Use of Data Mining Technology to Investigate Vehicle Speed in Winter Weather: a Case Study

Each winter, Alberta Transportation (AT) spends significant capital on winter road maintenance. It is not enough to use only “time to bare pavement” as the performance measure for winter maintenance. In addition, the databases from various Intelligent Transportation System services in AT are growing explosively every year. New techniques and tools are needed to intelligently transform the abundant of stored data into useful information and knowledge. Previous studies have shown that vehicle speed is a good measure and a fair assessment of winter maintenance operations. This study used the Apriori algorithm, a data mining technique, to investigate the association rules between vehicle speed and various factors. The traffic, weather, and winter maintenance operation data in Alberta are collected and integrated in the structured query language (SQL) server databases. Results of the case study confirmed the expected effects of several weather variables, including snow intensity and pavement surface conditions. The future study will add the collision data into the database for further analysis.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AH010 Surface Transportation Weather.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Liu, Gang
    • Shi, Ling
    • Lan, Cheng
    • Qiu, Tony Z
    • Fang, Jie
  • Conference:
  • Date: 2015

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 19p
  • Monograph Title: TRB 94th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01551963
  • Record Type: Publication
  • Report/Paper Numbers: 15-2382
  • Files: PRP, TRIS, TRB, ATRI
  • Created Date: Jan 29 2015 9:17AM