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Cost-effective ubiquitous method for motor vehicle speed estimation using smartphones

Cost-effective ubiquitous method for motor vehicle speed estimation using smartphones

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Estimating longitudinal vehicle speed is required in a wide range of applications from road safety to vehicular emissions modelling. Each vehicle speed estimation method has specific challenges including the high cost of measurement equipment, the small range of vehicle models accessible for performing tests and the low resolution of data in time and space. Thus, the goals of this study are (a) to investigate the use of smartphones’ integrated sensors as a convenient, reliable and powerful means of vehicle speed data collection which would mitigate the issues observed in previous methods, and (b) to propose a post-processing pipeline for generating vehicles’ speed profiles based on the raw data captured by the proposed data collection method. Accordingly, a smartphone application is developed to facilitate the collection of vehicles’ movement data, particularly inside tunnels and high-density urban areas, where state-of-the-art global positioning system (GPS) applications fail to record the desired data. Moreover, a post-processing pipeline is proposed for calculating vehicles’ speed profiles and emissions from the raw smartphone acceleration data, and the full system is evaluated through a series of controlled experiments as well as simulations.

References

    1. 1)
      • 1. Tanelli, M., Savaresi, S.M., Cantoni, C.: ‘Longitudinal vehicle speed estimation for traction and braking control systems’. 2006 IEEE Conf. Computer Aided Control System Design, 2006, pp. 27902795.
    2. 2)
      • 2. Wu, L.J.: ‘Experimental study on vehicle speed estimation using accelerometer and wheel speed measurements’. 2011 Second Int. Conf. Mechanic Automation and Control Engineering, 2011, pp. 294297.
    3. 3)
      • 3. Zhang, Z., Zhao, T., Ao, X., et al: ‘A vehicle speed estimation algorithm based on dynamic time warping approach’, IEEE Sens. J., 2017, 17, (8), pp. 24562463.
    4. 4)
      • 4. Chowdhury, A., Chakravarty, T., Balamuralidhar, P.: ‘A novel approach to improve vehicle speed estimation using smartphone's INS/GPS sensors’. Int. Conf. Sensing Technology (ICST), 2014.
    5. 5)
      • 5. Smit, R., Ntziachristos, L., Boulter, P.: ‘Validation of road vehicle and traffic emission models – a review and meta-analysis’, Atmos. Environ., 2010, 44, (25), pp. 29432953.
    6. 6)
      • 6. Zamboni, G., André, M., Roveda, A., et al: ‘Experimental evaluation of heavy duty vehicle speed patterns in urban and port areas and estimation of their fuel consumption and exhaust emissions’, Transp. Res. D, Transp. Environ., 2015, 35, pp. 110.
    7. 7)
      • 7. Odat, E., Shamma, J.S., Claudel, C.: ‘Vehicle classification and speed estimation using combined passive infrared/ultrasonic sensors’, IEEE Trans. Intell. Transp. Syst., 2017, 19, (5), pp. 114.
    8. 8)
      • 8. Dunne, J.G.: ‘Laser–based speed measuring device’, ed: Google Patents, 1994.
    9. 9)
      • 9. Kupersmit, C.: ‘Vehicle speed monitoring system’, ed: Google Patents, 1998.
    10. 10)
      • 10. Cevher, V., Chellappa, R., McClellan, J.H.: ‘Vehicle speed estimation using acoustic wave patterns’, IEEE Trans. Signal Process., 2009, 57, (1), pp. 3047.
    11. 11)
      • 11. Cheung, S., Coleri, S., Dundar, B., et al: ‘Traffic measurement and vehicle classification with single magnetic sensor’, Transp. Res. Rec., J. Transp. Res. Board, 2005, 1917, (1), pp. 173181.
    12. 12)
      • 12. Smit, R., McBroom, J.: ‘Development of a new high resolution traffic emissions and fuel consumption model for Australia and New Zealand-data quality considerations’, Air Qual. Clim. Change, 2009, 43, (2), pp. 1316.
    13. 13)
      • 13. Yu, J., Zhu, H., Han, H., et al: ‘Senspeed: sensing driving conditions to estimate vehicle speed in urban environments’, IEEE Trans. Mob. Comput., 2016, 15, (1), pp. 202216.
    14. 14)
      • 14. Thiagarajan, A., Ravindranath, L., LaCurts, K., et al: ‘VTrack: accurate, energy-aware road traffic delay estimation using mobile phones’. Proc. Seventh ACM Conf. Embedded Networked Sensor Systems, Berkeley, CA, 2009, pp. 8598.
    15. 15)
      • 15. Gundlegard, D., Karlsson, J.M.: ‘Handover location accuracy for travel time estimation in GSM and UMTS’, IET Intell. Transp. Syst., 2009, 3, (1), pp. 8794.
