Minimizing driver errors should improve driving safety. Driver errors are more common when workload is high than when it is low. Thus, it is of great importance to study driver workload. Knowing the amount of workload at any given time, take-over time can be determined, adaptive in-vehicle systems can be refined, and distracting in-vehicle secondary tasks can be regulated. In this dissertation, a model quantifying workload as a function of traffic, in which workload is proportional to inverse time headway (THW) and time to collision (TTC), was proposed. Two experiments were conducted to investigate how traffic affected driver workload and evaluate the proposed model. The driving scenarios were categorized into static (i.e., no relative movements among vehicles) and dynamic (i.e., there are relative velocities and lane change actions). Three categories of workload measures (i.e., workload rating, occlusion %, and driving performance statistics) were analyzed and compared. A GOMS model was built based upon a timeline model by using timerequired to represent mental resources demanded and timeavailable to represent mental resources available. In static traffic, the workload rating increased with increased number of vehicles around but was unaffected by participant age. The workload ratings decreased with increasing Distance Headways (DHWs) of each vehicle. From greatest to least, the effects were: DHWLead, DHWLeftLead, DHWLeftFollow, DHWFollow. Any surrounding vehicle that was 14.5 m away from the participant resulted in significant greater workload. Drivers tended to compromise longitudinal speed but still maintain lateral position when workload increased. Although occlusion% was less sensitive to scenarios having no lead vehicles, it can nonetheless be well predicted using the proposed workload model in sensitive scenarios. The resulting equations were occlusion% = 0.35 + 0.05/THWLead + 0.02/THWLeftLead - 0.08Age (Rocclusion2=0.91); rating = 1.74 + 1.74/THWLead + 0.20/THWFollow + 0.79/THWLeftLead + 0.28/THWLeftFollow (Rrating2=0.73). In dynamic traffic, drivers experienced greater workload in the faster lane; higher workload level was associated with greater relative velocity between two lanes. Both rating and occlusion% can be described using the proposed model: Anchored rating = 4.53 + 1.215/THWLeftLeadLead + 0.001/THWLeftFollow + 3.069/THWLeadLead + 0.524/THWLead + 0.240/(TTCLead×TTCLeadLateral) + 30.487/(TTCLeftLead×TTCLeftLeadLateral) (Rrating2=0.54); Occlusion% = 0.381 + 0.150/THWLeftLeadLead ˗ 0.117/THWLeadLead + 0.021/THWLead + 2.648/(TTCLeftLead×TTCLeftLeadLateral) (Rocclusion2=0.58). In addition, it was shown that the GOMS model accounted for the observed differences of workload ratings from the empirical data (R2>0.83). In contrast to most previous studies that focus on average long-term traffic statistics (e.g., vehicles/lane/hour), this dissertation provided equations to predict two measures of workload using real-time traffic. The comparisons among three workload measures provided insights into how to choose the desired workload measures in their future research. In GOMS model, the procedural knowledge of rating workload while driving was developed. They should be transferrable to other workload studies and can serve as the primary tool to justify experimental design. Scientifically, the results of this dissertation offer insights into the mechanism of the way that humans perceive workload and the corresponding driving strategies. From the engineering application and practical value perspective, the proposed workload model would help future driving …