Research Article | OPEN ACCESS
Traffic Demand Forecast of Road in Kigali, Rwanda
1Zhang Chao, 2Cao Peng and 1Li Jianbo
1China Road and Bridge Corporation, C88 Zhonglu Plaza Andingmenwai Street, Beijing
2Department of Transportation Science and Engineering, Harbin Institute of Technology, Haihe Street, Nangang District, Harbin
Research Journal of Applied Sciences, Engineering and Technology 2013 2:546-552
Received: May 15, 2012 | Accepted: June 08, 2012 | Published: January 11, 2013
Abstract
Accurate forecasting of traffic demand is one of the most important issues in the feasibility study on highway projects. The existing traffic forecasting models, to some extent, have the problem of limited accuracy. In this study, two widely used methods which are Elastic Coefficient Method (ECM) and Motorized Travel Frequency Method (MTFM) were comprehensively applied to forecast the traffic volume in Kigali, Rwanda. And Comparative analysis was made between the forecasting result and the actual survey traffic result in the project’s future years. Compared with the actual survey result, the predicted result of ECM is larger and relative error is 10.49%. The result of MTFM is smaller and relative error is -7.11%. While the weighted average of above methods is closer to the actual result with a relative error in the interval of -5.00 to 5.00%. The research has shown that the combined forecast method proposed in this study, which can make up the defects in accuracy of single model, is easy to operate and owns more accuracy in traffic prediction. This study has suggested that proper combination of several methods would be an advisable trend for the traffic demand forecasting.
Keywords:
Elastic coefficient method, motorized travel frequency method, traffic demand forecast,
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.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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