Pitfall in using average travel speed in traffic signalized intersection networks

Bongsoo Son, Jae Hwan Maeng, Young Jun Han, Bong Gyou Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For the effective use and management of urban traffic control systems, it is necessary to collect and process traffic data and produce traffic information. Average travel speed is typically used for classifying traffic conditions of traffic signalized intersection networks. The purpose of this paper is to solve the pitfall caused by the usage of average travel speed estimated by conventional technique for the signalized intersection networks. To do this, this paper has suggested the basis of criteria for selecting the speed data to be used for final estimation of travel speed. The key point is to check the relevancy of travel time of the vehicles traveled during the same evaluation time period.

Original languageEnglish
Title of host publicationMobile Ad-hoc and Sensor Networks - First International Conference, MSN 2005, Proceedings
Pages1059-1064
Number of pages6
DOIs
Publication statusPublished - 2005 Dec 1
Event1st International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2005 - Wuhan, China
Duration: 2005 Dec 132005 Dec 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3794 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2005
CountryChina
CityWuhan
Period05/12/1305/12/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Son, B., Maeng, J. H., Han, Y. J., & Lee, B. G. (2005). Pitfall in using average travel speed in traffic signalized intersection networks. In Mobile Ad-hoc and Sensor Networks - First International Conference, MSN 2005, Proceedings (pp. 1059-1064). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3794 LNCS). https://doi.org/10.1007/11599463_103