Data Mining and Visualization of Twin-Cities Traffic Data

Abstract

Data Mining(DM) is the process of extracting implicit, valuable, and interesting information from large sets of data. As huge amounts of data have been stored in traffic and transportation databases, data warehouses, geographic information systems, and other information repositories, data mining is receiving substantial interest from both academia and industry. The Twin-Cities traffic archival stores sensor network measurements collected from the freeway system in the Twin-Cities metropolitan area. In this paper, we construct a traffic data warehousing model which facilitates on-line analytical processing(OLAP), employ current data mining techniques to analyze the Twin-Cities traffic data set, and visualize the discoveries on the highway map. We also discuss some research issues in mining traffic and transportation data.