Urban Arterial Travel Time Prediction Using Support Vector Regression

  • Anna Mary Philip
  • Gitakrishnan Ramadurai
  • Lelitha Vanajakshi
Original Article


Travel time is one of the most appropriate traffic measures that can be provided to road users for easy understanding and decision making. This paper presents a simple method to predict travel time on urban arterial roads. Travel time prediction methods include historical methods, model based methods, and data driven approaches. Of these, data driven approaches capture the uncertainty and non-linearity of traffic time series better and hence could be used for travel time prediction under Indian traffic conditions effectively. Among data driven approaches, support vector regression (SVR) models predict travel times with reasonable accuracy, especially when the amount of data is less or the variability in the data is high. However, studies on the use of SVR for travel time prediction under Indian traffic conditions are limited. In this paper, SVR technique was used to predict the travel time using data collected from Bluetooth sensors placed at specific locations on an urban arterial corridor in Chennai, India. The optimum number of inputs, appropriate kernel function, cost parameter and width of tolerance for the SVR model were determined. The results obtained show that the SVR performed better than an Artificial Neural Network model and moving average approach.


Travel time prediction Support vector regression Bluetooth data Sensitivity analysis Heterogeneous traffic 



The authors acknowledge the opportunity provided by the 4th Conference of the Transportation Research Group of India (4th CTRG) held at IIT Bombay, Mumbai, India between 17th December, 2017 and 20th December, 2017 to present the work that forms the basis of this manuscript. The authors also acknowledge the support for this study as a part of the project RB/16-17/CIE/001/TATC/LELI under the Development of a Dynamic Traffic Congestion Prediction System for Indian Cities, funded by Tata Consultancy Services.


  1. 1.
    Dharia A, Adeli H (2003) Neural network model for rapid forecasting of freeway link travel time. Eng Appl Artif Intell 16(7):607–613CrossRefGoogle Scholar
  2. 2.
    Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, New YorkCrossRefzbMATHGoogle Scholar
  3. 3.
    IIT M (2014) Traffic data collection and analysis for the research study on Advanced Traveller Information System (ATIS) for Indian cities. Tech. rep.
  4. 4.
    Li R, Rose G (2011) Incorporating uncertainty into short-term travel time predictions. Transp Res Part C Emerg Technol 19(6):1006–1018CrossRefGoogle Scholar
  5. 5.
    Liang Z, Ling-Xiang Z (2006) Link travel time estimation model fusing data from mobile and stationary detector based on BP neural network. In: Communications, Circuits and Systems Proceedings, International Conference on, vol 3. IEEE, pp 2146–2149Google Scholar
  6. 6.
    Mori U, Mendiburu A, Alvarez M, Lozano JA (2015) A review of travel time estimation and forecasting for advanced traveller information systems. Transp A Transp Sci 11(2):119–157Google Scholar
  7. 7.
    Nikovski D, Nishiuma N, Goto Y, Kumazawa H (2005) Univariate short-term prediction of road travel times. In: Intelligent Transportation Systems. Proceedings. IEEE, pp 1074–1079, 13 Sep 2005Google Scholar
  8. 8.
    Park D, Rilett LR (1999) Forecasting freeway link travel times with a multilayer feedforward neural network. Comput Aided Civil Infrastruct Eng 14(5):357–367CrossRefGoogle Scholar
  9. 9.
    Qiao W, Haghani A, Shao CF, Liu J (2016) Freeway path travel time prediction based on heterogeneous traffic data through nonparametric model. J Intell Transp Syst 20(5):438–448CrossRefGoogle Scholar
  10. 10.
    Ramezani M, Geroliminis N (2012) On the estimation of arterial route travel time distribution with markov chains. Transp Res Part B Methodol 46(10):1576–1590CrossRefGoogle Scholar
  11. 11.
    Reddy KK, Kumar BA, Vanajakshi L (2016) Bus travel time prediction under high variability conditions. Curr Sci 111(4):700CrossRefGoogle Scholar
  12. 12.
    Rice J, Van Zwet E (2004) A simple and effective method for predicting travel times on freeways. IEEE Trans Intell Transp Syst 5(3):200–207CrossRefGoogle Scholar
  13. 13.
    Theja P, Vanajakshi L (2010) Short term prediction of traffic parameters using support vector machines technique. In: Emerging Trends in Engineering and Technology (ICETET), 3rd International Conference on 2010 November 19. IEEE, pp 70–75Google Scholar
  14. 14.
    Van Lint J (2006) Reliable real-time framework for short-term freeway travel time prediction. J Transp Eng 132(12):921–932CrossRefGoogle Scholar
  15. 15.
    Vanajakshi L, Rilett LR (2007) Support vector machine technique for the short term prediction of travel time. In: Intelligent Vehicles Symposium. IEEE, pp 600–605, 13 June 2007Google Scholar
  16. 16.
    Wu CH, Ho JM, Lee DT (2004) Travel-time prediction with support vector regression. IEEE Trans Intell Transp Syst 5(4):276–281CrossRefGoogle Scholar
  17. 17.
    Yeon J, Elefteriadou L, Lawphongpanich S (2008) Travel time estimation on a freeway using discrete time markov chains. Transp Res Part B Methodol 42(4):325–338CrossRefGoogle Scholar
  18. 18.
    Zeng X, Zhang Y (2013) Development of recurrent neural network considering temporal-spatial input dynamics for freeway travel time modeling. Comput Aided Civil Infrastruct Eng 28(5):359–371CrossRefGoogle Scholar
  19. 19.
    Zhang X, Rice JA (2003) Short-term travel time prediction. Transp Res Part C Emerg Technol 11(3):187–210CrossRefGoogle Scholar
  20. 20.
    Zheng F, Van Zuylen H (2013) Urban link travel time estimation based on sparse probe vehicle data. Transp Res Part C Emerg Technol 31:145–157CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Anna Mary Philip
    • 1
  • Gitakrishnan Ramadurai
    • 1
  • Lelitha Vanajakshi
    • 1
  1. 1.Department of Civil EngineeringIndian Institute of Technology MadrasChennaiIndia

Personalised recommendations