33,654 research outputs found
Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization
Statistical traffic data analysis is a hot topic in traffic management and
control. In this field, current research progresses focus on analyzing traffic
flows of individual links or local regions in a transportation network. Less
attention are paid to the global view of traffic states over the entire
network, which is important for modeling large-scale traffic scenes. Our aim is
precisely to propose a new methodology for extracting spatio-temporal traffic
patterns, ultimately for modeling large-scale traffic dynamics, and long-term
traffic forecasting. We attack this issue by utilizing Locality-Preserving
Non-negative Matrix Factorization (LPNMF) to derive low-dimensional
representation of network-level traffic states. Clustering is performed on the
compact LPNMF projections to unveil typical spatial patterns and temporal
dynamics of network-level traffic states. We have tested the proposed method on
simulated traffic data generated for a large-scale road network, and reported
experimental results validate the ability of our approach for extracting
meaningful large-scale space-time traffic patterns. Furthermore, the derived
clustering results provide an intuitive understanding of spatial-temporal
characteristics of traffic flows in the large-scale network, and a basis for
potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013
Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm
A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets
Clear Visual Separation of Temporal Event Sequences
Extracting and visualizing informative insights from temporal event sequences
becomes increasingly difficult when data volume and variety increase. Besides
dealing with high event type cardinality and many distinct sequences, it can be
difficult to tell whether it is appropriate to combine multiple events into one
or utilize additional information about event attributes. Existing approaches
often make use of frequent sequential patterns extracted from the dataset,
however, these patterns are limited in terms of interpretability and utility.
In addition, it is difficult to assess the role of absolute and relative time
when using pattern mining techniques.
In this paper, we present methods that addresses these challenges by
automatically learning composite events which enables better aggregation of
multiple event sequences. By leveraging event sequence outcomes, we present
appropriate linked visualizations that allow domain experts to identify
critical flows, to assess validity and to understand the role of time.
Furthermore, we explore information gain and visual complexity metrics to
identify the most relevant visual patterns. We compare composite event learning
with two approaches for extracting event patterns using real world company
event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data
Science (VDS), 201
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