THE APPLICATION OF MULTIVARIATE DISTANCE MATRIX REGRESSION IN TRANSPORTATION FOR TRAFFIC ANALYSIS

A critical function of intelligent transportation systems is studying and analyzing the effects of road condition variables (e.g. construction, severe weather, and the like) on traffic to aid in improving road designs, estimating travel time, and increasing safety. In this thesis, Multivariate Distance Matrix Regression (MDMR), a well-studied algorithm applied in brain research, is explored and applied in the transportation domain to assess the relationship and the effects of traffic conditions on transportation system performance. The Multivariate Distance Matrix Regression (MDMR) is utilized to study the relationship between input experimental factors and the association of response variables. When studying transportation, input factors can be represented as any factor that may have an effect on traffic, and response variables can be represented by traffic speed values over time for each segment of a road. The output is represented as a probability Value (P-Value) for each segment of the road as an indication of an effect of the studied factor on that specific segment. The National Performance Management Research Dataset (NPMRDS), (i.e., a probe-based traffic dataset) was used to study traffic performance based on specific factors by applying MDMR under different traffic scenarios.Moreover, a novel clustering algorithm for time series data is proposed by optimizing the F-statistic (i.e., a measurement metric to study the significance difference of two or more groups) to find the best segregation of time series between two or more groups. The clustering algorithm gave promising preliminary results when compared with K-means

On clustering procedures and nonparametric mixture estimation

This paper deals with nonparametric estimation of conditional den-sities in mixture models in the case when additional covariates are available. The proposed approach consists of performing a prelim-inary clustering algorithm on the additional covariates to guess the mixture component of each observation. Conditional densities of the mixture model are then estimated using kernel density estimates ap-plied separately to each cluster. We investigate the expected L 1 -error of the resulting estimates and derive optimal rates of convergence over classical nonparametric density classes provided the clustering method is accurate. Performances of clustering algorithms are measured by the maximal misclassification error. We obtain upper bounds of this quantity for a single linkage hierarchical clustering algorithm. Lastly, applications of the proposed method to mixture models involving elec-tricity distribution data and simulated data are presented

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Peer reviewedPostprin

Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing it with the underlying industrial activity structure. Specifically, we apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. In particular, by taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging.Comment: 31 pages, 17 figure