Modeling Freeway Merging in a Weaving Section as a Sequential Decision-Making Process

Vehicle merging is a tactical, dynamic optimizing process with a series of decision-making operations. Existing microscopic lane-changing simulation models have been criticized that they do not well capture the important sequential decision-making process during merging because of the lack of deeply investigating field data. This study first analyzes merging tactics based on noise-filtered next-generation simulation (NGSIM) data, and then a new combined sequential decision-making model was proposed for candidate gap generation, targeting gap selection, merge location choice, and acceleration decisions of the merging vehicles. This sequential decision-making model could dynamically simulate the merging vehicles’ choice of targeting gaps and merging tactics under the effects of changing traffic conditions. How the merging vehicles behave after rejecting their current adjacent gap and synchronize with the speed of the main-lane traffic during merging is embodied in this model. Such aspects were oversimplified and against reality in existing merging models. The proposed new merging model was calibrated and validated with a U.S. Highway 101 (U.S.101) data set. The superiority of this new framework is highlighted in comparison with other two merging models. The results demonstrate that the proposed model has an acceptable accuracy to reproduce the trajectories of merging vehicles. The simulated vehicles’ trajectories show that the gap-selection model and targeting gap approaching algorithm can well simulate the driving behavior of the merging vehicle, which carries gap-rejection experience. The empirical field data analysis and model calibration results in this study provide several useful thoughts on building merging models to capture more real-world freeway merging behaviors.