Approaches for freeway work zone capacity estimation: Incorporating probe vehicle data

Research output: Contribution to conferencePaperpeer-review

Abstract

This study presents an Artificial Neural Network (ANN) model for estimating work zone capacity by taking into consideration various factors affecting the capacity, such as approaching traffic volume, average work zone speed, and travel time. To overcome the deficiencies of the existing parametric model-based approaches, probe vehicle data are incorporated into the work zone capacity estimation procedure. Exploiting a VISSIM-based hypothetical work zone environment on a freeway, the accuracy of estimated capacity produced by the ANN model with and without probe vehicle data is examined. Simulation results show that the accuracy of estimated capacity incorporating probe-based travel time is improved up to 5%.

Original languageEnglish (US)
StatePublished - Jan 1 2014
Event21st World Congress on Intelligent Transport Systems: Reinventing Transportation in Our Connected World, ITSWC 2014 - Detroit, United States
Duration: Sep 7 2014Sep 11 2014

Other

Other21st World Congress on Intelligent Transport Systems: Reinventing Transportation in Our Connected World, ITSWC 2014
CountryUnited States
CityDetroit
Period9/7/149/11/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Automotive Engineering
  • Transportation
  • Electrical and Electronic Engineering

Keywords

  • Artificial neural network
  • Capacity
  • Probe vehicle data
  • Work zone

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