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

Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses

Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of vehicles in India has increased tremendously, leading to severe traffic congestion and pollution in urban areas, particularly during peak periods. A desirable strategy to deal with such issues is to shift more people from personal vehicles to public transport by providing better service (comfort, convenience and so on). In this context, advanced public transportation systems (APTS) are one of the most important ITS applications, which can significantly improve the traffic situation in India. One such application will be to provide accurate information about bus arrivals to passengers, leading to reduced waiting times at bus stops. This needs a real-time data collection technique, a quick and reliable prediction technique to calculate the expected travel time based on real-time data and informing the passengers regarding the same. The scope of this study is to use global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India, to predict travel times under heterogeneous traffic conditions using an algorithm based on the Kalman filtering technique. The performance of the proposed algorithm is found to be promising and expected to be valuable in the development of APTS in India. The work presented here is one of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions.

References

    1. 1)
      • Hoffman, C., Janko, J.: `Travel time as a basis of the LISB guidance strategy', Proc. IEEE Road Traffic Control Conf., IEEE, 1990, p. 6–10.
    2. 2)
      • Jeong, R., Rilett, L.R.: `Bus arrival time prediction model for real-time applications', Transportation Research Record: Journal of the Transportation Research Board, No. 1927, TRB, National Research Council, 2005, Washington, D.C., p. 195–204.
    3. 3)
      • Chien, S., Liu, X., Ozbay, K.: `Predicting travel times for the south jersey real-time motorist information system', Transportation Research Board, National Research Council, 2003, Washington, D.C., CD-ROM.
    4. 4)
      • Yang, J.S.: `Travel time prediction using the GPS test vehicle and Kalman filtering techniques', American Control Conf., 2005, Portland, OR, USA, p. 2128–2133.
    5. 5)
      • Xiao, H., Sun, H., Ran, B.: `The fuzzy-neural network traffic prediction framework with wavelet decomposition', Transportation Research Board, National Research Council, 2003, Washington, D.C., CD-ROM.
    6. 6)
      • Chamberlain, R.G.: ‘Great circle distance between 2 points’, http://www.movable-type.co.uk/scripts/gis-faq-5.1.html, accessed November 10, 2007.
    7. 7)
      • Chen, M., Chien, S.I.J.: `Dynamic freeway travel time prediction using probe vehicle data: link based vs. path based', Presented at the 80th Annual Meeting (CDROM), TRB, National Research Council, 2001, Washington, D.C..
    8. 8)
    9. 9)
      • Liu, H., van Lint, H., van Zuylen, H., Zhang, K.: `Two distinct ways of using Kalman filters to predict urban arterial travel time', IEEE Conference on Intelligent Transportation Systems, 2006, p. 845–850.
    10. 10)
      • Van Lint, J.W.C., Hoogendoorn, S.P., van Zuylen, H.J.: `Freeway travel time prediction with state space neural networks', Transportation Research Board, National Research Council, 2002, Washington, D.C., CD-ROM.
    11. 11)
      • Chien, S.I.J., Kuchipudi, C.M.: `Dynamic travel time prediction with real time and historical data', Presented at the 81st Annual Meeting (CDROM), TRB, National Research Council, 2002, Washington, D.C..
    12. 12)
      • Thakuriah, P., Sen, A., Li, J., Liu, N., Koppelman, F.S., Bhat, C.: `Data needs for short term link travel time prediction', Advance working paper series number 19, Urban Transportation Center, University of Illinois, 1992, Chicago.
    13. 13)
      • R.E. Kalman . New approach to linear filtering and prediction problems. Trans. ASME, J. Basic Eng. D , 35 - 45
    14. 14)
      • Nanthawichit, C., Nakatsuji, T., Suzuki, H.: `Application of probe vehicle data for real time traffic state estimation and short term travel time prediction on a freeway', Transportation Research Board, National Research Council, 2003, Washington, D.C., CD-ROM.
    15. 15)
      • Shalaby, A., Farhan, A.: `Bus travel time prediction model for dynamic operations control and passenger information systems', Transportation Research Board, National Research Council, 2003, Washington, D.C., CD-ROM.
    16. 16)
      • Chu, L., Oh, J.S., Recker, W.: ‘Adaptive Kalman filter based freeway travel time estimation’, http://www.its.uci.edu/its/publications/papers/JOURNALS/TRB_05-118.pdf, accessed July 1, 2007.
    17. 17)
      • Rice, J., van Zwet, E.: `A simple and effective method for predicting travel times on freeways', IEEE Intelligent Transportation Systems Conf. Proc., 2001, p. 227–232.
    18. 18)
      • Sen, A., Liu, N., Thakuriah, P., Li, J.: `Short-term forecasting of link travel times: a preliminary proposal', ADVANCE Working Paper Series, No. 7, 1991.
    19. 19)
      • D'Angelo, M.P., Al-Deek, H.M., Wang, M.C.: `Travel time prediction for freeway corridors', Transportation Research Record: Journal of the Transportation Research Board, No. 1676, TRB, National Research Council, 1998, Washington, D.C., p. 184–191.
    20. 20)
      • S. Ishak , H. Al-Deek . Performance evaluation of short term time series traffic prediction model. J. Transp. Eng., ASCE , 6 , 490 - 498
    21. 21)
      • Vanajakshi, L., Rilett, L.R.: `A comparison of the performance of artificial neural networks and support vector machines for the prediction of vehicle speed', IEEE Intelligent Vehicles Symp., 2004, p. 194–199.
    22. 22)
      • Chowdhury, M., Sadek, A., Ma, Y., Kanhere, N., Bhavsar, P.: `Applications of artificial intelligence paradigms to decision support in real-time traffic management', Transportation Research Record: Journal of the Transportation Research Board, No.1968, TRB, National Research Council, 2006, Washington, D.C., p. 92–98.
    23. 23)
      • Kwon, J., Coifman, B., Bickel, P.: `Day-to-day travel time trends and travel time prediction from loop detector data', Transportation Research Record: Journal of the Transportation Research Board, No. 1717, TRB, National Research Council, 2000, Washington, D.C., p. 120–129.
    24. 24)
      • Liu, H., van Zuylen, H., van Lint, H., Salomons, M.: `Urban arterial travel time prediction with state-space neural networks and Kalman filters', TRB Annual Meeting, 2006, CD-ROM.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its_20080013
Loading

Related content

content/journals/10.1049/iet-its_20080013
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address