Lane changing is regarded as one of the most challenging behaviors of drivers. The lane-changing behaviors are divided into mandatory and discretionary. This study proposes an adaptive neuro-fuzzy model of discretionary lane-changing behavior in real traffic flow. Similar to other behaviors of drivers, lane changing is influenced by human factors, including age, gender, level of driving experience, hastiness, cautiousness, and alertness as well as environmental factors such as road and weather conditions. Identifying and measuring the said factors seem to be difficult or, in some cases, impossible. This study sorts out the lane-changing behavior into moments and two time intervals. In these time intervals, distance and relative speed, affected by the said factors, are accounted for in terms of time parameters and fed as inputs to the proposed predictive model. This is the innovative and distinguishing feature of the present study when compared to other researches. Finally, simulation and comparison based on real data indicate that when time parameters are considered and fed as inputs to model the error between the driver’s behavior and the proposed predictive model is less than when time parameter is not accounted for.