Advances in Engineering Advances in Engineering features breaking research judged by Advances in Engineering advisory team to be of key importance in the Engineering field. Papers are selected from over 10,000 published each week from most peer reviewed journals.
Computer-Aided Civil and Infrastructure Engineering, Volume 28, Issue 8, pages 621–634, 2013.
Heng Wei, Hao Liu, Qingyi Ai, Zhixia Li, Hui Xiong, Benjamin Coifman.
College of Engineering & Applied Science, University of Cincinnati, Cincinnati, OH, USA and
Department of Civil and Environment Engineering, University of Wisconsin-Madison, Madison, WI, USA and
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China and
Department of Civil & Environmental Engineering & Geodetic Science, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, US.
Abstract
Clarifying traffic flow phases is a primary requisite for applying length-based vehicle classifications with dual-loop data under various traffic conditions. One challenge lies in identifying traffic phases using variables that could be directly calculated from the dual-loop data. This article presents an innovative approach and associated algorithm for identifying traffic phases through a hybrid method that incorporates level of service method and K-means clustering method. The “phase representative variables” are identified to represent traffic characteristics in the traffic flow phase identification algorithm. The traffic factors influencing the vehicle classification accuracy under non-free traffic conditions are successfully identified using video-based vehicular trajectory data, and the innovative length-based vehicle classification models are then developed. The result of the concept-of-evidence test with use of sample data indicates that compared with the existing model, the accuracy of the estimated vehicle lengths is increased from 42% to 92% under synchronized and stop-and-go conditions. The results also foster a better understanding of the traffic stream characteristics and associated theories to lay out a good foundation for further development of relevant microscopic simulation models with other sensing traffic data sources.
© 2013 Computer-Aided Civil and Infrastructure Engineering
