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

access icon openaccess Urban link travel time estimation using traffic states-based data fusion

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 02/04/2017, Accepted 28/01/2018, Revised 28/11/2017, Published 14/02/2018

Estimated travel time is a key input for many intelligent transport systems (ITS) applications and traffic management functions. There are numerous studies that show that fusing data from different sources such as global positioning system (GPS), Bluetooth, mobile phone network (MPN), and inductive loop detector (ILD) can result in more accurate travel time estimation. However, to date, there has been little research investigating the contribution of individual data sources to the quality of the final estimate or how this varies according to source-specific data quality under different traffic states. Here, three different data sources, namely bus-based GPS (bGPS) data, ILD data, and MPN data, of varying quality are combined using three different data fusion techniques of varying complexity. In order to quantify the accuracy of travel time estimation, travel time calculated using automatic number plate recognition (ANPR) data are used as the ‘ground truth’. The final results indicate that fusing multiple data together does not necessarily enhance the accuracy of travel time estimation. The results also show that even in dense urban areas, bGPS data, when combined with ILD data, can provide reasonable travel time estimates of general traffic stream under different traffic states.

Inspec keywords: traffic engineering computing; intelligent transportation systems; sensor fusion

Other keywords: automatic number plate recognition; Bluetooth; urban link travel time estimation; intelligent transport systems; bGPS data; ILD data; traffic states-based data fusion; MPN; GPS; inductive loop detector; ILD; ANPR data; mobile phone network; bus-based GPS; MPN data; traffic management functions; ground truth; ITS applications; global positioning system

Subjects: Traffic engineering computing; Data handling techniques

References

    1. 1)
      • 6. Alonso, M., Plaza, J.: ‘Developing HMI components for a driver assistance system for safe speed and safe distance’, Adv. Transp. Stud., 2010, 3, (21), pp. 5–14.
    2. 2)
      • 5. Tang, J., Zou, Y., Ash, J., et al: ‘Travel time estimation using freeway point detector data based on evolving fuzzy neural inference system’, PLoS ONE, 2016, 11, (2), pp. 1–24.
    3. 3)
      • 11. Han, J., Krishnan, R., Polak, J.W., et al: ‘A new method for probabilistic traffic state identification using loop detector data: theory and empirical results’. UTSG, 2010.
    4. 4)
      • 22. Wong, A., Hounsell, N.: ‘Using the iBus system to provide improved public transport information and applications for London’. 12th World Conf. on Transport Research, 2010, pp. 1–8.
    5. 5)
      • 20. Chen, C.: ‘Detecting errors and imputing missing data for single loop surveillance systems’, Transp. Res. Rec. J. Transp. Res. Board, 2003, 1855, pp. 160–167.
    6. 6)
      • 9. Choi, K., Chung, Y.: ‘A data fusion algorithm for estimating link travel time’, J. Intell. Transp. Syst., 2010, 7, (3), pp. 235–260.
    7. 7)
      • 13. Pirc, J., Turk, G., Žura, M.: ‘Highway travel time estimation using multiple data sources’, IET Intell. Transp. Syst., 2016, 10, (10), pp. 649–657.
    8. 8)
      • 15. Zeng, X., Zhang, Y.: ‘Development of recurrent neural network considering temporal-spatial input dynamics for freeway travel time modeling’, Comput. Civ. Infrastruct. Eng., 2013, 28, (5), pp. 359–371.
    9. 9)
      • 12. Van Lint, J.W.C., Hoogendoorn, S.P., van Zuylen, H.J.: ‘Accurate freeway travel time prediction with state-space neural networks under missing data’, Transp. Res. C Emerg. Technol., 2005, 13, (5), pp. 347–369.
    10. 10)
      • 14. Guo, F., Polak, J.W., Krishnan, R.: ‘Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions’, IEEE Conf. Intelligent Transportation Systems. Proc., ITSC, 2010, pp. 1209–1214.
    11. 11)
      • 18. Robinson, S., Polak, J.: ‘Overtaking rule method for the cleaning of matched license-plate data’, J. Transp. Eng., 2006, 132, (8), pp. 609–617.
    12. 12)
      • 23. Daganzo, C. F.: ‘Fundamentals of transportation and traffic operations’ (Emerald Group Publishing Limited, Oxford, 1997).
    13. 13)
      • 21. Robinson, S., Polak, J.: ‘Use of the k-NN method to estimate travel time in Central London’, Traffic Eng. Control, 2006, 47, (10), pp. 408–411.
    14. 14)
      • 2. Jenelius, E., Koutsopoulos, H.N.: ‘Travel time estimation for urban road networks using low frequency probe vehicle data’, Transp. Res. B Methodol., 2013, 53, (8), pp. 64–81.
    15. 15)
      • 19. Hunt, P.B., Robertson, D.I., Bretherton, R.D., et al: ‘SCOOT – a traffic responsive method of coordinating signals’ (Crowthrone, Berkshire, UK, 1981).
    16. 16)
      • 1. Mori, U., Mendiburu, A., Álvarez, M., et al: ‘A review of travel time estimation and forecasting for advanced traveller information systems’, Transp. A Transp. Sci., 2014, 11, (2), pp. 119–157.
    17. 17)
      • 16. Arezoumandi, M.: ‘Estimation of travel time reliability for freeways using mean and standard deviation of travel time’, J. Transp. Syst. Eng. Inf. Technol., 2011, 11, (6), pp. 74–84.
    18. 18)
      • 10. Guo, F., Polak, J.W., Krishnan, R.: ‘Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions’. 13th Int. IEEE Conf. on Intelligent Transportation Systems, 2010, pp. 1209–1214.
    19. 19)
      • 17. Bachmann, C.: ‘Multi-sensor data fusion for traffic speed and travel time estimation’. PhD thesis, University of Toronto, 2011.
    20. 20)
      • 7. Hall, D.L., McMullen, S.A.H.: ‘Mathematical techniques in multisensor data fusion’ (Artech House, Norwood, MA, 1992).
    21. 21)
      • 4. Iqbal, M.S., Choudhury, C.F., Wang, P., et al: ‘Development of origin–destination matrices using mobile phone call data’, Transp. Res. C Emerg. Technol., 2014, 40, pp. 63–74.
    22. 22)
      • 3. Zheng, F., Van Zuylen, H.: ‘Urban link travel time estimation based on sparse probe vehicle data’, Transp. Res. C Emerg. Technol., 2013, 31, (111), pp. 145–157.
    23. 23)
      • 8. Han, J.: ‘Multi-sensor data fusion for travel time estimation’. PhD thesis, Imperial College London, 2012.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0116
Loading

Related content

content/journals/10.1049/iet-its.2017.0116
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address