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Method of speed data fusion based on Bayesian combination algorithm and high-order multi-variable Markov model

Method of speed data fusion based on Bayesian combination algorithm and high-order multi-variable Markov model

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The variety of data collecting and communication methods used in intelligent transportation systems such as sensors, cameras, and communication networks bring about huge volumes of data that are available for numerous transportation applications and related research on smart cities. However, it is still a challenge to integrate these heterogeneous data sources into a singular data schema in practice. Compared to a single data source, higher data accuracy can be obtained through integration of the multiple data sources if the data quality from each source has been known. In this study, a data fusion method based on Bayesian fusion rules is proposed to merge traffic speed from different data sources according to their prior probability that can be inferred from a high-order multivariable Markov model by considering the relations of multiple traffic factors in a systemic perspective. Case studies based on freeway data, such as loop data, INRIX data, and data from the National Performance Management and Research Data Set, are performed to validate the effectiveness of proposed speed fusion method.

References

    1. 1)
      • 6. Xu, C., Tarko, A.P., Wang, W., et al: ‘Predicting crash likelihood and severity on freeways with real-time loop detector data’, Accident Anal. Prev., 2013, 57, pp. 3039.
    2. 2)
      • 13. Gravina, R., Alinia, P., Ghasemzadeh, H., et al: ‘Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges’, Inf. Fusion, 2017, 35, pp. 6880.
    3. 3)
      • 1. Ma, D., Luo, X., Li, W., et al: ‘Traffic demand estimation for lane groups at signal-controlled intersections using travel times from video-imaging detectors’, IET Intell. Transp. Syst., 2017, 11, (4), pp. 222229.
    4. 4)
      • 7. 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. C, 2015, 54, pp. 187197.
    5. 5)
      • 8. Zhang, W., Tang, J., Kristian, H., et al: ‘Hybrid short-term prediction of traffic volume at ferry terminal based on data fusion’, IET Intell. Transp. Syst., 2016, 10, (8), pp. 524534.
    6. 6)
      • 22. Shamshad, A., Bawadi, M.A., Wan Hussin, W.M.A., et al: ‘First and second order Markov chain models for synthetic generation of wind speed time series’, Energy, 2005, 30, (2005), pp. 693708.
    7. 7)
      • 23. Thede, S.M., Harper, M.P.: ‘A second-order hidden Markov model for part-of-speech tagging’. ACL ‘99 Proc. 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, Maryland, USA, 1999, pp. 175182.
    8. 8)
      • 19. Petridis, V., Kehagias, A., Petrou, L., et al: ‘A Bayesian multiple models combination method for time series prediction’, J. Intell. Robotic Syst., 2001, 31, pp. 6989.
    9. 9)
      • 17. Zou, Y., Yang, H., Zhang, Y., et al: ‘Mixture modeling of freeway speed and headway data using multivariate skew-t distributions’, Transportmetrica A, 2017, 13, (7), pp. 657678.
    10. 10)
      • 16. INRIX, INRIX Traffic Data Services: ‘Tapping into real-time traffic flow’, accessed 14 August 2013.
    11. 11)
      • 11. Wu, C., Thai, J., Yadlowsky, S., et al: ‘Cellpath: fusion of cellular and traffic sensor data for route flow estimation via convex optimization’, Transp. Res. C, 2015, 59, (2015), pp. 111128.
    12. 12)
      • 21. Hofleitner, A., Herring, R., Bayen, A.: ‘Arterial travel time forecast with streaming data: a hybrid approach of flow modeling and machine learning’, Transp. Res. B., 2012, 46, pp. 10971122.
    13. 13)
      • 25. Gan, Q., Harris, C.J.: ‘Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion’, IEEE Trans. Aerosp. Electron. Syst., 2001, 37, (1), pp. 273280.
    14. 14)
      • 20. Zhang, W., Qi, Y., Kris, H., et al: ‘Vehicle traffic delay prediction in ferry terminal based on Bayesian multiple models combination method’, Transportmetrica A: Transp. Sci., 2017, 13, (5), pp. 467490.
    15. 15)
      • 18. Bruneau, P., Gelgon, M., Picarougne, F.: ‘A low-cost variational-Bayes technique for merging mixtures of probabilistic principal component analyzers’, Inf. Fusion, 2013, 14, pp. 268280.
    16. 16)
      • 9. Chang, T.H., Chen, A.Y., Hsu, Y.T., et al: ‘Freeway travel time prediction based on seamless spatio-temporal data fusion: case study of the freeway in Taiwan’, Transp. Res. Procedia, 2014, 17, pp. 452459.
    17. 17)
      • 14. Pourkhak, B., Mireei, S.A., Sadeghi, M., et al: ‘Multi-sensor data fusion in the nondestructive measurement of kiwifruit texture’, Measurement, 2017, 101, pp. 157165.
    18. 18)
      • 5. Ma, X., Luan, S., Du, B., et al: ‘Spatial copula model for imputing traffic flow data from remote microwave sensors’, Sensors, 2017, 17, (10), p. 2160.
    19. 19)
      • 15. Henrickson, K., Zou, Y., Wang, Y.: ‘Flexible and Robust Method for Missing Loop Detector Data Imputation’, Transp. Res. Rec., J. Transp. Res. Board, 2015, 2527, (2527), pp. 2936.
    20. 20)
      • 3. Arnaiz-Gonzalez, A., Diez-Pastor, J.F., Rodriguez, J.J., et al: ‘Instance selection of linear complexity for big data’, Knowl.-Based Syst., 2016, 107, pp. 8395.
    21. 21)
      • 24. Ma, X., Wu, Y. J., Wang, Y.: ‘DRIVE Net E-Science Transportation Platform for Data Sharing, Visualization, Modeling, and Analysis’, Transp. Res. Rec., J. Transp. Res. Board, 2011, 2215, (2215), pp. 3749.
    22. 22)
      • 10. Lee, H., Coifman, B.: ‘Identifying chronic splash over errors at freeway loop detectors’, Transp. Res. C, 2012, 24, pp. 141156.
    23. 23)
      • 2. Ma, X., Zhang, J., Ding, C., et al: ‘A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership’, Comput. Environ. Urban Syst., 2018, 70, pp. 113124, in press.
    24. 24)
      • 4. Ma, X., Dai, Z., He, Z., et al: ‘Learning traffic as images: a deep convolution neural network for large-scale transportation network speed prediction’, Sensors, 2017, 17, p. 818.
    25. 25)
      • 12. Chen, X., Wei, Z., Li, Z., et al: ‘Ensemble correlation-based low-rank matrix completion with applications to traffic data imputation’, Knowl.-Based Syst., 2017, 000, pp. 114.
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