NEW ALGORITHM FOR GROUPING OBSERVATIONS FROM A LARGE TRANSPORTATION DATA BASE

In this paper is presented a new cluster-segmentation algorithm. Its distance measure, derived by using Fisher's likelihood theory, depends on the probability density function (frequency function) of the observations. The resulting measure of similarity or dissimilarity is consistent with the likelihood theory. It shows attractive features: (a) curtailment of cluster-segmentation techniques; each probability density function has its own optimal measure of similarity or dissimilarity; (b) detection of dependencies between variables; and (c) all the advantages of hierarchical divisive techniques, which makes it suitable for analysis of large transportation surveys. The use of the new algorithm is illustrated by using a large data base, the Netherlands National Travel Survey. The goal of this research is to analyze mobility (expressed in daily mileage) by constructing homogeneous population groups. This example clearly demonstrates that the methodology can satisfactorily deal with numerous observations.

• Record URL:
  • http://onlinepubs.trb.org/Onlinepubs/trr/1986/1090/1090-008.pdf
• Record URL:
  • https://scholar.google.com/scholar_lookup?title=NEW+ALGORITHM+FOR+GROUPING+OBSERVATIONS+FROM+A+LARGE+TRANSPORTATION+DATA+BASE&author=R.+Hamerslag&author=W.+SCHELTES&publication_year=1986
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/0309041104
• Supplemental Notes:
  • Publication of this paper sponsored by Committee on Transportation Data and Information Systems. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved
• Authors:
  • Hamerslag, Rudi
  • Scheltes, Wim H
• Publication Date: 1986

Media Info

• Media Type: Print
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 52-60
• Monograph Title: Statewide data collection and management systems
• Serial:
  • Transportation Research Record
  • Issue Number: 1090
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Algorithms; Cluster analysis; Data analysis; Data collection; Databases; Distributions (Statistics); Mathematical analysis; Mobility; Surveys; Travel patterns
• Uncontrolled Terms: Clustering
• Old TRIS Terms: Segmentation
• Subject Areas: Administration and Management; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 00471784
• Record Type: Publication
• ISBN: 0309041104
• Files: TRIS, TRB
• Created Date: Mar 31 1988 12:00AM