Vehicle modeling for the analysis of the response of detectors based on inductive loops

English version

Autoři: Ferran Mocholí Belenguer ^aff001; Antonio Martínez Millana ^aff002; Antonio Mocholí Salcedo ^aff003; Victor Milián Sánchez ^aff004
Působiště autorů: Traffic Control Systems Group, ITACA Institute, Universitat Politècnica de València, Valencia, Spain ^aff001; SABIEN Group, ITACA Institute, Universitat Politècnica de València, Valencia, Spain ^aff002; Department of Electronic Engineering, ITACA Institute, Universitat Politècnica de València, Valencia, Spain ^aff003; Chemical and Nuclear Engineering Department, Institute of Industrial, Radiological and Environmental Safety, Universitat Politècnica de València, Valencia, Spain ^aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0218631

Souhrn

Magnetic loops are one of the most popular and used traffic sensors because of their widely extended technology and simple mode of operation. Nevertheless, very simple models have been traditionally used to simulate the effect of the passage of vehicles on these loops. In general, vehicles have been considered simple rectangular metal plates located parallel to the ground plane at a certain height close to the vehicle chassis. However, with such a simple model, it is not possible to carry out a rigorous study to assess the performance of different models of vehicles with the aim of obtaining basic parameters such as the vehicle type, its speed or its direction in traffic. For this reason and because computer simulation and analysis have emerged as a priority in intelligent transportation systems (ITS), this paper aims to present a more complex vehicle model capable of characterizing vehicles as multiple metal plates of different sizes and heights, which will provide better results in virtual simulation environments. This type of modeling will be useful when reproducing the actual behavior of systems installed on roads based on inductive loops and will also facilitate vehicle classification and the extraction of basic traffic parameters.

Klíčová slova:

Engineering and technology – Civil engineering – Transportation infrastructure – Roads – Transportation – Control engineering – Control systems – Mechanical engineering – Engines – Research and analysis methods – Simulation and modeling – Specimen preparation and treatment – Specimen sectioning – Computer and information sciences – Physical sciences – Mathematics – Systems science – Biology and life sciences – Neuroscience – Cognitive science – Cognitive psychology – Intelligence – Psychology – Social sciences

Zdroje

1. World Health Organization (WHO), Global Report on Road Safety 2013: Supporting a Decade of Action, WHO Press, World Health Organization, Geneva, Switzerland, 2013.

2. Hosseinpour F, and Hajihosseini H. Importance of Simulation in Manufacturing. World Academy of Science, Engineering and Technology International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering Vol:3, No:3, 2009.

3. Anderson RL. Electromagnetic loop vehicle detectors. IEEE Transactions on Vehicular Technology; vol. VT-19, no. 1, pp. 23–30, 1970.

4. Prucha MJ, and View M. Inductive loop vehicle presence detector. U.S. Patent 3 576 525, Apr. 27, 1971.

5. Koerner RJ, and Park C. Inductive loop vehicle detector. U.S. Patent 3 989 932, Nov. 2, 1976.

6. Patrick HM, and Raymond JL. Vehicle presence loop detector. U.S. Patent 4 472 706, Sep. 18, 1984.

7. Clark MAG. Induction loop vehicle detector. U.S. Patent 4 568 937, Feb. 4, 1986.

8. Lee SH, Oh Y, and Lee S. New loop detector installation guidelines for real-time adaptive signal control system. Journal of the Eastern Asia Society for Transportation Studies; vol. 6, pp. 2337–2348, 2005.

9. Liu K, Jia J, Zuo Z. and Ando R. Heterogeneity in the effectiveness of cooperative crossing collision prevention systems. Transportation Research Part C: Emerging Technologies, 2018, Vol.87, 1–10.

10. Peng Y, Jiang Y, Lu J, Zou Y (2018) Examining the effect of adverse weather on road transportation using weather and traffic sensors. PLoS ONE 13(10): e0205409. https://doi.org/10.1371/journal.pone.0205409 30325948

11. Liu K, Cui M-Y, Cao P, Wang J-B (2016) Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data. PLoS ONE 11(6): e0158123. https://doi.org/10.1371/journal.pone.0158123 27362654

12. Ki YK, and Baik DK. Vehicle-classification algorithm for single-loop detectors using neural networks. IEEE Transactions on Vehicular Technology; vol. 55, no. 6, pp. 1704–1711, 2006.

13. Zheng Z, Wang C, Wang P, Xiong Y, Zhang F, Lv Y (2018) Framework for fusing traffic information from social and physical transportation data. PLoS ONE 13(8): e0201531. https://doi.org/10.1371/journal.pone.0201531 30071064

14. Pursula M and Kosonen I. Microprocessor and PC-based vehicle classification equipments using induction loops. Proceedings of the IEEE Second International Conference on Road Traffic Monitoring and Control; pp. 24–28, 1989.

15. Gajda J, Sroka R, Stencel M, Wajda A, and Zeglen T. A vehicle classification based on inductive loop detectors. Proceedings of the IEEE Instrumentation and Measurement Technology Conference, Budapest; pp. 460–464, 2001.

16. Nihan NL. Evaluation of forced flows on freeways with single-loop detectors. Journal of Advanced Transportation; vol. 34, no. 2, pp. 269–296, 2000.

17. Ametha J, Tumer S, and Darbha S. Formulation of a new methodology to identify erroneous paired loop detectors. Proceedings of the IEEE Intelligent Transportation Systems, Oakland; pp. 591–596, 2001.

