ABSTRACT YU, ZHANG. An Empirical Assessment of the Role of Driver Motivation and Emotion State, and Driving Conditions in Perceived Safety Margins. (Under the direction of Dr.

YU, ZHANG. An Empirical Assessment of the Role of Driver Motivation and Emotion State, and Driving Conditions in Perceived Safety Margins. (Under the direction of Dr. David B. Kaber). Models of motivation in driver behavior have been developed to predict driver performance under different conditions. Among existing models, Näätänen and Summala’s multi-dimensional threshold model (1976) was selected as basis for the current study. They proposed that driver behavior is modified based on the complexity of traffic situations and driver risk tolerance, which is influenced by driver motives and emotional states. According to this model, various factors may influence driver behavior, such as traffic patterns (other driver behavior) and driver motivation to comply with social norms, extreme emotions (e.g., urgency) and long-term emotional tendencies. The latter factor has been assessed using Driver Stress Inventories (Matthews, Desmond, Joyner, Carcary, & Gilliland, 1996). This model also indentified measures of change in driver risk-taking decisions, or safety margins, as being predictors of driver performance. The objectives of this study were to: 1) provide empirical evidence of the influence of motivation and emotional factors in driver risk-taking behavior; and 2) identify additional variables that might mediate the effects of motivational factors on behavior, including roadway environment complexity. The study examined the following specific factors: 1) traffic patterns, including traffic jam, school zone, normal traffic flow and speeding conditions to assess the influence of social norms on driver behavior; 2) payment systems, including time-based and performance-based compensation to assess the influence of extreme emotions on performance; and 3) environment complexity, including rural and city conditions to assess the influence of task difficulty on behavior. Response measures included safety margins and speed measures. Safety margins were measured by: 1) spatial variables, including headway distance (HW) and lateral distance (LD); and 2) time variables, including time headway (THW), time to collision (TTC) and time to line crossing (TTLC). Speed measures consisted of average speed, maximum speed and the percentage of time spent speeding. Ten participants drove in a high-fidelity simulator and performed daily driving tasks (e.g., lane maintenance). A split-plot experiment design was used with the whole-plot factors including environment complexity and the payment system. Traffic pattern was manipulated as the split-plot factor with each of the four patterns occurring during a single segment in a trial. Participants completed eight trials, including two replications of all combinations of complexity and payment system. Participants were also required to finish a DSI prior to driving. The experiment results revealed an effect of payment system. More risky driving behavior was associated with the performanceversus time-based system. The influence of environment complexity was also observed. Smaller safety margins appeared in the rural environment as compared to city. The effects of traffic pattern were significant across all response measures except TTLC: traffic jams led to minimum safety margins; speeding segments produced the highest driving speeds and largest safety margins; school zones were associated with conservative behavior, including lower speeds and larger safety margins. Furthermore, drivers were influenced more by other driver behavior in the city versus rural setting in terms of THW. Correlation analyses showed significant linear associations between long-term emotional tendencies and safety margin and speed measures. In summary, this research contributed to further development of motivational driving models by providing evidence of factors significantly influencing perceived safety margins and performance. The study also identified additional (lateral) measures for specifying safety margins. Future research should investigate a broader range of emotional factors that may influence safety margins. Long-term emotional tendencies should also be studied under a broader range of driving conditions. Additionally, future work might examine more diverse populations (beyond college students) to obtain a more comprehensive understanding of motivational/emotional factors in driving performance. An Empirical Assessment of the Role of Driver Motivation and Emotion State, and Driving Conditions in Perceived Safety Margins