Studying the effects of freeway alignment, traffic flow, and sign information on subjective driving workload and performance

Xie, Lian; Wu, Chaozhong; Lyu, Nengchao; Duan, Zhicheng

doi:10.1177/1687814019853925

Cited by 13 publications

(7 citation statements)

References 27 publications

(29 reference statements)

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“…Furthermore, the NASA-TLX has been shown to lead to success in driving. Xie [ 23 ] indicated that drivers who experienced a sharp curve scored higher on the NASA-TLX. However, the participants were of a similar age.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing

Liu

Zhang

et al. 2022

IJERPH

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The aim of this study was to quantify the effect of radius over horizontal curve sections on driving workload (DW). Twenty-five participants participated in the driving simulation experiments and completed five driving scenes. The NASA-TLX scale was used to measure the mental demand, physical demand, and temporal demand in various scenes, which were applied to assess subjective workload (SW). Objective workload (OW) assessment methods were divided into three types, in which the eye tracker was used to measure the blink frequency and pupil diameter, and the electrocardiograph (ECG) was used to measure the heart rate and the heart rate variability. Additionally, the simulator was used to measure the lateral position and the steering wheel angle. The results indicate that radius is negatively correlated with DW and SW, and the SW in a radius of 300 m is approximately twice that in a radius of 550 m. Compared with the ECG, the explanatory power of the OW can be increased to 0.974 by combining eye-movement, ECG, and driving performance. Moreover, the main source of the DW is the maneuver stage, which accounts for more than 50%. When the radius is over 550 m, the DW shows few differences in the maneuver stage. These findings may provide new avenues of research to harness the role of DWs in optimizing traffic safety.

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Section: Introductionmentioning

confidence: 99%

Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing

Liu

Zhang

et al. 2022

IJERPH

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“…e supplementary experiment was carried out on the basis of a previous study [35]. e number of subjects increased from 11 to 45 in this research.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, 16 simulated driving test scenarios were designed. More details of the scene design parameters can be found in [35].…”

Section: Scenario Designmentioning

confidence: 99%

“…Currently, it is difficult to determine how and which factors profoundly affect driving workload and behavior. erefore, the purpose of this study was to study the influence of various road environmental factors on drivers' psychology and behavior on the basis of previous research [35]. In current study, the L 16 (4 5 ) orthogonal experimental design was used to select representative points from the overall experiment, which reduced the number of experiments and resulted in a small data sample, and the reflected information was inaccurate and incomplete, with gray characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Freeway Safety Influencing Factors on Driving Workload and Performance Based on the Gray Correlation Method

Xie

Deng³

et al. 2021

Journal of Advanced Transportation

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Human-related factors are a crucial inducement of traffic accidents. Understanding the influence of freeway environments on the driving behavior and workload experienced by drivers has been demonstrated to be of primary importance for improving traffic safety. To study the effect of alignment, traffic flow, and sign information on drivers’ mental workload and behavior, 16 scenarios were constructed using the orthogonal design method, and simulated driving experiments were carried out with 45 participants. During driving, indicators such as the mean and standard deviation of vehicle speed and lane departure were collected, and the NASA-TLX questionnaire was adopted to measure workload. Analysis of variance results indicated that the radius of the horizontal curve, gradient, flow, and sign information level have a significant influence on drivers’ workload and speed keeping ability. In addition, the horizontal curve radius has a significant effect on lane keeping ability. The importance of safety influencing factors on driving workload and performance was quantitatively ranked by integrating the trend of Deng’s correlation degree, comprehensive correlation degree, and similar correlation degree, whose weight was calculated using the entropy method. Traffic sign information was found to have the greatest impact on workload. In terms of driving performance, traffic volume has the greatest influence on the mean and standard deviation of vehicle speed, followed by the amount of sign information. Lane departure is most affected by the radius of the horizontal curve. These findings provide guidance for freeway traffic safety regulation, including workload control and road facility optimization.

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“…The model involves the road form is similar to the expressway in China, and some studies show that the longitudinal profile of the road has a great influence on traffic safety, especially on the degree of the longitudinal slope of the road [29]. Therefore, this paper draws on the modelling idea of multi-lane expressway accident prediction model in HSM.…”

Section: Establishment Of Black Spot Identification Methods For Expressway Accidentsmentioning

confidence: 99%

Identifying Expressway Accident Black Spots Based on the Secondary Division of Road Units

Liang

Sun

Zhang

et al. 2021

PROMET

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For the purpose of reducing the harm of expressway traffic accidents and improving the accuracy of traffic accident black spots identification, this paper proposes a method for black spots identification of expressway accidents based on road unit secondary division and empirical Bayes method. Based on the modelling ideas of expressway accident prediction models in HSM (Highway Safety Manual), an expressway accident prediction model is established as a prior distribution and combined with empirical Bayes method safety estimation to obtain a Bayes posterior estimate. The posterior estimated value is substituted into the quality control method to obtain the black spots identification threshold. Finally, combining the Xi'an-Baoji expressway related data and using the method proposed in this paper, a case study of Xibao Expressway is carried out, and sections 9, 19, and 25 of Xibao Expressway are identified as black spots. The results show that the method of secondary segmentation based on dynamic clustering can objectively describe the concentration and dispersion of accident spots on the expressway, and the proposed black point recognition method based on empirical Bayes method can accurately identify accident black spots. The research results of this paper can provide a basis for decision-making of expressway management departments, take targeted safety improvement measures.

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Studying the effects of freeway alignment, traffic flow, and sign information on subjective driving workload and performance

Cited by 13 publications

References 27 publications

Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing

Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing

Analysis of Freeway Safety Influencing Factors on Driving Workload and Performance Based on the Gray Correlation Method

Identifying Expressway Accident Black Spots Based on the Secondary Division of Road Units

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