Research Article | OPEN ACCESS
The Method of Traffic State Identification and Travel Time Prediction on Urban Expressway
1, 2Yanguo Huang, 1Lunhui Xu and 1Qiang Luo
1Department of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510640, China
2Department of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
Research Journal of Applied Sciences, Engineering and Technology 2013 4:1271-1277
Received: June 28, 2012 | Accepted: August 17, 2012 | Published: February 01, 2013
Abstract
This study presented a method of real-time traffic condition identification based on the fuzzy c-means clustering and travel time prediction on urban expressway. The traffic flow characteristics of expressway were analyzed and the traffic flow states were divided into four classes. Then the fuzzy c-means clustering technique was used to classify the sampled historical data and the clustering center of different traffic condition was gotten. In the test module, the real-time traffic data were used to identify which state the traffic data belong to. Based on the analysis, a travel time prediction model of urban expressway was given by using fuzzy regression. According to the collecting real-time traffic dada and the result of traffic state identification, a method of predicting travel time was introduced. Finally, an urban expressway in Guanzhou was as an example and the result of traffic state identification was same with the results of actual measurement data and questionnaire survey through drivers. The result of travel time prediction show that the predicted results had better fitting degree and precision and the feasibility of this method was verified. It can provide the basis for urban expressway traffic control and traffic induction.
Keywords:
Fuzzy C-means clustering, fuzzy regression, traffic flow state, travel time prediction, urban expressway,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
|
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
|
Information |
|
|
|
Sales & Services |
|
|
|