http://iet.metastore.ingenta.com
1887

Wavelet-based short-term forecasting with improved threshold recognition for urban expressway traffic conditions

Wavelet-based short-term forecasting with improved threshold recognition for urban expressway traffic conditions

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.

Accurate traffic flow prediction can provide reliable and precise information for traffic departments to formulate effective management measures and assist drivers in performing more intelligent route planning and rerouting. The authors propose a short-term traffic flow forecasting framework for urban expressways based on data-driven mixed models including an approach to traffic flow threshold identification based on an improved semi-supervised K-means clustering algorithm, a hybrid multi-scale traffic speed forecasting method based on wavelet decomposition, and a traffic condition index corresponding to three-phase traffic flow theory for reflecting traffic status in real time. Model performance evaluation is performed using multi-source travel speed data. The results show that the traffic threshold recognition algorithm can correctly identify traffic speed thresholds confirming to the three-phase traffic flow transition and that the proposed short-term estimation technique outperforms traditional auto-regressive integrated moving average models, extended Kalman filtering methods, and artificial neural network models in terms of both accuracy and robustness. The proposed traffic condition index using adaptive thresholds and predicted speeds can provide real-time quantitative surveillance for urban expressway traffic.

References

    1. 1)
      • 1. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: ‘Short-term traffic forecasting: where we are and where we're going’, Transp. Res. C, Emerg. Technol., 2014, 43, pp. 319.
    2. 2)
      • 2. Chang, G.L., Park, S., Paracha, J.: ‘Intelligent transportation system field demonstration: integration of variable speed limit control and travel time estimation for a recurrently congested highway’, Transp. Res. Rec., J. Transp. Res. Board, 2011, 1, (2243), pp. 5566.
    3. 3)
      • 3. Han, C., Song, S., Wang, C.: ‘A real-time short-term traffic flow adaptive forecasting method based on ARIMA model’, Acta Simulata Systematica Sin., 2004, 7, p. 043.
    4. 4)
      • 4. Huang, H.Q., Tang, T.H.: ‘Short-term traffic flow forecasting based on ARIMA-ANN’. 2007 IEEE Int. Conf. Control and Automation (ICCA), 2007, pp. 23702373.
    5. 5)
      • 5. Zeng, D., Xu, J., Gu, J., et al: ‘Short term traffic flow prediction using hybrid ARIMA and ANN models’. The Workshop on Power Electronics and Intelligent Transportation System, 2008, pp. 621625.
    6. 6)
      • 6. Wong, K.I., Hsieh, Y.C.: ‘Short-term traffic flow forecasting for urban roads using space–time ARIMA’, Transp. Urban Sustainability, 2010, 10, pp. 583584.
    7. 7)
      • 7. Kamarianakis, Y., Prastacos, P.: ‘Space–time modeling of traffic flow’, Comput. Geosci., 2005, 31, (2), pp. 119133.
    8. 8)
      • 8. Diamantopoulos, T., Kehagias, D., König, F., et al: ‘Investigating the effect of global metrics in travel time forecasting’. Proc. 16th Int. IEEE Annual Conf. Intelligent Transportation Systems (ITSC 2013), Hague, Netherlands, October 2013, pp. 412417.
    9. 9)
      • 9. Wang, H., Liu, L., Dong, S., et al: ‘A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework’, Transportmetrica B, Transp. Dyn., 2016, 4, (3), pp. 159186.
    10. 10)
      • 10. Papageorgiou, M., Blosseville, J.M., Hadj-Salem, H.: ‘Modelling and real-time control of traffic flow on the southern part of Boulevard Périphérique in Paris: part I: modelling’, Transp. Res. A, Gen., 1990, 24, (5), pp. 345359.
    11. 11)
      • 11. Papageorgiou, M., Blosseville, J.M., Haj-Salem, H.: ‘Modelling and real-time control of traffic flow on the southern part of Boulevard Périphérique in Paris: part II: coordinated on-ramp metering’, Transp. Res. A, Gen., 1990, 24, (5), pp. 361370.
    12. 12)
      • 12. Wang, Y., Papageorgiou, M., Messmer, A.: ‘Motorway traffic state estimation based on extended Kalman filter’. European Control Conf. (ECC), 2003, pp. 19341939.
    13. 13)
      • 13. Wang, Y., Papageorgiou, M.: ‘Real-time freeway traffic state estimation based on extended Kalman filter: a general approach’, Transp. Res. B, Methodol., 2005, 39, (2), pp. 141167.
    14. 14)
      • 14. Wang, Y., Papageorgiou, M., Messmer, A.: ‘Real-time freeway traffic state estimation based on extended Kalman filter: a case study’, Transp. Sci., 2007, 41, (2), pp. 167181.
    15. 15)
