Estimation of time-varying origin-destination flows from traffic counts: A neural network approach
✍ Scribed by Yang Hai; T. Akiyama; T. Sasaki
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 930 KB
- Volume
- 27
- Category
- Article
- ISSN
- 0895-7177
No coin nor oath required. For personal study only.
✦ Synopsis
A dynamic model based on the error back-propagation learning principle in neural network theory ie 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 end observed exiting counts which are normalized using a logistic function. Two numerical experiments are conducted to evaluate the performance of the propoeed model; one usea 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-deatination flows with a hiih stability. @ 1998 Eleevier Science Ltd. All rights reserved.