A metric for pattern-matching applications to traffic management

Richard Mounce*, Garry Hollier, Mike Smith, Victoria J Hodge, Tom Jackson, Jim Austin

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This paper considers signal plan selection; the main topic is the design of a system for utilising pattern matching to assist the timely selection of sound signal control plan changes. In this system, historical traffic flow data is continually searched, seeking traffic flow patterns similar to today's. If, in one of these previous similar situations, (a) the signal plan utilised was different to that being utilised today and (b) it appears that the performance achieved was better than the performance likely to be achieved today, then the system recommends an appropriate signal plan switch. The heart of the system is "similarity". Two traffic flow patterns (two time series of traffic flows arising from two different days) are said to be "similar" if the distance between them is small; similarity thus depends on how the metric or distance between two time series of traffic flows is defined. A simple example is given which suggests that utilising the standard Euclidean distance between the two sequences comprising cumulatives of traffic flow may be better than utilising the standard Euclidean distance between the original two sequences of traffic flow data. The paper also gives measured on-street public transport benefits which have arisen from using a simple rule-based (traffic-responsive) signal plan selection system, compared with a time-tabled signal plan selection system.

Original languageEnglish
Pages (from-to)148-155
Number of pages8
JournalTransportation Research Part C: Emerging Technologies
Volume29
DOIs
Publication statusPublished - Apr 2013

Keywords

  • cumulatives
  • intelligent decision support
  • pattern matching
  • signal plan selection

ASJC Scopus subject areas

  • Transportation
  • Automotive Engineering
  • Management Science and Operations Research
  • Computer Science Applications

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