The majority of vehicle collisions occur because of human error; in fact, studies have shown that approximately 95% of collisions are caused, at least in part, as a result of human mistakes. Therefore, it is important to study the main causes of human mistakes and develop mitigation strategies to reduce, if not eliminate, these errors from occurring. In this respect, designing highways that balance mental workload is a crucial task that ensures drivers have sufficient time to make appropriate decisions. However, the quantitative relationship between mental workload and collisions is not well documented in the safety literature. Enough evidence exists to support the reasonable conclusion that safety is affected by changes in workload, but there is no quantitative evidence of this effect. Consequently, this paper tries to investigate the relation between mental workload ratings and collisions on highways using data from Alberta, Canada. Horizontal and vertical curve parameters on two-lane, two-way highways were first extracted from LiDAR and GPS data using MATLAB algorithms, and the resulting features were summarized using Civil 3D. The mental workload ratings were assigned based on the presence of four major geometric features, namely, intersections, horizontal curves, vertical curves, and changes in cross-section. The results show that mental workload has a significant effect on safety for two-way, two-lane highways. Furthermore, the findings strongly indicated the need to integrate mental workload into the design process, to not only meet the operational needs of the highway, but also ensure that the geometric layout does not mentally overwhelm drivers.

The generic nature of road design is indiscriminate to age, race, or gender, as it is implicitly assumed that there are few behavioral differences between drivers while traversing various alignment elements (e.g., horizontal curves, tangential segments, etc.). For instance, the perception reaction time required, which is based on an 85th percentile value, on a tangent section is the same as that on a horizontal curve. This suggests that current guidelines do not consider the complexity that some geometric features might induce on drivers, and consequently, there is a need to address the many considerations of diversity. In this respect, human factors should be explicitly included in design guidelines. One aspect of human factors that has received little attention in the literature is related to the mental workload. In this study, a procedure is presented to estimate the mental workload for stopping sight distance. Then, reliability analysis is conducted to compare the change in the probability of non-compliance owing to the available sight distance and based on the mental workload. By analyzing data from 12 horizontal curves in Alberta, Canada, the probability of non-compliance dropped from 9.1% to 0.7%, and a moderate correlation with collisions was found. The results of the analysis showed that incorporating mental workload into the geometric design process can improve safety performance.

Cycling remains a popular mode of transportation, yet cyclists are vulnerable road users that face numerous safety challenges. Although human factors research typically focuses on motor vehicle drivers, studies addressing active transportation users, like cyclists, are scarce. The unique aspects of cycling, such as physical effort, exposure to the environment, and disconnected infrastructure, can affect cyclists’ mental perception, workload, and behavior, which are argued to influence their safety on the road. Therefore, this scoping literature review identified factors influencing cyclists’ workload and explored different measures used to quantify mental workload. The findings highlighted age, infrastructure, portable devices, and type of bike as factors that could affect workload levels. However, research on cyclists’ workload from their perspective is limited. This paper summarizes three types of workload measure: subjective, performance, and physiological. These have been used to quantify workload in relation to cyclists and in other settings. We reflect on their benefits and challenges were they to be used to quantify cyclists’ workload. Our discussion emphasizes the need for future research to take a comprehensive approach that considers multiple factors simultaneously to gain a more holistic understanding of their collective impact on cyclists’ mental workload. Moreover, we emphasize the importance of supplementing subjective workload measures with psychophysiological ones for better accuracy and reliability. The review revealed a lack of data and guidelines specific to cycling infrastructure, contributing to cyclists’ vulnerability, and underscored the need for previous findings to be translated into actionable recommendations to improve cyclist safety.

This study proposes a framework that accounts for mental workload and available sight distances to estimate advisory speed limits on horizontal curves. To achieve this goal, automated scripts were used to extract data on horizontal curve elements (i.e., the degree of curvature and the deflection angle), to detect crest vertical curves, and to compute the available sight distance from remote sensing data collected on highways in Alberta, Canada. Mental workload ratings were then assigned to each horizontal curve to calculate the perception–reaction time needed by drivers to maintain control of their vehicles while negotiating these curves. Finally, a curve advisory speed was calculated based on the available sight distance and the mental workload perception–reaction time needed to ensure a safe driving environment. This study presents a unique approach that incorporates human factors, including the responses of drivers based on their perception of the driving environment, in the development of speed advisory systems.

As awareness of cycling’s multifaceted benefits—spanning health, environmental, economic, and social domains—continues to grow, adoption rates are steadily increasing. Despite these benefits, urban cycling environments pose significant challenges, as cyclists share road space with motor vehicles and pedestrians, thereby increasing the risks of crashes and conflicts. Most existing research has focused on infrastructure aspects; however, few studies have explored physiological dimensions by measuring cycling stress across a ride. This research addresses this gap by using electrocardiogram (ECG) sensors to measure cyclists’ physiological stress levels while accounting for fatigue during cycling an urban route, with heart rate (HR) serving as the primary indicator. Twenty-two participants completed the same urban route twice in different directions while wearing an ECG device. Results from statistical analyses reveal that specific intersections and directional sequences through which cyclists move have a significant influence on their stress levels. Stress tends to increase with the accumulation of physical fatigue. Moreover, stress levels indicated by HR were also elevated at unsignalized intersections with unclear right-of-way, and at major signalized intersections with high traffic volume. These findings demonstrate that considering physiological data provides valuable insights into cycling experiences and can inform transportation planning and intersection design for safer, more comfortable urban cycling experiences.