Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.
Taking variability and uncertainty involved in performance prediction into account, in order to make the prediction reliable and meaningful, a distribution-based method is developed to predict future PSI. This method, which is based on the AASHTO pavement performance model, treats predictor variables as random variables with certain probability distributions and obtains the distribution of future PSI through the method of Monte-Carlo simulation. A computer program PERFORM using Monte Carlo simulation is developed to implement the numerical computation. Simulation results based on pavement and traffic parameters show that traffic, surface layer material property, and initial pavement performance are the most significant factors affecting pavement performance. Once the distribution of future PSI is determined, statistics such as the mean and the variance of future PSI are readily available.
