The paper develops a reactive-driving agent based algorithm for modeling driver left turn gap acceptance behavior. The model considers the interaction between driver characteristics and vehicle physical capabilities. The model explicitly captures the vehicle constraints on driving behavior using kinematics models. The model uses the driver’s input to the vehicle and the psychological deliberation in accepting/rejecting a gap. The model is developed using a total of 301 accepted gaps and subsequently tested on 2,429 rejected gaps at the same test site in addition to 1,485 gap decisions (323 accepted and 1,162 rejected) at another site. The proposed model is considered as a mix between traditional and reactive methods for decision making. The model consists of three main components: Input, Data processing and Output. The input component uses sensing information, vehicle and driver characteristics to process the data and estimate the critical gap value. Thereafter, the agent decides to either accept or reject the offered gap by comparing to a driver-specific critical gap (the offered gap should be greater than the critical gap to be accepted). Finally, the model is tested on two different datasets for validation. The proposed modeling framework can be generalized to capture different vehicle types, roadway, movement, intersection characteristics, and weather effects on driver gap acceptance behavior.