Type-2 fuzzy logic approach for short-term traffic forecasting

Type-2 fuzzy logic approach for short-term traffic forecasting

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

Buy article PDF
(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
Your details
Why are you recommending this title?
Select reason:
IEE Proceedings - Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The performance of many components in intelligent transportation systems depends heavily on the quality of short-term traffic forecasts. We propose a new method for forecasting traffic based on type-2 fuzzy logic. Type-2 fuzzy logic is powerful in handling uncertainties, including uncertainties in measurements and data used to calibrate the parameters. In our formulation, the value of a membership function corresponding to a particular traffic state is no longer a crisp value. Rather, it is associated with a range of values that can be characterised by a function that reflects the level of uncertainty. Day-to-day traffic information is combined with real-time traffic information to construct fuzzy rules. The performance of the prediction procedure based on type-2 fuzzy logic is encouraging. The mean relative error is in the neighbourhood of 12% for occupancies and 5% for flows. A distinct advantage of a type-2 fuzzy logic-based traffic forecasting approach is that it can produce prediction intervals as a by-product of the fuzzy reduction process. Another desirable property of the proposed model is that the fuzzy engine formulated is usually tractable at every step, making it easy to incorporate site-specific information into the model-building process to obtain more accurate results.


    1. 1)
      • Short term traffic forecasting using time series methods
    2. 2)
      • Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting
    3. 3)
      • Dynamic prediction of traffic volume through Kalman filter theory
    4. 4)
      • Non-parametric regression and short-term freeway traffic forecasting
    5. 5)
      • Turochy, R.E., Pierce, B.D.: `Relating short-term traffic forecasting to current system state using nonparametric regression', Proc. 2004 IEEE Intelligent Transportation Systems Conf., October 2004, Washington, D.C., p. 239–244
    6. 6)
      • Duncan, G. and Littlejohn, J.K.: ‘High Performance Microscopic Simulation for Traffic Forecasting’. IN: Computing and Control Division Colloquium on Strategic Control of Inter-Urban Road Networks, London, England: Strategic control of inter-urban road networks: Computing and Control Division colloquium (Institution of Electrical Engineers, 1997)
    7. 7)
      • Chrobok, R., Wahle, J., Schreckenberg, M.: `Traffic forecast using simulations of large scale networks', Proc. 2001 IEEE Intelligent Transportation Systems Conf., August 2001, Oakland, CA, p. 434–439
    8. 8)
      • Adaptive Forecasting of Freeway Traffic Congestion
    9. 9)
      • Traffic flow forecasting: comparison of modeling approaches
    10. 10)
      • Use of local linear regression model for short-term traffic forecasting
    11. 11)
      • Short-term traffic flow prediction: neural network approach
    12. 12)
      • Short term traffic forecasting using neural network, Transportation Systems: Theory and Application of Advanced Technology
    13. 13)
      • Neural network models for classification and forecasting of freeway traffic flow stability, Transportation System: Theory and Application of Advanced Technology
    14. 14)
      • Short-term freeway traffic volume forecasting using radial basis function neural network
    15. 15)
      • An object-oriented neural network approach to short-term traffic forecasting
    16. 16)
      • Hybrid model-based and memory-based traffic prediction system
    17. 17)
      • Uncertain rule-based fuzzy logic systems
    18. 18)
      • Skabardonis, A., Noeimi, H., Petty, K., Rydzewski, D, Varaiya, P.: `Freeway Service Patrol Evaluation', Research Report UCB-ITS-PRR-94, , (Institute of Transportation Studies, University of California, Berkeley, 1994)

Related content

This is a required field
Please enter a valid email address