期刊
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
卷 47, 期 -, 页码 139-154出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2014.06.011
关键词
Traffic volume; Forecasting method; Data mining; Neural networks; Flocking phenomena; Missing data
资金
- National Natural Science Foundation of China [712620]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China
- Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region, China [NJYT-13-B02]
The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical. (C) 2014 Elsevier Ltd. All rights reserved.
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