Modeling historical traffic data using artificial neural networks

Mohammad S. Ghanim, Ghassan Abu-Lebdeh, Kamran Ahmed

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

4 Citations (Scopus)

Abstract

The Design-Hour Volume (DHV), which is defined as the 30th highest hour volume in a year, is a significant concept in transportation engineering and planning. Finding the DHV requires hourly traffic counts for an entire year. However, this becomes a challengeable task when part of the data is not collected because of different reasons, such as construction activities or hardware failure. In this paper, an Artificial Neural Network (ANN) approach is used to develop a DHV prediction model based on historical traffic counts. The model takes into account the correlation between DHV and other variables such as AADT, functional classification, and number of lanes. Results show that the ANN model is capable of providing accurate and reliable DHV estimates.

Original languageEnglish
Title of host publication2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013
DOIs
Publication statusPublished - Aug 19 2013
Event2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013 - Hammamet, Tunisia
Duration: Apr 28 2013Apr 30 2013

Publication series

Name2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013

Other

Other2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013
Country/TerritoryTunisia
CityHammamet
Period4/28/134/30/13

Keywords

  • Artificial Neural Networks
  • Design Hour Volume
  • Historical Traffic Modeling
  • Traffic Forecast

ASJC Scopus subject areas

  • Control and Optimization
  • Modelling and Simulation

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