Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Smartphone-based crowdsourcing for position estimation of public transport vehicles

© The Institution of Engineering and Technology
Received 25/09/2016, Accepted 13/08/2017, Revised 31/07/2017, Published 17/08/2017

In this research, a real-time positioning method, which utilises crowdsourced positioning data obtained from smartphone GPS is developed. Such vehicle location information obtained from crowdsourcing and smartphones in public transport could replace traditional automatic vehicle location systems. However, the location information from smartphone GPS is more erroneous. The proposed methodology serves as an alternative to existing positioning methods to improve the vehicle positioning accuracy. The developed enhanced particle filter algorithm takes smartphone GPS positioning data [from multiple passengers in a single transit vehicle (e.g. bus)] as input data. This ‘crowdsourced’ data can then be utilised to calculate the vehicles’ positioning information with better accuracy using the developed enhanced particle filter algorithm. The developed algorithm was tested using data collected on 14 different bus routes in urban and suburban areas of Mumbai, India, and it was identified that the algorithm is effective in reducing the average error up to 21.3% from a regular smartphone GPS and 10% from extended Kalman filter algorithm and was able to curtail positioning error within 8.672 m (average over 14 routes).

Inspec keywords: Global Positioning System; public transport; crowdsourcing; particle filtering (numerical methods); intelligent transportation systems; mobile computing; smart phones; traffic information systems

Other keywords: smartphone GPS positioning data; vehicle location information; bus routes; public transport vehicles; automatic vehicle location systems; smartphone-based crowdsourcing; enhanced particle filter algorithm; crowdsourced data; vehicle positioning accuracy; data collection; real-time positioning method; extended Kalman filter algorithm; position estimation

Subjects: Filtering methods in signal processing; Traffic engineering computing; Mobile, ubiquitous and pervasive computing; Digital signal processing; Radionavigation and direction finding

