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

access icon free Short-term traffic flow prediction using fuzzy information granulation approach under different time intervals

  • Author(s): Jianhua Guo 1 ; Zhao Liu 1 ; Wei Huang 1 ; Yun Wei 2 ; Jinde Cao 3
    • View affiliations
  • Source: Volume 12, Issue 2, March 2018, p. 143 – 150
    DOI:  10.1049/iet-its.2017.0144 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Received 02/05/2017, Accepted 26/11/2017, Revised 01/11/2017, Published 29/11/2017

Short-term traffic flow forecasting has been regarded as essential for intelligent transportation systems, including both point prediction and interval prediction. Compared with point prediction, interval prediction of traffic flow in the future will be critical for traffic managers to make reasonable decisions. This study applies the fuzzy information granulation method to obtain the dispersion range of the collected traffic flow time series, and classical forecasting approaches of K-nearest neighbours, back-propagation neural network, and support vector regression are applied on the dispersion range and the original series itself, constituting a short-term traffic flow forecasting system with the capability of joint point and interval prediction. Using real-world traffic flow data collected from a field transportation system in America, the proposed forecasting system is shown to generate workable point prediction and associated prediction interval, demonstrating the validity of the proposed forecasting system. In addition, for unravelling the impact of time interval on the forecasting system, different time intervals are investigated, showing that with the increase in time interval, the stability of the forecasting system increases. Discussions are provided for the proposed approach, and future works are expected to enhance the proposed forecasting system.

Inspec keywords: time series; backpropagation; support vector machines; neural nets; traffic information systems; regression analysis

Other keywords: support vector regression; traffic flow forecasting; stability; K-nearest neighbours; short-term traffic flow prediction; short-term traffic flow forecasting system; back-propagation neural network; point prediction; fuzzy information granulation method; interval prediction; traffic flow time series

Subjects: Traffic engineering computing; Other topics in statistics; Neural computing techniques; Knowledge engineering techniques

