In highway on-ramp sections, the conflictual interactions between a subject vehicle (merging vehicle) in the acceleration lane and a following vehicle (lagging vehicle) in the adjacent mainline can lead to traffic congestion, go–stop oscillations, and serious safety hazards. Human drivers combine their previous lane-changing experience and their perception of surrounding traffic conditions to decide whether to merge. However, the decisions that they make are not always optimal in specific traffic scenarios due to fuzzy perception and misjudgment. That is, they make lane-changing decisions in a bounded rational way. In this paper, a game-theory-based approach is used to model the interactive behavior of mandatory lane-changing in a highway on-ramp section. The model comprehensively considers vehicle interactions and the bounded rationality of drivers by modeling lane-changing behavior on on-ramps as a two-person non-zero-sum non-cooperative game with incomplete information. In addition, the Logit QRE is used to explain the bounded rationality of drivers. In order to estimate the parameters, a bi-level programming framework is built. Vehicle trajectory data from NGSIM and an unmanned aerial vehicle survey were used for model calibration and validation. The validation results were rigorously evaluated by using various performance indicators, such as the mean absolute error, root mean square error, detection rate, and false-alarm rate. It can be seen that the proposed game theory-based model was able to effectively predict merging and yielding interactions with a high degree of accuracy.

Most of the existing map matching methods are developed considering global positioning system (GPS) data. However, the limitations of GPS data, such as a small sample size and presence of position errors, impede the application of such data in intelligent transportation system (ITS) applications. In contrast, automatic vehicle identification (AVI) data exhibit unique advantages and can overcome the issues related to the GPS data. The existing map matching methods are unsuitable for use with AVI data due to the sparsity issue. Therefore, in this study, a map matching method named AVI-MM is developed that is customized for sparse AVI data. The AVI-MM first decomposes the AVI trajectories with the connective observation pairs. Subsequently, the set of candidate sub-paths for each sensor pair is generated, and each candidate sub-path is assigned a matching probability. Finally, the matching path is determined by connecting the sub-path with the highest matching probability. As many sub-paths can connect the sparse AVI observations, a candidate set generation algorithm is developed to generate a sufficient number of feasible and attractive candidates. Moreover, to reliably and accurately determine the true path, the matching probability is defined based on a spatial-temporal analysis and drivers’ route choice behaviour analysis. Field-test data are used to estimate and evaluate the proposed method. Compared with three benchmark methods, the proposed AVI-MM exhibits a significantly enhanced matching accuracy without a considerable loss in the computation efficiency. Moreover, the evaluation results of the robustness of the proposed algorithm suggest that the algorithm can correctly identify 82.55% of the links when the spatial gap between the observation pair is less than 20 km. Specifically, the proposed method exhibits a satisfactory performance for 84.02% of the observed samples in the test dataset.