18. Ki YK and Baik DK. Model for accurate speed measurement using double-loop detectors. IEEE Transactions on Vehicular Technology; vol. 55, no. 4, pp. 1094–1101, Jul, 2006.

19. Lin WH, Dahlgren J, and Huo H. Enhancement of vehicle speed estimation with single loop detectors. Journal of the Transportation Research Board; vol. 1870, pp. 147–152, 2004.

20. Tang J, Zou Y, Ash J, Zhang S, Liu F, Wang Y (2016) Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System. PLoS ONE 11(2): e0147263. https://doi.org/10.1371/journal.pone.0147263 26829639

21. Wei X, Xu C, Wang W, Yang M, Ren X (2017) Evaluation of average travel delay caused by moving bottlenecks on highways. PLoS ONE 12(8): e0183442. https://doi.org/10.1371/journal.pone.0183442 28854213

22. Tok A, Hernandez SV, and Ritchie SG. Accurate individual vehicle speeds from single inductive loop signatures. Proceedings of 88th Annual Meeting of the Transportation Research Board, National Research Council, Washington, D.C, USA, 2009, paper 09–3512.

23. Hilliard SR. Vehicle speed estimation using inductive vehicle detection systems. United States Patent 6999886, Feb. 2003.

24. Sun C, and Ritchie SG. Individual Vehicle Speed Estimation Using Single Loop Inductive Waveforms. Journal of Transportation Engineering; vol. 126, No. 6, 1999, pp. 531–538.

25. Gajda J, Piwowar P, Sroka R, Stencel M, and Zeglen T. Application of inductive loops as wheel detectors. Transportation Research Part C: Emerging Technologies; vol. 21, no. 1, pp. 57–66, 2012.

26. Marszalek Z, Sroka R, Zeglen T. Inductive loop for vehicle axle detection from first concepts to the system based on changes in the sensor impedance components. Proceedings of 20th international conference on methods and models in automation and robotics, 24–27, August 2015, Miedzyzdroje, Poland, pp 765–769.

27. Arroyo Núñez JH, Mocholí Salcedo A, Barrales Guadarrama R, and Arroyo Nuñez A. Communication between magnetic loops. Proceedings of 16th World Road Meeting, Lisbon, Portugal, May 2010.

28. Gajda J and Burnos P. Identification of the spatial impulse response of inductive loop detectors. IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2015, pp. 1997–2002.

29. Klein LA, Gibson DRP, and Mills MK. Traffic Detector Handbook. FHWAHRT-06-108. Federal Highway Administration, U.S. Department of Transportation 2006.

30. Mills MK. Inductive loop system equivalent circuit model. Proceedings of the 39th Vehicular Technology Conference, May 1989, pp. 689–700.

31. Davies P. Vehicle Detection and Classification, Information Technology Applications in Transport. VNU Science Press, Haarlem, Netherlands, 1986, pp. 11–40.

32. Grover FW. Inductance Calculations: Working Formulas and Tables. New York, NY, USA: Dover, 1962, p. 34

33. Mills MK. Self-Inductance Formulas for Multi- Turn Rectangular Loops Used with Vehicle Detectors. 33rd IEEE VTG Conference Record, May 1983, pp. 64–73.

34. Mocholi Salcedo A, Arroyo Nunez JH, Milian Sanchez V, Palomo Anaya MJ, and Arroyo Nunez A. Magnetic field generated by the loops used in traffic control systems. IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 8, pp. 2126–2136, Aug 2017.

35. Mocholí Belenguer F, Mocholí Salcedo A, Milián Sánchez V, and Arroyo Núñez JH. Double Magnetic Loop and Methods for Calculating Its Inductance. Journal of Advanced Transportation; vol. 2018, Article ID 6517137, 15 pages, 2018. https://doi.org/10.1155/2018/6517137.

36. Mocholí Salcedo A, Arroyo Núñez JH, Milián Sánchez VM, Verdú Martín GJ, and Arroyo Nuñez A. Traffic Control Magnetic Loops Electric Characteristics Variation Due to the Passage of Vehicles Over Them. IEEE Transactions On Intelligent Transportation Systems, vol. 18, no. 6, pp. 1540–1548, 2017.

37. Mocholí Belenguer F, Mocholí Salcedo A, Guill Ibañez A, Milián Sánchez V (2019) Advantages offered by the double magnetic loops versus the conventional single ones. PLoS ONE 14(2): e0211626. https://doi.org/10.1371/journal.pone.0211626 30753200

38. Feng C, Suren C, Xiaoxiang M. Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. Journal of safety research. 2018. 65: 153–159. doi: 10.1016/j.jsr.2018.02.010 29776524

39. Ma X, Chen S, Chen F. Multivariate space-time modeling of crash frequencies by injury severity levels. Analytic Methods in Accident Research, 2017, vol 15, pp 29–40.

40. Chen F, Song M, Ma X, and Zhu X. Assess the Impacts of Different Autonomous Trucks’ Lateral Control Modes on Asphalt Pavement Performance. Transportation Research Part C: Emerging Technologies 103 (April). Elsevier: 17–29. doi: 10.1016/j.trc.2019.04.001

41. Wu G, Chen F, Pan X, Xu M, Zhu X. Using the Visual Intervention Influence of Pavement Marking for Rutting Mitigation–Part II: Visual Intervention Timing Based on the Finite Element Simulation. International Journal of Pavement Engineering 20 (5). Taylor & Francis: 573–584. doi: 10.1080/10298436.2017.1316646

Vehicle modeling for the analysis of the response of detectors based on inductive loops

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