###### Abstract
In [9], the free-flow speed was modeled considering as an explanatory variable the trucks' percentage in order to test if the flow heterogeneity (the traffic composition in terms of vehicles' type) impacted speed. The results showed that the trucks' percentage is significant for the four models: daytime speed for cars and trucks, and nighttime for cars and trucks. In [10], with the aim of identifying explanatory factors for mean speed and speed dispersion considering two-lane rural highways on tangent segments and horizontal curves, free-flow speed models were developed. The drivers' travel speed is influenced by the posted speed limit. In [11], it was found that the posted speed limit is highly correlated with the average free-flow speed for urban streets, multilane highways, and freeways. In [12], the speed limits in Utah rural interstates were increased from 75 to 80 mph, finding that the travel speed and the probability to overcome the speed limit were then higher, and that by increasing the speed limit, the speed variance was not reduced. However, as the literature review presented in [13] suggests, the results concerning the effects of speed limit on speed variation do not support each other. The investigation in [14] centers on the local and global impacts of speed limits considering two groups of drivers--the compliant and the non-compliant drivers. When choosing speed, both consider the travel time cost, the perceived crash risk, and the perceived ticket risk. The factors influencing the drivers' desired speed on the road are numerous. There are multiple interacting factors that explain drivers' behaviors [15]. A study on drivers' speeding behavior is presented in [16]. The drivers' characteristics and their relation to speed (as a risk of accidents level) are presented in [17]. In [18], the drivers' speed choice (when there is at least a 6 s time gap to the next vehicle) at two different sites were compared, and at two different days for the same site. How a driver is influenced by the collective driving behavior is discussed in [19]. Empirical evidence suggesting that the speed choice is influenced by the pleasure of driving and other drivers' behavior is presented in [20]. Preliminary data for supporting the notion that a driver adjusts their speed by comparing their own speed with that of others are introduced in [21]. A model including attitudes and perceptions of other drivers' behavior explains approximately 15% of the variation in observed speed [22]. A speed choice prediction model was presented in [23]. It was found that the number of heavy vehicles (trucks and buses) and four-wheelers (informal paratransit) flowing in the same direction as the subject vehicle negatively influences the driver speed choice of the higher-speed class, and the same applies for trucks and motorcycles driving in the opposite direction. Buses, trucks, four-wheelers, and motorcycles negatively influence the speed of the lower-speed class (same traffic direction), and the same applies for non-motorized and buses driving in the opposite direction. The operating speed was modeled in [24], and the dependence of the drivers' speed choice on individual driver' behavior and the roadway geometry was demonstrated. The necessity of vehicle class-specific modeling has been noted. Models to establish the free-flow speed of different types of vehicles are presented in [25]. In [26], it was found that the level-of-service (LOS) is different for each vehicle type. Besides, removing some vehicle types seems suitable to improve the system performance. Further, it was noticed that, in mixed traffic, the average stream speed better represents the dominant vehicle type, which is two-wheelers in [26]. The work in [27] uses percent speed-reduction and percent slower vehicles to define the LOS of a two-lane highway under heterogeneous traffic. It was concluded that it is impractical to evaluate the LOS of the entire traffic situation (with integration of motorized and non-motorized modes) with a common scale.