###### Abstract
In [28], two new methodologies are presented for estimating the percent time spent following (PTFS) on two-lane highways. The probabilistic method considers the slow-moving vehicles' average speed and the distribution of the desired speeds to calculate the percentage of vehicles whose desired speed is superior to the slower vehicles' average speed. This percentage (used to calculate the probability of a vehicle being impeded), along with the probability of a vehicle being part of a platoon, are used to calculate the percent following (PF), which is a surrogate measure for PTFS. In [29], it was noticed that the aggregation of faster and slower vehicles on a single class may not capture the interactions among the vehicles of the same class. The dynamic of homogeneous and heterogeneous traffic is very different; in the latter, there are different types of vehicles, thus there are interactions (such as lane change maneuvers) between slow-moving and fast-moving vehicles. In [30], particle-hopping models for two-lane traffic were developed considering two types of vehicles: fast and slow vehicles, where the fast vehicles' maximum allowed speed is considerable higher than that of the slow vehicles. The proposed models are different from each other because of the lane-changing rules, thus producing different results about the movement of the fast and slow vehicles. In [31], the heterogeneous traffic behavior in developing countries was reproduced with a discrete cellular automata (CA) model, modeling five types of vehicles: car, bus, truck, two-wheeler, and three-wheeler. In [32], a CA model was used to simulate traffic in a two-lane system; it was demonstrated that even small densities of slow vehicles induced the formation of platoons. Moreover, modifications were incorporated into the basic model, e.g., for allowing drivers to anticipate the speed of their predecessor, leading to diminishing the effect of slow vehicles. The multi-class traffic flow model presented in [33] incorporates heterogeneous drivers, i.e., drivers selecting a different speed to drive, thus there are faster vehicles trying to overtake slower vehicles. In addition, the model explains traffic flow theory phenomena: two-capacity regimes in the fundamental diagram, hysteresis, and platoon dispersion. In [34], a vehicle class was defined considering the desired speed in free-flow conditions, and a multiclass first-order simulation model was proposed that can replicate non-linear phenomena, including the dispersion of traffic platoons when a distribution of desired speeds exists. In [35], data were collected in several two-lane roads in India, and it was observed that the capacity decreases as the proportion of three-wheeler, tractor, or heavy vehicle increases, and that the capacity increases as the proportion of two-wheeler increases. In [36], through agent-based modeling, simulationsrepresentative of the traffic in the USA (with cars and trucks) and of the traffic in India (with different types of vehicles) were performed. It was found that the heterogeneity of vehicle types causes a high number of lane changes. In this investigation, we simulate and analyze traffic, where the speed differential affects travel speed, thus affecting travel time. We were interested in finding a relation between the percentage of slow vehicles and two variables related with the fast vehicles: the mean extra travel time (\(T\)) and the extra travel time variability (\(V\)). We propose procedures for modeling two metrics of importance in traffic studies: \(T\) and \(V\), which were calculated with the fast vehicles' travel times from simulations. Our research is different from the current literature as we perform traffic simulations considering a wide range of fast and slow vehicles' percentage values for a wide range of fast and slow vehicles' speed tendency values, thus adding insight for this kind of traffic situation.