Towards data-driven microscopic traffic simulation models - Papathanasopoulou, Vasileia (Σχολή Αγρονόμων και Τοπογράφων Μηχανικών, 2018)
The objective of this research is to develop more accurate, robust and reliable microscopic models. An integrated methodological framework based on non–parametric approaches is proposed for estimation of data–driven microscopic traffic simulation models. The methodology is implemented using different ma-chine learning techniques such as clustering, classification, locally weighted regression, spline fitting, Gaussian processes, Kernel support vector machines and neural networks. The methodology is demonstrated using real trajectory data from three different sources and specifically an experiment from Naples, NGSIM data and non–lane disciplinary trajectory data from India. The focus is given on car–following models and Gipps’ model, one of the most extensively used car–following models, is calibrated against the same data in order to be used as a reference benchmark. Many parameters affect driving behavior and it is explored how the performance of the models is improved by including more explanatory variables. Then, a practical and simple approach is developed and motivated for the online calibration of microscopic traffic simulation models, which considers dynamic parameters for individual drivers, in time and space. The model adapts to driving behavior in a rolling horizon and leads to less than 10% error in speed prediction even for ten steps into the future. This research also examines the feasibility and the benefits of using data–driven models on mixed traffic trajectory data, including non–lane discipline and heterogeneity in vehicle types, common characteristics in cities in developing countries. Although typical car–following models are theoretically justified, data–driven approaches are more flexible and allow the easy incorporation of additional information to the process of speed estimation. The results indicate that data–driven models could ensure reliability and improvement in estimation of microscopic models.