RESEARCH ARTICLE


Macroscopic Congestion Intensity Measurement Model Based on Cumulative Logistic Regression



Lei Yu*, 1, Menghan Liu2, Qinyi Shi3, Guohua Song4
1 Department of Transportation Studies, College of Science and Technology, Texas Southern University, USA; Beijing Jiaotong University, Beijing, China
2 Highway Transport Division, Transport Planning and Research Institute, Ministry of Communications, Building No. 2, A6, Shuguang Xili, Chaoyang District, Beijing, 100028, P.R. China
3 Graduate Research Assistant, Department of Transportation Studies, Texas Southern University, 3100 Cleburne Avenue, Houston, Texas, 77004, USA
4 MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, #3 Shangyuancun, Haidian District, Beijing, 100044, P. R. China

Article Information


Identifiers and Pagination:

Year: 2010
Volume: 4
First Page: 44
Last Page: 51
Publisher ID: TOTJ-4-44
DOI: 10.2174/1874447801004010044

Article History:

Received Date: 17/10/2009
Revision Received Date: 11/11/2009
Acceptance Date: 11/11/2009
Electronic publication date: 1/12/2010
Collection year: 2010

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Creative Commons License
© 2010 Yuet al;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Transportation Studies, College of Science and Technology, Texas Southern University, USA; Beijing Jiaotong University,3100 Cleburne Avenue, Houston, Texas 77004, USA; Tel: 713-313-7007; Fax: 713-313-1853; E-mail: Yu_VLX@TSU.EDU


Abstract

An efficient and accurate measurement of congestion intensity helps investigate the traffic conditions at different road classes and provides useful information for transportation planning and traffic operation improvement. However, the existing traffic congestion intensity measurement models often suffer from two major problems: one is that there is no generally accepted method that can be used to classify the grades of congestion intensity, and the other is that the variables that have been incorporated into the exiting congestion intensity measurement models are often inter-correlated. In order to overcome these two deficiencies, this paper analyzes the ordinal characteristics of congestion intensity, and introduces the cumulative logistic regression into the congestion intensity measurement model.

In the model development process, first it adopts the likelihood ratio test to validate the adaptability of the cumulative logistic regression and Wald test to select the independent variables. Then, it develops the measurement model of congestion intensity by using travel speed as the independent variable. The proposed model shows a determination coefficient (pseudo R2) higher than 0.77 in the goodness-of-fit test, and a prediction with the accuracy of 73.39% against the field observed data. Therefore, the proposed model can be effectively used to determine the traffic congestion intensity on different road classes.

Keywords: Traffic congestion intensity, cumulative logistic regression, likelihood ratio test, wald test.