      • 15. Wang, Y., Papageorgiou, M., Messmer, A.: ‘Real-time freeway traffic state estimation based on extended Kalman filter: adaptive capabilities and real data testing’, Transp. Res. A, Policy Pract., 2008, 42, (10), pp. 13401358.
    16. 16)
      • 16. Wang, Y., Papageorgiou, M., Messmer, A., et al: ‘An adaptive freeway traffic state estimator’, Automatica, 2009, 45, (1), pp. 1024.
    17. 17)
      • 17. Wang, Y., Coppola, P., Tzimitsi, A., et al: ‘Real-time freeway network traffic surveillance: large-scale field-testing results in southern Italy’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (2), pp. 548562.
    18. 18)
      • 18. Daganzo, C.: ‘The cell transmission model. Part I: a simple dynamic representation of highway traffic’, California Partners for Advanced Transit and Highways (PATH), 1992.
    19. 19)
      • 19. Daganzo, C.F.: ‘The cell transmission model, part II: network traffic’, Transp. Res. B, Methodol., 1995, 29, (2), pp. 7993.
    20. 20)
      • 20. Muñoz, L., Sun, X., Horowitz, R., et al: ‘Traffic density estimation with the cell transmission model’. American Control Conf., 2003, vol. 5, pp. 37503755.
    21. 21)
      • 21. Staňková, K., Schutter, B.: ‘On freeway traffic density estimation for a jump markov linear model based on Daganzo's cell transmission model’. 2010 13th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), 2010, pp. 1318.
    22. 22)
      • 22. Zhang, L., Ma, Y., Shi, L.: ‘A hybrid traffic flow model for real time freeway traffic simulation’, Korean Soc. Civ. Eng. J. Civ. Eng., 2014, 18, (4), pp. 11601164.
    23. 23)
      • 23. Sumalee, R.Z., Pan, T., Szeto, W.: ‘Stochastic cell transmission model (SCTM): a stochastic dynamic traffic model for traffic state surveillance and assignment’, Transp. Res. B, 2011, 3, (45), pp. 507533.
    24. 24)
      • 24. Zhong, R.X., Sumalee, A., Pan, T.L., et al: ‘Stochastic cell transmission model for traffic network with demand and supply uncertainties’, Transportmetrica A, Transp. Sci., 2013, 9, (7), pp. 567602.
    25. 25)
      • 25. Sumalee, A., Pan, T., Zhong, R., et al: ‘Dynamic stochastic journey time estimation and reliability analysis using stochastic cell transmission model: algorithm and case studies’, Transp. Res. C, Emerg. Technol., 2013, 35, pp. 263285.
    26. 26)
      • 26. Pan, T.L., Sumalee, A., Zhong, R.X., et al: ‘Short-term traffic state prediction based on temporal–spatial correlation’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (3), pp. 12421254.
    27. 27)
      • 27. Park, B.: ‘Hybrid neuro-fuzzy application in short-term freeway traffic volume forecasting’, Trans. Res. Rec. J. Trans. Res. Board, 2002, 1802, pp. 190196.
    28. 28)
      • 28. Quek, C., Pasquier, M., Lim, B.B.S.: ‘POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (2), pp. 133146.
    29. 29)
      • 29. Alarcon-Aquino, V., Barria, J.A.: ‘Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction’, IEEE Trans. Syst. Man Cybern. C, 2006, 36, (2), pp. 208220.
    30. 30)
      • 30. Chan, K.Y., Dillon, T.S., Singh, J., et al: ‘Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm’, IEEE Trans. Intell. Transp. Syst., 2015, 13, pp. 644654.
    31. 31)
      • 31. Fusco, G., Colombaroni, C., Comelli, L., et al: ‘Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models’. 2015 Int. Conf. Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015, pp. 93101.
    32. 32)
      • 32. Meng, M., Shao, C., Wong, Y., et al: ‘A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques’, J. Central South Univ., 2015, 22, pp. 779786.
    33. 33)
      • 33. Tang, J., Liu, F., Zhang, W., et al: ‘Exploring dynamic property of traffic flow time series in multi-states based on complex networks: phase space reconstruction versus visibility graph’, Phys. A, Stat. Mech. Appl., 2016, 450, pp. 635648.
    34. 34)
      • 34. Mei, H., Ma, A., Poslad, S., et al: ‘Short-term traffic volume prediction for sustainable transportation in an urban area’, J. Comput. Civ. Eng., 2013, 29, (2), p. 04014036.
    35. 35)
      • 35. Van, W.C., Van, H.C.: ‘Short-term traffic and travel time prediction models’, Artif. Intell. Appl. Crit. Transp. Issues, 2012, 22, pp. 2241.
    36. 36)
      • 36. Balke, K.N., Chaudhary, N.A., Chu, C., et al: ‘Dynamic traffic flow modeling for incident detection and short-term congestion prediction: year 1 progress report’, Incident Detect., 2005, 3, pp. 3639.
    37. 37)
      • 37. Kong, Q.J., Li, Z., Chen, Y., et al: ‘An approach to urban traffic state estimation by fusing multisource information’, IEEE Trans. Intell. Transp. Syst., 2009, 10, (3), pp. 499511.
