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.

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Document Details

Document Type
Technical Report
Publication Date
Mar 08, 2001
Accession Number
AD1020010

Entities

People

  • Chang-tien Lu
  • Pusheng Zhang
  • Sanjay Chawla
  • Shashi Shekhar

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Computer Science
  • Data Analysis
  • Data Mining
  • Data Sets
  • Data Visualization
  • Data Warehousing
  • Databases
  • Detection
  • Detectors
  • Geographic Information Systems
  • Information Science
  • Machine Learning
  • Network Science
  • New York

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Pavement Materials Engineering.

Technology Areas

  • AI & ML