    16. 16)
      • 16. Bevly, D.M., Gerdes, J.C., Wilson, C., et al: ‘The use of GPS based velocity measurements for improved vehicle state estimation’. American Control Conf., 2000, vol. 4, pp. 25382542.
    17. 17)
      • 17. Assemi, B., Safi, H., Mesbah, M., et al: ‘Developing and validating a statistical model for travel mode identification on smartphones’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (7), pp. 19201931.
    18. 18)
      • 18. Feng, T., Timmermans, H.J.P.: ‘Transportation mode recognition using GPS and accelerometer data’, Transp. Res. C, Emerg. Technol., 2013, 37, pp. 118130.
    19. 19)
      • 19. Chakravarty, T., Ghose, A., Bhaumik, C., et al: ‘Mobidrivescore – a system for mobile sensor based driving analysis: a risk assessment model for improving one's driving’. Seventh Int. Conf. Sensing Technology (ICST), 2013, pp. 338344.
    20. 20)
      • 20. Liu, M.: ‘A study of mobile sensing using smartphones’, Int. J. Distrib. Sens. Netw., 2013, 9, (3), pp. 111.
    21. 21)
      • 21. Ravi, N., Dandekar, N., Mysore, P., et al: ‘Activity recognition from accelerometer data’. Am. Assoc. Artif. Intell., 2005, 5, pp. 15411546.
    22. 22)
      • 22. Apple: ‘CMMotionmanager’. 17 January 2017. Available at https://developer.apple.com/documentation/coremotion/cmmotionmanager, accessed October 2017.
    23. 23)
      • 23. Seifert, K., Camacho, O.: ‘Implementing positioning algorithms using accelerometers’, Freescale Semicond., Application Note (AN3397)2007, pp. 113.
    24. 24)
      • 24. Safi, H., Assemi, B., Mesbah, M., et al: ‘Design and implementation of a smartphone-based system for personal travel survey: case study from New Zealand’, Transp. Res. Record, J. Transp. Res. Board, 2015, 2526, pp. 99107.
    25. 25)
      • 25. Apple: ‘Apple developer documentation’. 2015. Available at https://developer.apple.com/documentation/, accessed March 2015.
    26. 26)
      • 26. Arraigada, M., Partl, M.: ‘Calculation of displacements of measured accelerations, analysis of two accelerometers and application in road engineering’. Proc. Sixth Swiss Transport Research Conf., Ascona, Switzerland, 2006, vol. 1517.
    27. 27)
      • 27. Orfanidis, S.J.: ‘Introduction to signal processing’ (Prentice-Hall, Englewood Cliffs, NJ, 1995).
    28. 28)
      • 28. Kendall, M.G., Stuart, A.: ‘Design and analysis, and time-series(The advanced theory of statistics). (Macmillan, London, UK, 1983, 4th edn.).
    29. 29)
      • 29. Gander, W., Hrebicek, J.: ‘Solving problems in scientific computing using Maple and MATLAB®’ (Springer Science & Business Media, Berlin, 2011).
    30. 30)
      • 30. Tretter, S.A.: ‘Communication system design using DSP algorithms: with laboratory experiments for the TMS320C6713TM DSK’ (Kluwer Academic/Plenum Publishers, New York, 2003, 1st edn.).
    31. 31)
      • 31. Singh, S.P.: ‘Approximation theory, spline functions and applications’. Proc. NATO Advanced Study Institute on Approximation Theory, Spline Functions and Applications Maratea, Italy, 28 April–9 May 1992, Springer Science & Business Media.
    32. 32)
      • 32. SINTEF: ‘Data acquired by torque Pro (Senseapp) and an ODB II Bluetooth adapter monitoring a car’, https://github.com/SINTEF-9012/sensapp/tree/master/net.modelbased.sensapp.data.samples/OBD_II, accessed September 2018.
    33. 33)
      • 33. Wahlström, J., Skog, I., Händel, P.: ‘Smartphone-based vehicle telematics – a ten-year anniversary’, arXiv preprint arXiv:1611.03618, 2016.
    34. 34)
      • 34. Google: ‘Elevation API’. 2018, 12/09/2018. Available at https://developers.google.com/maps/documentation/elevation/start, accessed September 2018.
    35. 35)
      • 35. Smit, R.: ‘Transport emissions simulation’, in Parry-Jones, M. (Ed.): ‘Vehicle technology engineer’ (Society of Automotive Engineers, Australasia, 2014).
    36. 36)
      • 36. Smit, R.: ‘Development and performance of a new vehicle emissions and fuel consumption software (PΔP) with a high resolution in time and space’, Atmos. Pollut. Res. (APR), 2013, 4, (3), pp. 336345.
    37. 37)
      • 37. R Core Team: ‘R: a language and environment for statistical computing’, (R Foundation for Statistical Computing, Vienna, Austria, 2017).
    38. 38)
      • 38. RStudio: ‘RStudio’, ver. 1.1.453, Boston, MA, 2018.
    39. 39)
      • 39. Fritsch, S., Guenther, F., Suling, M., et al: ‘Neuralnet: training of neural networks’. CRAN Package, ver. 1.33, 2016.
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