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Margit Herle

Bio: Margit Herle is an academic researcher. The author has contributed to research in topics: Cognitive skill & Driving test. The author has an hindex of 1, co-authored 2 publications receiving 13 citations.

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Journal ArticleDOI
TL;DR: In this paper, the authors used a modern theoretical framework to assess which psychometric tests are still able to predict safe driving performance in today's professional drivers under new circumstances and found that logical reasoning showed significant effects.
Abstract: Large truck and bus crashes still cause a high rate of fatalities and costs. Considering that the human factor plays an important role it is obvious that there is great interest in predicting safe driving performance in professional drivers, especially with new technologies emerging to assist drivers. This study uses a modern theoretical framework to assess which psychometric tests are still able to predict safe driving performance in today’s professional drivers under these new circumstances. 126 male professional bus drivers completed a standardized digital test battery and three driving exercises. The test battery was used to assess reaction time, concentration, ability to gain an overview, reactive stress tolerance, logical reasoning, and safety-related personality traits. The exercises consisted of an on-road driving test, an obstacle course, and a maneuvering course. The study yielded satisfactory indicators of criterion related validity. It also showed that different tests were relevant for the prediction of safe driving performance in different driving exercises. Contrary to previous research, logical reasoning showed significant effects. The results indicate that in order to assess safe driving performance in professional drivers, a comprehensive assessment with psychometric tests should be recommended.

19 citations

Journal ArticleDOI
01 Sep 2021-PLOS ONE
TL;DR: In this article, the authors developed a valid screening tool for fitness-to-drive assessment in older people with cognitive impairment externally validated on the basis of on-road driving performance in a single-centre, non-randomized cross-sectional trial.
Abstract: Introduction Due to aging and health status people may be subjected to a decrease of cognitive ability and subsequently also a decline of driving safety. On the other hand there is a lack of valid and economically applicable instruments to assess driving performance. Objective The study is designed to develop a valid screening-tool for fitness-to-drive assessment in older people with cognitive impairment externally validated on the basis of on-road driving performance. Methods In a single-centre, non-randomized cross-sectional trial cognitive functioning and on-road-driving-behavior of older drivers will be assessed. Forty participants with cognitive impairment of different etiology and 40 healthy controls will undergo an extensive neuropsychological assessment. Additionally, an on-road driving assessment for external validation of fitness to drive will be carried out. Primary outcome measures will be performance in attention, executive functions and visuospatial tasks that will be validated with respect to performance on the on-road-driving-test. Secondary outcome measures will be sociodemographic, clinical- and driving characteristics to systematically examine their influence on the prediction of driving behavior. Discussion In clinical practice counselling patients with respect to driving safety is of great relevance. Thus, having valid, reliable, time economical and easily interpretable screening-tools on hand to counsel patients is of great relevance for practitioners. Ethics and dissemination Ethics approval was obtained from the Ethics Committee at the Ludwig-Maximilians-University Munich. The trial results will be disseminated through peer-reviewed publications and various conferences. Trial registration 18–640. Trial registration: German Clinical Trials Register. Registration number: DRKS00023549.

Cited by
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Journal ArticleDOI
TL;DR: A conceptual framework is outlined whereby DB is viewed in terms of different dimensions established within the Driver–Vehicle–Environment (DVE) system, and an interpretive framework incorporating multiple dimensions influencing the driver’s conduct is identified.

87 citations

01 Jan 2006
TL;DR: In this paper, the authors presented a comparison of methods for estimating Equations using multinomial regression models and generalized linear mixed models for non-normal responses in the context of disaster scenarios.
Abstract: INTRODUCTION BINOMIAL DATA Challenger Disaster Example Binomial Regression Model Inference Tolerance Distribution Interpreting Odds Prospective and Retrospective Sampling Choice of Link Function Estimation Problems Goodness of Fit Prediction and Effective Doses Overdispersion Matched Case-Control Studies COUNT REGRESSION Poisson Regression Rate Models Negative Binomial CONTINGENCY TABLES Two-by-Two Tables Larger Two-Way Tables Matched Pairs Three-Way Contingency Tables Ordinal Variables MULTINOMIAL DATA Multinomial Logit Model Hierarchical or Nested Responses Ordinal Multinomial Responses GENERALIZED LINEAR MODELS GLM Definition Fitting a GLM Hypothesis Tests GLM Diagnostics OTHER GLMS Gamma GLM Inverse Gaussian GLM Joint Modeling of the Mean and Dispersion Quasi-Likelihood RANDOM EFFECTS Estimation Inference Predicting Random Effects Blocks as Random Effects Split Plots Nested Effects Crossed Effects Multilevel Models REPEATED MEASURES AND LONGITUDINAL DATA Longitudinal Data Repeated Measures Multiple Response Multilevel Models MIXED EFFECT MODELS FOR NONNORMAL RESPONSES Generalized Linear Mixed Models Generalized Estimating Equations NONPARAMETRIC REGRESSION Kernel Estimators Splines Local Polynomials Wavelets Other Methods Comparison of Methods Multivariate Predictors ADDITIVE MODELS Additive Models Using the gam Package Additive Models Using mgcv Generalized Additive Models Alternating Conditional Expectations Additivity and Variance Stabilization Generalized Additive Mixed Models Multivariate Adaptive Regression Splines TREES Regression Trees Tree Pruning Classification Trees NEURAL NETWORKS Statistical Models as NNs Feed-Forward Neural Network with One Hidden Layer NN Application Conclusion APPENDICES Likelihood Theory R Information Bibliography Index

