A highly selective vehicle classification utilizing dual-loop inductive detector

• Autorzy: Gajda J. , Stencel M.

Identyfikatory

DOI

10.2478/mms-2014-0040

• Języki publikacji: EN

Abstrakty

EN

In general, currently employed vehicle classification algorithms based on the magnetic signature can distinguish among only a few vehicle classes. The work presents a new approach to this problem. A set of characteristic parameters measurable from the magnetic signature and limits of their uncertainty intervals are determined independently for each predefined class. The source of information on the vehicle parameters is its magnetic signature measured in a system that enables independent measurement of two signals, i.e. changes in the active and reactive component of the inductive loop impedance caused by a passing vehicle. These innovations result in high selective classification system, which utilizes over a dozen vehicle classes. The evaluation of the proposed approach was carried out for good vehicles consisting of 2-axle tractor and a 3-axle semi-trailer.

Słowa kluczowe

EN

inductive loop detectors; magnetic signature; vehicle classification

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2014
• Tom: Vol. 21, nr 3
• Strony: 473--484
• Opis fizyczny: Bibliogr. 9 poz., rys., tab., wykr.

Twórcy

autor

Gajda J.

• AGH-University of Science and Technology, Department of Measurement and Electronics, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Stencel M.

• AGH-University of Science and Technology, Department of Measurement and Electronics, Al. Mickiewicza 30, 30-059 Krakow, Poland

Bibliografia

• [1] Pursula, M., Kosonen, I. (1989). Microprocessor and PC-based vehicle classification equipment using induction loops. In Proc. IEE 2nd Int. Conf. Road Traffic Monit, 24-28.
• [2] Gajda, J., Sroka, R., Stencel M., Zeglen. T. (2000) An Eastern European example of the identification of moving vehicle parameters using the tried and trusted method of weigh in motion. Traffic Technol. Int., 87-90.
• [3] Sun, C. (2000) An investigation in the use of inductive loop signatures for vehicle classification. Inst. Transp. Stud., Univ. California, Berkeley, CA, California PATH Res. Rep., UCB-ITS-PRR-2000 4.
• [4] Gajda, J., Sroka, R., Stencel, M., Wajda, A., Zeglen, T. (2001). A vehicle classification based on inductive loop detectors. In Proc. 18th IEEE IMTC, (1), 460-464.
• [5] Sroka, R. (2004) Data fusion methods based on fuzzy measures in vehicle classification process. In Proc. 21st IEEE IMTC, (3), 2234-2239.
• [6] Zhang, G.H., Wang, Y. H., Wie, H. (2006) Artificial neural network method for length-based vehicle classification using single-loop outputs. Traffic Urban Data, Transp. Res. Rec., (1945), 100-108.
• [7] Ki, Y.K., Baik, D.K. (2006) Vehicle classification algorithm for single loop detectors using neural networks. IEEE Trans. Veh. Technol., (55), 1704-1711.
• [8] Meta, S., Cinsdikici, M.G. (2010) Vehicle-Classification Algorithm Based on Component Analysis for Single-Loop Inductive Detector. IEEE Trans. Veh. Technol, (59), 2795-2805.
• [9] Gajda, J., Piwowar, P., Sroka, R., Stencel, M., Zeglen, T. (2012) Application of inductive loops as wheel detectors. Transportation Research, Part C, Emerging Technologies, (21), 57-66.