References

    1. 1)
      • 8. Innamaa, S.: ‘Short-term prediction of traffic situation using MLP-neural networks’. Proc. 7th World Congress on Intelligent Transport Systems, Turin, Italy, 2000, pp. 6–9.
    2. 2)
      • 30. Yu, F., Pedrycz, W.: ‘The design of fuzzy information granules: tradeoffs between specificity and experimental evidence’, Appl. Soft Comput., 2009, 9, (1), pp. 264–273.
    3. 3)
      • 2. Guo, J., Huang, W., Williams, B.M.: ‘Integrated heteroscedasticity test for vehicular traffic condition series’, J. Transp. Eng., 2012, 138, (9), pp. 1161–1170.
    4. 4)
      • 21. Habtemichael, F.G., Cetin, M.: ‘Short-term traffic flow rate forecasting based on identifying similar traffic patterns’, Transp. Res. Part C, 2016, 66, pp. 61–78.
    5. 5)
      • 31. Chang, H., Lee, Y., Yoon, B., et al: ‘Dynamic near-term traffic flow prediction: system oriented approach based on past experiences’, IET Intell. Transport Syst., 2012, 6, (3), pp. 292–305.
    6. 6)
      • 12. Xie, Y., Zhang, Y.: ‘A wavelet network model for short-term traffic volume forecasting’, J. Intell. Transport. Syst.: Technol. Plann. Oper., 2006, 10, (3), pp. 141–150.
    7. 7)
      • 27. Maciel, L., Ballini, R., Gomide, F.: ‘Evolving granular analytics for interval time series forecasting’, Granular Comput., 2016, 1, (4), pp. 213–224.
    8. 8)
      • 4. Sun, Y., Shao, C., Ji, X., et al: ‘Urban traffic accident time series prediction model based on combination of ARIMA and information granulation SVR’, Tsinghua Sci. Technol., 2014, 54, (3), pp. 348–353+359.
    9. 9)
      • 22. Zhang, Y., Zhang, Y.: ‘A comparative study of three multivariate short-term freeway traffic flow forecasting methods with missing data’, J. Intell. Transport. Syst.: Technol. Plann. Oper., 2016, 20, (3), pp. 205–218.
    10. 10)
      • 10. Jiang, X., Adeli, H.: ‘Dynamic wavelet neural network model for traffic flow forecasting’, J. Transp. Eng., 2005, 131, (10), pp. 771–779.
    11. 11)
      • 14. Guo, J., Williams, B.: ‘Real-time short-term traffic speed level forecasting and uncertainty quantification using layered Kalman filters’, Transp. Res. Rec., 2010, 2175, pp. 28–37.
    12. 12)
      • 11. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: ‘Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach’, Transp. Res. Part C, 2005, 13, (3), pp. 211–234.
    13. 13)
      • 18. Yoon, B., Chang, H.: ‘Potentialities of data-driven nonparametric regression in urban signalized traffic flow forecasting’, J. Transp. Eng., 2014, 140, (7), 04014027.
    14. 14)
      • 6. Smith, B.L., Ulmer, J.M.: ‘Freeway traffic flow rate measurement: investigation into impact of measurement time interval’, J. Transp. Eng., 2003, 129, (3), pp. 223–229.
    15. 15)
      • 20. Guo, J., Huang, W., Williams, B.M.: ‘Real time traffic flow outlier detection using short-term traffic conditional variance prediction’, Transp. Res. Part C, 2015, 50, pp. 160–172.
    16. 16)
      • 3. Guo, J., Williams, B., Smith, B.: ‘Data collection time intervals for stochastic short-term traffic flow forecasting’, Transp. Res. Rec., 2008, 2024, pp. 18–26.
    17. 17)
      • 25. Zhang, Y., Zhang, Y., Haghani, A.: ‘A hybrid short term traffic flow forecasting method based on spectral analysis and statistical volatility model’, Transp. Res. Part C, 2014, 43, pp. 65–78.
    18. 18)
      • 1. Smith, B.L., Williams, B.M., Oswald, R.K.: ‘Comparison of parametric and nonparametric models for traffic flow forecasting’, Transp. Res. Part C, 2002, 10, (4), pp. 303–321.
    19. 19)
      • 7. Dougherty, M.S., Cobbett, M.R.: ‘Short-term inter-urban traffic forecasts using neural networks’, Int. J. Forecasting, 1997, 13, (1), pp. 21–31.
    20. 20)
      • 16. Wang, J., Shi, Q.: ‘Short-term traffic speed forecasting hybrid model based on chaos–wavelet analysis-support vector machine theory’, Transp. Res. Part C, 2013, 27, pp. 219–232.
    21. 21)
      • 5. Lu, W., Chen, X., Pedrycz, W., et al: ‘Using interval information granules to improve forecasting in fuzzy time series’, Int. J. Approx. Reason., 2015, 57, pp. 1–18.
    22. 22)
      • 34. Xu, W., Yu, J.: ‘A novel approach to information fusion in multi-source datasets: a granular computing viewpoint’, Inf. Sci., 2017, 378, pp. 410–423.
    23. 23)
      • 13. Castro-Neto, M., Jeong, Y.S., Jeong, M.K., et al: ‘Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions’, Expert Syst. Appl., 2009, 36, (3), pp. 6164–6173.
    24. 24)
      • 24. Ma, X., Tao, Z., Wang, Y., et al: ‘Long short-term memory neural network for traffic speed prediction using remote microwave sensor data’, Transp. Res. Part C, 2015, 54, pp. 187–197.
    25. 25)
      • 9. Chen, H., Grant-Muller, S., Mussone, L., et al: ‘A study of hybrid neural network approaches and the effects of missing data on traffic forecasting’, Neural Comput. Appl., 2001, 10, (3), pp. 277–286.
    26. 26)
      • 15. Zhang, Y., Liu, Y.: ‘Traffic forecasting using least squares support vector machines’, Transportmetrica, 2009, 5, (3), pp. 193–213.
    27. 27)
      • 17. Zhu, J.Z., Cao, J.X., Zhu, Y.: ‘Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections’, Transp. Res. Part C, 2014, 47, pp. 139–154.
    28. 28)
      • 28. Zadeh, L.A.: ‘Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic’, Fuzzy Sets Syst., 1997, 90, (2), pp. 111–127.
    29. 29)
      • 19. Cai, P., Wang, Y., Lu, G., et al: ‘A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting’, Transp. Res. Part C, 2016, 62, pp. 21–34.
    30. 30)
      • 33. Qu, X., Wang, W., Wang, W., et al: ‘Real-time freeway sideswipe crash prediction by support vector machine’, IET Intell. Transport Syst., 2013, 7, (4), pp. 445–453.
    31. 31)
      • 32. Zheng, Z., Su, D.: ‘Short-term traffic volume forecasting: a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm’, Transp. Res. Part C, 2014, 43, pp. 143–157.
    32. 32)
      • 26. Polus, A., Shwartzman, Y.: ‘Flow characteristics at freeway work zones and increased deterrent zones’, Transp. Res. Rec., 1999, 1657, pp. 18–23.
    33. 33)
      • 23. Tang, J., Liu, F., Zou, Y., et al: ‘An improved fuzzy neural network for traffic speed prediction considering periodic characteristic’, IEEE Trans. Intell. Transport. Syst., 2017, 18, (9), pp. 2340–2350.
    34. 34)
      • 29. Pedrycz, W., Vukovich, G.: ‘Feature analysis through information granulation and fuzzy sets’, Pattern Recognit., 2002, 35, (4), pp. 25–834.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0144
Loading

Related content

content/journals/10.1049/iet-its.2017.0144
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address