| Summary: | In this work, the authors developed procedures to explain mean extra travel time (<i>T</i>) and extra travel time variability (<i>V</i>). This was carried out for situations (through simulations) where the fast vehicles’ travel time, whose speed tendency (<i>sp</i><sub>1</sub>) is the permitted speed limit, is negatively affected (i.e., increasing travel time) by the presence of slow vehicles, whose speed tendency (<i>sp</i><sub>2</sub>) is half the speed limit. The speed limit was set in the range of 60 km/h to 90 km/h, with seven cases, and every case had eight simulations, each with different <i>p</i><sub>1</sub> (fast vehicles’ percentage) and <i>p</i><sub>2</sub><i>=</i> 100% <i>− p</i><sub>1</sub> (slow vehicles’ percentage) values. <i>p</i><sub>2</sub> ranged from 10% to 80% at intervals of 10%, for a total of 56 simulations. From the simulations’ data, we calculated the fast vehicles’ extra travel time, which is the additional time to traverse an avenue segment owing to the presence of slow vehicles. The fast and slow vehicles recreate heterogenous traffic in terms of speed. We developed procedures for modeling <i>T</i> and <i>V</i> with <i>p</i><sub>2</sub>, and <i>V</i> with <i>T</i>. For modeling, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>~</mo></semantics></math></inline-formula>71.42% of the data from simulations was used. We find that the models’ parameters values can be used for explaining the remaining data. In addition, we discovered that the pattern of <i>p</i><sub>2</sub> vs. <i>V</i>, for <i>p</i><sub>2</sub> ranging from 50% to 80%, is different among <i>sp</i><sub>1</sub> cases and not linear.
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