62 citations

Journal ArticleDOI
TL;DR: Investigation of the impact of mobile phone use and drivers’ compensatory behaviours on the collision risk in a car-following situation indicated that female drivers and nonprofessional drivers were more likely to be involved in high risk group than male drivers and professional drivers.
Abstract: Mobile phone distraction has been recognized as an adverse factor that degrades drivers’ performance on road. Although research showed that drivers take various compensatory strategies to minimize the risk in distracted driving, little consensus has been achieved regarding the actual change in collision risk because of compensatory behaviours. This study aims to investigate the impact of mobile phone use and drivers’ compensatory behaviours on the collision risk in a car-following situation. By using a high-fidelity driving simulator, 37 participants completed the simulation experiment in three mobile phone use conditions: no phone (baseline), hands-free and hand-held. Cluster analysis was adopted to classify the final collision risk into different levels. Two logit regression models were developed to examine the relationships between drivers’ characteristics, mobile phone use, collision avoidance performances and their involvement in the collision risk. Results show that compared to no phone and hands-free, drivers using hand-held phone had a longer brake reaction time and also an increased likelihood of being involved in a high risk group. Drivers compensated to reduce the likelihood of safety-critical events through a simultaneous control of car-following speed and distance (i.e. Time-to-collision (TTC)) in distracted condition. Additionally, the results also indicated that female drivers and nonprofessional drivers were more likely to be involved in high risk group than male drivers and professional drivers. The study provided a systematic method to quantify the impact of mobile phone distraction and drivers’ compensation behaviors on collision risk. The effectiveness of compensatory strategy by controlling TTC also shed light on the development of intelligent transport systems to help distracted drivers avoid safety-critical situations.

50 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the association between personality characteristics and accidents among professional truck drivers at the facet level of personality using company records of accidents over time and found that more empathetic individuals had lower rates of accident involvement, whereas more anxious, guilt-prone, exhibitionistic, and risk-taking individuals had higher rates.

22 citations

Journal ArticleDOI
TL;DR: Research found that attention, speed anticipation and performance stability were found to positively predict safety performance of HSR drivers, providing new ideas for the selection and management of H SR drivers and the improvement of driver safety performance.
Abstract: A relationship between cognitive ability and safety performance in car drivers has been consistently supported in previous literature. However, this relationship has been neglected in research regarding high-speed railway drivers. Drivers play a vital role in ensuring safety operation of high-speed railway in China. Therefore, it is of great significance to predict the impact of cognitive ability and railway driving experience of high-speed railway (HSR) drivers on safety performance. This study uses a standard driving adaptability system to assess driver’s cognitive abilities which predict safety performance in Chinese high-speed railway. 154 HSR drivers completed a standardized driving adaptability system test on a display device, which is widely used in the HSR drivers’ selection in China. The driving adaptability system is used to assess attention, multiple reaction ability, learning ability, short-term memory, speed anticipation and performance stability. Thereafter, monthly performance assessment data were collected after cognitive test since they truly reflect the driver's safety performance. Research found that attention, speed anticipation and performance stability were found to positively predict safety performance of HSR drivers. The railway driving experience plays a moderating role in the relationship between speed anticipation and safety performance, performance stability and safety performance. The richer the driver's driving experience, the stronger the effect of speed perception and job stability on safety performance, and vice versa. The results provide new ideas for the selection and management of HSR drivers and the improvement of driver safety performance.

18 citations