Neural network approach to classification of traffic flow states
Abstract
The classification of traffic flow states in China has traditionally been based on the Highway Capacity Manual, published in the United States. Because traffic conditions are generally different from country to country, though, it is important to develop a practical and useful classification method applicable to Chinese highway traffic. In view of the difficulty and complexity of a mathematical and physical realization, modern pattern recognition methods are considered practical in fulfilling this goal. This study applies a self-organizing neural network pattern recognition method to classify highway traffic states into some distinctive cluster centers. A small scale test with actual data is conducted, and the method is found to be potentially applicable in practice.
- Authors:
-
- Hong Kong Univ. of Science and Technology, Kowloon (Hong Kong). Dept. of Civil Engineering
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 316104
- Resource Type:
- Journal Article
- Journal Name:
- Journal of Transportation Engineering
- Additional Journal Information:
- Journal Volume: 124; Journal Issue: 6; Other Information: PBD: Nov-Dec 1998
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; TRAFFIC CONTROL; CHINA; PATTERN RECOGNITION; NEURAL NETWORKS
Citation Formats
Yang, H, and Qiao, F. Neural network approach to classification of traffic flow states. United States: N. p., 1998.
Web. doi:10.1061/(ASCE)0733-947X(1998)124:6(521).
Yang, H, & Qiao, F. Neural network approach to classification of traffic flow states. United States. https://doi.org/10.1061/(ASCE)0733-947X(1998)124:6(521)
Yang, H, and Qiao, F. 1998.
"Neural network approach to classification of traffic flow states". United States. https://doi.org/10.1061/(ASCE)0733-947X(1998)124:6(521).
@article{osti_316104,
title = {Neural network approach to classification of traffic flow states},
author = {Yang, H and Qiao, F},
abstractNote = {The classification of traffic flow states in China has traditionally been based on the Highway Capacity Manual, published in the United States. Because traffic conditions are generally different from country to country, though, it is important to develop a practical and useful classification method applicable to Chinese highway traffic. In view of the difficulty and complexity of a mathematical and physical realization, modern pattern recognition methods are considered practical in fulfilling this goal. This study applies a self-organizing neural network pattern recognition method to classify highway traffic states into some distinctive cluster centers. A small scale test with actual data is conducted, and the method is found to be potentially applicable in practice.},
doi = {10.1061/(ASCE)0733-947X(1998)124:6(521)},
url = {https://www.osti.gov/biblio/316104},
journal = {Journal of Transportation Engineering},
number = 6,
volume = 124,
place = {United States},
year = {Sun Nov 01 00:00:00 EST 1998},
month = {Sun Nov 01 00:00:00 EST 1998}
}