Incorporating Mental Workload into Highway Design
Creators: Habib, Karim Sayed Abouelala
Publisher: name: University of Alberta Library
Year: 2022

<p>Human error is a leading cause of collisions on highways. Thus, if the aim is to reduce collision rates and keep drivers safe, it is necessary to investigate the factors that influence human mistakes and develop mitigation strategies to prevent these errors from occurring. One crucial approach to achieving lower collision rates is to design highways that do not overwhelm drivers with high mental workloads since too many tasks that challenge drivers’ workload levels lead to human error. Better design will allow drivers enough mental capacity to make proper decisions. However, there is a lack of research on the quantitative relationship between mental workload and objective safety measures, as well as an absence of information on how to explicitly incorporate workload demand in geometric design guidelines and manage highway speeds in complex road environments. To address this research gap, this thesis investigates the relationship between mental workload and highway traffic safety from a geometric design perspective. The geometric design attributes for horizontal and vertical curvatures were automatically extracted from LiDAR data. This data offered various highway geometric parameters such as degree of curvatures and deflection angles of horizontal curves, detection of the existence of vertical crest curves, changes in cross-section, intersections, and available sight distances. Then, the outputs from the self-report measures were related to collision data and highway geometric design parameters. The objectives were to: (1) examine and model the relationship between workload demand and collisions using safety performance function, (2) calibrate highway design guidelines using reliability analysis, (3) create a system advisory speed limit on horizontal curves that incorporates workload demand, and finally (4) update the current workload ratings by adding new factors to assess the impact of weather and in-car technologies on workload levels. The results from the safety performance function showed a statistically significant relationship between collisions and MWL. The probability of non-compliance from the reliability analysis revealed that the mental workload stopping sight distance satisfied 99% of the driving population. Then, based on the mental workload stopping sight distance, an advisory speed limit, which compensates for any restrictions in mental workload sight distance was proposed. Finally, the last objective resulted in mental workload ratings that were highly correlated to their counterparts in the literature. Additionally, the new ratings incorporated several essential factors such as active transportation, severe weather conditions, and in-car assistance technologies. This thesis significantly contributes to the current body of work by providing a framework to transition from a subjective workload measure to safety, design, and operational transportation applications. These applications would provide engineers with the tools to evaluate their designs in terms of mental workload and identify the countermeasures to address challenging infrastructural designs.</p>