    38. 38)
      • 38. Hinsbergen, C.P.I.V., Schreiter, T., Zuurbier, F.S., et al: ‘Fast traffic state estimation with the localized extended Kalman filter’. Int. IEEE Conf. Intelligent Transportation Systems, 2010, pp. 917922.
    39. 39)
      • 39. YiShui, S., Hongjiang, Z., Wei, C.: ‘Research of highway bottlenecks based on catastrophe theory’. 2015 Int. Conf. Transportation Information and Safety (ICTIS), 2015, pp. 138142.
    40. 40)
      • 40. Shi, W., Liu, Y.: ‘Real-time urban traffic monitoring with global positioning system-equipped vehicles’, IET Intell. Transp. Syst., 2010, 4, (2), pp. 113120.
    41. 41)
      • 41. Bi, S., Sun, D., Han, L., et al: ‘Research on method of feature extraction and recognition of road condition from nighttime video without vehicle segmentation’. 2012 IEEE Second Int. Conf. Cloud Computing and Intelligent Systems (CCIS), 2012, pp. 1:610.
    42. 42)
      • 42. Shibata, N., Terauchi, T., Kitani, T., et al: ‘A method for sharing traffic jam information using inter-vehicle communication’. Third Int. Conf. Mobile and Ubiquitous Systems: Networking & Services, 2007, pp. 17.
    43. 43)
      • 43. Thianniwet, T., Phosaard, S., Pattara-Atikom, W.: ‘Classification of road traffic congestion levels from vehicle's moving patterns: a comparison between artificial neural network and decision tree algorithm’, in Ao, S., Gelman, L. (Eds.): ‘Electronic engineering and computing technology’ (Springer, Netherlands, 2010), pp. 261271.
    44. 44)
      • 44. Guo, G., Cao, C., Xu, J.: ‘Traffic condition recognition based on probability neural network’, Comput. Eng. Appl., 2014, 45, (13), pp. 214216, 219.
    45. 45)
      • 45. Yang, Q., Wang, J., Song, X., et al: ‘Urban traffic congestion prediction using floating car trajectory data’. Int. Conf. Algorithms and Architectures for Parallel Processing, 2015, pp. 1830.
    46. 46)
      • 46. Huisken, G.: ‘Inter-urban short-term traffic congestion prediction’ (University of Twente, Enschede, 2006).
    47. 47)
      • 47. Kerner, B.S.: ‘Introduction to modern traffic flow, theory and control’ (Springer, Berlin, 2009).
    48. 48)
      • 48. Kerner, B.S.: ‘The physics of traffic’, New Sci., 2013, 197, (2638), p. 48.
    49. 49)
      • 49. Rehborn, H., Klenov, S.L., Palmer, J.: ‘An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany’, Phys. A, Stat. Mech. Appl., 2011, 390, (23–24), pp. 44664485.
    50. 50)
      • 50. Tian, J.F., Yuan, Z.Z., Treiber, M., et al: ‘Cellular automaton model within the fundamental-diagram approach reproducing some findings of the three-phase theory’, Phys. A, Stat. Mech. Appl., 2012, 391, (11), pp. 31293139.
    51. 51)
      • 51. Jia, B., Li, X.G., Chen, T., et al: ‘Cellular automaton model with time gap dependent randomisation under Kerner's three-phase traffic theory’, Transportmetrica, 2011, 7, (2), pp. 127140.
    52. 52)
      • 52. Schönhof, M., Helbing, D.: ‘Criticism of three-phase traffic theory’, Transp. Res. B, Methodol., 2009, 43, (7), pp. 784797.
    53. 53)
      • 53. Wang, X.: ‘Classification of three-phase traffic flow of urban expressway based on fuzzy C-means clustering’, J. Transp. Inf. Saf., 2009, 27, pp. 149152.
    54. 54)
      • 54. Kouhi, R., Shahbazi, F., Akbarzadeh, M.: ‘Three phase classification of an uninterrupted traffic flow: a k-means clustering study’, arXiv preprint arXiv:1610.06636, 2016.
    55. 55)
      • 55. Navin, F.P.D.: ‘Traffic congestion catastrophes’, Transp. Plan. Technol., 1986, 11, pp. 1925.
    56. 56)
      • 56. Qiu, H., Zhu, Y., Wu, X., et al: ‘Research of road traffic state classification threshold value of expressway and arterial roads in cities based on travel speed’. The 12th COTA Int. Conf. Transportation Professionals, 2012, pp. 493502.
    57. 57)
      • 57. Tang, J., Wang, Y., Liu, F.: ‘Characterizing traffic time series based on complex network theory’, Phys. A, Stat. Mech. Appl., 2013, 392, (18), pp. 41924201.
    58. 58)
      • 58. Li, F., Gong, J., Liang, Y., et al: ‘Real-time congestion prediction for urban arterials using adaptive data-driven methods’, Multimedia Tools Appl., 2016, 75, (24), pp. 120.
    59. 59)
      • 59. Highway Capacity Manual in Book Highway Capacity Manual Transportation research board (2010) Washington, DC.
    60. 60)
      • 60. Yildirimoglu, M., Geroliminis, N.: ‘Experienced travel time prediction for congested freeways’, Transp. Res. B, Method, 2013, 53, pp. 4563.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0236
Loading

Related content

content/journals/10.1049/iet-its.2017.0236
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address