学术文献库

The estimation of travel time distribution (TTD) is critical for reliable route guidance and provides theoretical bases and technical support for advanced traffic management and control. The state-of-the art procedure for estimating arterial TTD commonly assumes that the path travel time follows a certain distribution without considering segment correlation. However, this approach is usually unrealistic as travel times on successive segments may be dependent. In this study, copula functions are used to model arterial TTD as copulas are able to incorporate for segment correlation. First, segment correlation is empirically investigated using day-to-day GPS data provided by BMW Group for one major urban arterial in Munich, Germany. Segment TTDs are estimated using a finite Gaussian Mixture Model (GMM). Next, several copula models are introduced, namely Gaussian, Student-t, Clayton, and Gumbel, to model the dependent structure between segment TTDs. The parameters of each copula model are obtained by Maximum Log Likelihood Estimation. Then, path TTDs comprised of consecutive segment TTDs are estimated based on the copula models. The scalability of the model is evaluated by investigating the performance for an increasing number of aggregated links. The best fitting copula is determined in terms of goodness-of-fit test. The results demonstrate the advantage of the proposed copula model for an increasing number of aggregated segments, compared to the convolution without incorporating segment correlations.