  Lane changes are complex safety and throughput critical driver actions. Most
lane changing models deal with lane-changing maneuvers solely from the merging
driver's standpoint and thus ignore driver interaction. To overcome this
shortcoming, we develop a game-theoretical decision-making model and validate
the model using empirical merging maneuver data at a freeway on-ramp.
Specifically, this paper advances our repeated game model in a previous paper
by using updated payoff functions. Validation results using the NGSIM empirical
data show that the developed game-theoretical model provides better prediction
accuracy compared to previous work, with correct predictions approximately 86
percent of the time. In addition, a sensitivity analysis demonstrates the
rationality and sensitivity of the model to variations in various factors. To
provide evidence of the benefits of the repeated game approach, which takes
into account previous decision-making results, a case study is conducted using
an agent-based simulation model. The proposed repeated game model produces
superior performance to a one-shot game model, when simulating actual freeway
merging behaviors. Finally, this lane change model, which captures the
collective decision-making between human drivers, can be used to develop
automated vehicle driving strategies.
