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

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.

Assessment of Urban Railway Transit Driver Workload and Fatigue under Real Working Conditions

This paper describes an experiment conducted to establish a workload model by employing physiological methods to measure driver workload and fatigue under real working conditions. Experienced healthy metro drivers were selected as subjects; they performed normal schedules during which simultaneous electrocardiogram (ECG) recording was used to assess their levels of fatigue. Then, subjective workload assessment and reaction time tests were conducted during each break interval to monitor the drivers’ physiological and psychological performance. Based on task analysis, driving workload models with time weight parameters of four types of tasks were established and the workload real-time changes during different shifts were evaluated. The results demonstrate that workload tends to increase over time and it is significantly higher during manual driving mode than autonomous mode ( p = 0.015 < 0.05). Driving fatigue occurs earlier in the night shift than in the day shift according to ECG spectrum analysis results. Although the results of reaction time tests show no significance ( p = 0.917 > 0.05), the increase in the number of reaction errors after fatigue driving indicates a reduction in drivers’ cognitive ability. Regression analysis shows a significant regression relationship with a mutual incentive effect between workload and fatigue in three shifts ( R2 > 0.4). These will be used as a future reference for fatigue research and to help develop reasonable schedules to ensure operational safety.

Target visibility under mesopic vision using a driving simulator

Small target visibility is widely used to evaluate the quality of road lighting. It provides a link between lighting design and driving performance. However, it is based on a strong simplification of the driving task using psychophysical data from laboratory conditions. Using a driving simulator to mimic the real driving environment, the impact of driving workload on target detection performance in mesopic vision conditions has been evaluated. The target visibility level is studied with and without driving workload together with different luminance contrasts and target positions, with reference to the small target visibility scenario. The results show that the driving workload significantly reduces the target detection performance. Consequently, the visibility level value for driving conditions should be much higher (visibility level ≥21) than some currently recommended ones (visibility level = 7) to achieve the same detection rates. Effects of target position and contrast are found in a way consistent with the literature. In addition, results indicate that the small target visibility model used for road lighting is limited and needs to be improved for a reliable prediction of visual performance with driving workload.