule

Kitali

et al. 2021

Transportation Research Record

View full text Add to dashboard Cite

show abstract

“…A recently developed intricate error taxonomy (Stanton & Salmon, 2009) might provide a good starting point together with factors contributing to such errors. Also, the need for self-report studies will remain in the future even though studies involving instrumented vehicles have become ever more intricate (Precht, Keinath & Krems, 2017), because physical measurements are underdetermined psychologically (Transport Research Laboratory, 2015): for instance, a sudden acceleration can be due to either racing from the lights or unfamiliarity with car controls.…”

Section: Discussionmentioning

confidence: 99%

Network models of driver behavior

Mattsson

2019

PeerJ

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The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

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“…A recently developed intricate error taxonomy 54 might provide a good starting point together with factors contributing to such errors. Also, the need for self-report studies will remain in the future even though studies involving instrumented vehicles have become ever more intricate 62 , because Manuscript to be reviewed physical measurements are underdetermined psychologically 29 : for instance, a sudden acceleration can be due to either racing from the lights or unfamiliarity with car controls.…”

Section: Discussionmentioning

confidence: 99%

Peer Review #1 of "Network models of driver behavior (v0.2)"

2019

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The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation-and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental / theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

show abstract

Identifying effects of driving and secondary task demands, passenger presence, and driver characteristics on driving errors and traffic violations – Using naturalistic driving data segments preceding both safety critical events and matched baselines

Cited by 22 publications

References 28 publications

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

Network models of driver behavior

Peer Review #1 of "Network models of driver behavior (v0.2)"

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