References

    1. 1)
      • 24. Schweiger, R., Neumann, H., Ritter, W.: ‘Multiple-cue data fusion with particle filters for vehicle detection in night view automotive applications’. Proc. IEEE Intelligent Vehicles Symp. 2005, 2005.
    2. 2)
      • 14. Marchetta, P., Natale, E., Salvi, A., et al: ‘Trusted information and security in smart mobility scenarios: the case of s2-move project’. Algorithms and Architectures for Parallel Processing 2013, 2013, pp. 185–192.
    3. 3)
      • 8. Corsar, D., Edwards, P., Nelson, J.D., et al: ‘Linking open data and the crowd for real-time passenger information’, J. Web Semant., 2017, 43, pp. 18–24. doi: http://dx.doi.org/10.1016/j.websem.2017.02.002.
    4. 4)
      • 33. Arulampalam, M.S., Maskell, S., Gordon, N., et al: ‘A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking’, IEEE Trans. Signal Process., 2002, 50, (2), pp. 107–113.
    5. 5)
      • 11. Howe, J.: ‘The rise of crowdsourcing’, Wired, June 2006.
    6. 6)
      • 7. Sheldon, P.J.: ‘Destination information systems’, Ann. Tourism Res., 1993, 20, (4), pp. 633–649.
    7. 7)
      • 25. Wendlandt, K., Khider, M., Angermann, M., et al: ‘Continuous location and direction estimation with multiple sensors using particle filtering’. 2006 IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems, 2006.
    8. 8)
      • 6. Velaga, N.R., Quddus, M.A., Bristow, A.L., et al: ‘Map-aided integrity monitoring of a land vehicle navigation system’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (2), pp. 848–858.
    9. 9)
      • 19. Quddus, M.A.: ‘High integrity map matching algorithms for advanced transport telematics applications’. PhD thesis, Centre for Transport Studies, Imperial College London, UK, 2006.
    10. 10)
      • 3. Chang, B.R., Tsai, H.F., Young, C.P.: ‘Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing’, Expert Syst. Appl., 2010, 37, (3), pp. 2439–2450.
    11. 11)
      • 41. Velaga, N.R.: ‘Development of a weight-based topological map-matching algorithm and an integrity method for location-based’. PhD thesis, Loughborough University, 2010.
    12. 12)
      • 40. Velaga, N.R., Quddus, M.A., Bristow, A.L.: ‘Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems’, Transp. Res. C: Emerg. Technol., 2009, 17, (6), pp. 672–683.
    13. 13)
      • 4. Shen, Z., Georgy, J., Korenberg, M.J., et al: ‘Low cost two dimensional navigation using an augmented Kalman filter/fast orthogonal search module for the integration of reduced inertial sensor system and global positioning system’, Transp. Res. Part C, 2011, 19, (6), pp. 1111–1131.
    14. 14)
      • 26. Salmon, D.C., Bevly, D.M.: ‘An exploration of low-cost sensor and vehicle model solutions for ground vehicle navigation’. Position, Location and Navigation Symp. – PLANS 2014, 2014 IEEE/ION, May 2014, pp. 462–471.
    15. 15)
      • 10. Lekshmy, V.V., Corsar, D., Joshi, S., et al: ‘Utilising the crowd to support a smart mobility system’. 96th Annual Meeting of the Transportation Research Board (TRB) of National Academies, Washington, DC, USA, 8–12 January 2017.
    16. 16)
      • 16. Motta, G., Sacco, D., Belloni, A., et al: ‘A system for green personal integrated mobility: a research in progress’. 2013 IEEE Int. Conf. on In Service Operations and Logistics, and Informatics (SOLI), July 2013, pp. 1–6.
    17. 17)
      • 13. Velaga, N.R., Beecroft, M., Nelson, J.D., et al: ‘Transport poverty meets the digital divide: accessibility and connectivity in rural communities’, J. Transp. Geogr., 2012, 21, pp. 102–112.
    18. 18)
      • 18. Chuang, Y.-T., Yi, C.W., Lu, Y.C., et al: ‘Itraffic: a smartphone-based traffic information system’. 42nd Int. Conf. on Parallel Processing, 2013.
    19. 19)
      • 2. Velaga, N.R., Pangbourne, K.: ‘Achieving genuinely dynamic road user charging: issues with a GNSS-based approach’, J. Transp. Geogr., 2014, 34, pp. 243–253.
    20. 20)
      • 32. Annapureddy, K., Finlow-Bates, K.: ‘Navigation using crowdsourcing data’. US Patent No. 9,336,681, 10 May 2016.
    21. 21)
      • 5. Velaga, N.R., Quddus, M.A., Bristow, A.L.: ‘Improving the performance of a topological map-matching algorithm through error detection and correction’, J. Intell. Transp. Syst., 2012, 16, (3), pp. 147–158.
    22. 22)
      • 1. Velaga, N.R., Nelson, J.D., Sripada, S., et al: ‘Development of a hybrid map-matching algorithm for rural passenger information systems via mobile phones and crowd-sourcing’, J. Comput. Civil Eng. ASCE, 2013, 27, (6), pp. 732–742.
    23. 23)
      • 31. Raol, J.R.: ‘Multi-sensor data fusion with MATLAB®’ (CRC Press, Boca Raton, Florida, USA, 2009), pp. 51–78.
    24. 24)
      • 12. Corsar, D., Edwards, P., Baillie, C., et al: ‘Getthere: a rural passenger information system utilising linked data & citizen sensing’. Proc. of the 2013th Int. Conf. on Posters & Demonstrations Track, 2013, vol. 1035, pp. 85–88.
    25. 25)
      • 36. Yin, S., Zhu, X.: ‘Intelligent particle filter and its application to fault detection of nonlinear system’, IEEE Trans. Ind. Electron., 2015, 62, (6), pp. 3852–3861.
    26. 26)
      • 15. Forbes, P.J., Wells, S., Masthoff, J., et al: ‘SUPERHUB: integrating behaviour change theories into a sustainable urban-mobility platform’. Proc. of Workshops Using Technology to Facilitate Behaviour Change and Support Healthy, Sustainable Living, BCS HCI, 2012.
    27. 27)
      • 22. Zandbergen, P., Barbeau, S.J.: ‘Positional accuracy of assisted GPS data from high-sensitivity GPS-enabled mobile phones’, J. Navig., 2011, 64, (03), pp. 381–399.
    28. 28)
      • 27. Du, L., Liu, N., Fang, R., et al: ‘A cooperative positioning scheme based on extended Kalman filter for curve warning systems’, Adv. Mater. Res., 2014, 915–916, pp. 1189–1193.
    29. 29)
      • 23. Zimmerman, J., Tomasic, A., Garrod, C., et al: ‘Field trial of Tiramisu: crowd-sourcing bus arrival times to spur co-design’. Conf. on Human–Computer Interaction (CHI), Vancouver, BC, Canada, 2011.
    30. 30)
      • 30. Venanzi, M.: ‘Learning models for data fusion and spatial regression with untrustworthy crowdsourced information’. Dissertation, University of Southampton, 2013, pp. 16–21.
    31. 31)
      • 37. Seliger, D.: ‘State estimation: particle filter’. Technical Report, Programming and Software Technique, June 2012.
    32. 32)
      • 29. Velaga, N.R., Sathiaseelan, A.: ‘Role of location based technologies in intelligent transportation systems’, Asian J. Inf. Technol., 2011, 10, (6), pp. 227–233.
    33. 33)
      • 38. Andrews, T.: ‘Computation time comparison between Matlab and C++ using launch windows’, 2012.
    34. 34)
      • 20. Kiran, M.K., Velaga, N.R., Ramasankaran, R.A.A.J.: ‘Development of two-stage extended Kalman filter for vehicle tracking from GPS enabled smart phones through crowd-sourcing’. 14th World Conf. on Transport Research – WCTR, Shanghai, China, 10–15 July 2016.
    35. 35)
      • 39. Greene, W.H.: ‘Econometric analysis’ (Pearson Education India, 2003).
    36. 36)
      • 21. Bergman, N.: ‘Recursive Bayesian estimation: navigation and tracking applications’. PhD dissertation, Linköping University, Linköping, Sweden, 1999.
    37. 37)
      • 34. Zhao, L., Ochieng, W.Y., Quddus, M.A., et al: ‘An extended Kalman filter algorithm for integrating GPS and low-cost dead reckoning system data for vehicle performance and emissions monitoring’, J. Navig., 2003, 56, pp. 257–275.
    38. 38)
      • 17. Neale, W., Danaher, D., McDonough, S., et al: ‘Data acquisition using smart phone applications’. SAE Technical Paper 2016-01-1461, 2016.
    39. 39)
      • 28. Wang, X., Ni, W.: ‘An improved particle filter and its application to an INS/GPS integrated navigation system in a serious noisy scenario’, Meas. Sci. Technol., 2016, 27, (9), pp. 1–10.
    40. 40)
      • 9. Chaudhary, M., Bansal, A., Bansal, D., et al: ‘Finding occupancy in buses using crowdsourced data from smartphones’. ICDCN ‘16, Singapore, 4–7 January 2016.
    41. 41)
      • 35. Gordon, N.J., Salmond, D.J., Smith, A.F.M.: ‘Novel approach to nonlinear/non-Gaussian Bayesian state estimation’, IEE Proc. F (Radar Signal Process.), 1993, 140, (2), pp. 107–113.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2016.0247
Loading

Related content

content/journals/10.1049/iet-its.2016.0247
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address