Empirical Innovation of Computational Dual-Loop Models for Identifying Vehicle Classifications against Varied Traffic Conditions

Loop data gained from freeways have been increasingly applied to generate traffic information for various traffic analysis applications. The clarifying of traffic flow phases is essential for applying length-based vehicle classifications with dual-loop data under various traffic conditions. A challenge lies in identifying traffic phases using variables that could be directly calculated from the dual-loop data. 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 are presented in this paper. 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 presented in this paper provide an understanding of the traffic stream characteristics and the associated theories to lay out a good foundation for further development of relevant microscopic simulation models with other sensing traffic data sources. The capability of measuring vehicle lengths makes dual-loop detectors a potential real-tie source for vehicle classification.


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  • Accession Number: 01492031
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 3 2013 12:30PM