In the past decade, the development and the application of traffic micro-simulation to replicate real-world traffic behavior have become pervasive among traffic and transport researchers. The modeling of a driver's car-following behavior, which forms the fundamental component of traffic microsimulation, has meanwhile been an important research direction leading to the sophistication of traffic microsimulation. However, recent studies have pointed out that a driver's following behavior varies when the lead vehicle is a passenger car as opposed to a heavy vehicle. Nevertheless, existing models do not precisely address those differences. This oversight could diversely affect the accuracy of traffic microsimulations, particularly with the current trend of an increasing number of heavy vehicles in the traffic stream. A novel car-following model that considered the heterogeneity of lead vehicles was developed. Two types of lead vehicles were considered in this study: passenger cars and heavy vehicles. The model was developed on the basis of the local linear model tree approach. This approach is able to incorporate human perceptual imperfections into a car-following model. The input space is partitioned incrementally, and a linear model is developed for each locality (partition). The final output is calculated by the fuzzy combination of local models according to the validity function of each model. For training and testing purposes, two real-world data sets were obtained from a U.S. freeway under congested traffic conditions. The results showed very close agreement between the real data and the outputs of the proposed model.