Estimation of time-varying origin-destination flows from traffic counts: A neural network approach

  • Authors:

  • Yang Hai;T. Akiyama;T. Sasaki

  • Affiliations:

  • Department of Civil and Structural Engineering The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;Department of Transportation Engineering, Faculty of Engineering Kyoto University, Kyoto 606, Japan;Department of Transportation Engineering, Faculty of Engineering Kyoto University, Kyoto 606, Japan

  • Venue:
  • Mathematical and Computer Modelling: An International Journal
  • Year:
  • 1998

Quantified Score

Hi-index 0.98

Visualization

Abstract

A dynamic model based on the error back-propagation learning principle in neural network theory is proposed for estimating origin-destination flows from the road entering and exiting counts in a transportation network. The origin-destination flows in each short time interval are estimated through minimization of the squared errors between the predicted and observed exiting counts which are normalized using a logistic function. Two numerical experiments are conducted to evaluate the performance of the proposed model; one uses a typical four-way intersection, and the other one uses a real freeway section. Numerical results show that the back-propagation based model is capable of tracking the time variations of the origin-destination flows with a high stability.