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Journal of Intelligent Transportation Systems
Technology, Planning, and Operations
Volume 25, 2021 - Issue 5
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Innovations for Smart and Connected Traffic. Guest Editor. Professor Zhibin Li, Southeast University, China

Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory

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Pages 439-454 | Received 22 Jul 2019, Accepted 07 Jan 2020, Published online: 27 Jan 2020

References

  • Ahmed, M. S., & Cook, A. R. (1979). Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Transportation Research Record: Journal of the Transportation Research Board, 722, 1–9.
  • Al-Deek, H. M., Venkata, C., & Chandra, S. R. (2004). New algorithms for filtering and imputation of real-time and archived dual-loop detector data in I-4 data warehouse. Transportation Research Record: Journal of the Transportation Research Board, 1867(1), 116–126. doi:10.3141/1867-14
  • Allison, P. D. (2001). Missing data. Thousand Oaks, CA: Sage Publications.
  • Amiri, M., & Jensen, R. (2016). Missing data imputation using fuzzy-rough methods. Neurocomputing, 205, 152–164. doi:10.1016/j.neucom.2016.04.015
  • Baharaeen, S., & Masud, A. S. (1986). A computer program for time series forecasting using single and double exponential smoothing techniques. Computers & Industrial Engineering, 11(1), 151–155. doi:10.1016/0360-8352(86)90068-9
  • Boyles, S. (2011, January). A comparison of interpolation methods for missing traffic volume data. Proceedings of the 90th Annual Meeting of the Transportation Research Board, Washington, DC, pp. 23–27.
  • Chen, J., & Shao, J. (2000). Nearest neighbor imputation for survey data. Journal of Official Statistics, 16(2), 113.
  • Chen, X., Wang, S., Shi, C., Wu, H., Zhao, J., & Fu, J. (2019). Robust Ship Tracking via Multi-view Learning and Sparse Representation. Journal of Navigation, 72(1), 176–192. doi:10.1017/S0373463318000504
  • Clark, S. D., Watson, S., & Redfern, E. (1993). Application of outlier detection and missing value estimation techniques to various forms of traffic count data. Leeds, UK: Institute of Transport Studies, University of Leeds.
  • De Boor, C. (1978). A practical guide to splines. New York: Springer-Verlag.
  • Duan, G., Liu, P., Chen, P., Jiang, Q., & Li, N. (2011). Short-term traffic flow prediction based on rough set and support vector machine. Eighth International Conference on Fuzzy Systems & Knowledge Discovery.
  • Fan, A. (2013). The traffic prediction and control based on rough set theory. Advanced Materials Research, 756–759, 632–635. doi:10.4028/www.scientific.net/AMR.756-759.632
  • Gao, H., & Fasheng, L. (2009). Combination prediction model of traffic flow based on rough set theory. International Conference on Information Technology & Computer Science.
  • Ghosh, B., Basu, B., & O’Mahony, M. (2005, January). Time-series modeling for forecasting vehicular traffic flow in Dublin. Proceedings of the 84th Annual Meeting of Transportation Research Board, Washington, DC.
  • Gold, D. L., Turner, S. M., Gajewski, B. J., & Spiegelman, C. (2000, January). Imputing missing values in its data archives for intervals under 5 minutes. Proceedings of the 80th Transportation Research Board Meeting, Washington, DC.
  • Hofleitner, A., Herring, R., & Bayen, A. (2012). Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning. Transportation Research Part B: Methodological, 46(9), 1097–1122. doi:10.1016/j.trb.2012.03.006
  • Holt, C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10. doi:10.1016/j.ijforecast.2003.09.015
  • Jensen, R., & Cornelis, C. (2011). Fuzzy-rough nearest neighbour classification and prediction. Theoretical Computer Science, 412(42), 5871–5884. doi:10.1016/j.tcs.2011.05.040
  • Kumar, B. A., Vanajakshi, L., & Subramanian, S. C. (2018). A hybrid model based method for bus travel time estimation. Journal of Intelligent Transportation Systems, 22(5), 390–406. doi:10.1080/15472450.2017.1378102
  • Li, Z., Li, L., & Li, Y. (2013). Efficient missing data imputing for traffic flow by considering temporal and spatial dependence. Transportation Research Part C: Emerging Technologies, 34, 108–120.
  • Nguyen, L. H., & Scherer, W. T. (2003). Imputation techniques to account for missing data in support of intelligent transportation systems applications. Technical Report UVACTS-13-0-78, University of Virginia, Center for Transportation Studies.
  • Ni, D., Leonard, J. D., Guin, A., & Feng, C. (2005). Multiple imputation scheme for overcoming the missing values and variability issues in ITS data. Journal of Transportation Engineering, 131(12), 931–938. doi:10.1061/(ASCE)0733-947X(2005)131:12(931)
  • Nowicki, R. (2009). Rough neuro-fuzzy structures for classification with missing data. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)), 39(6), 1334–1347. doi:10.1109/TSMCB.2009.2012504
  • Nowicki, R. (2010). On classification with missing data using rough-neuro-fuzzy systems. International Journal of Applied Mathematics and Computer Science, 20(1), 55–67. doi:10.2478/v10006-010-0004-8
  • Qu, L., Hu, J., Li, L., & Zhang, Y. (2009). PPCA-based missing data imputation for traffic flow volume: A systematical approach. IEEE Transactions on Intelligent Transportation Systems, 10(3), 512–522.
  • Qu, L., Zhang, Y., Hu, J., Jia, L., & Li, L. (2008). A BPCA based missing value imputing method for traffic flow volume data. Paper presented at the 985-990.
  • Ramsey, B., & Hayden, G. (1994). AutoCounts: A way to analyse automatic traffic count data. Traffic Engineering & Control, 35 (4), 245.
  • Ran, B., Tan, H., Feng, J., Wang, W., Cheng, Y., & Jin, P. (2016). Estimating missing traffic volume using low multilinear rank tensor completion. Journal of Intelligent Transportation Systems, 20(2), 152–161. doi:10.1080/15472450.2015.1015721
  • Redfern, E. J., Waston, S. M., Tight, M. R., & Clark, S. D. (1993). A comparative assessment of current and new techniques for detecting outliers and estimating missing values in transport related time series data. Proceedings of Highways and Planning Summer Annual Meeting, Institute of Science and Technology, University of Manchester, England.
  • Tan, H., Feng, J., Feng, G., Wang, W., Zhang, Y., & Li, F. (2013). A tensor-based method for missing traffic data completion. Transportation Research Part C: Emerging Technologies, 28, 15–27. doi:10.1016/j.trc.2012.12.007
  • Tang, J., Chen, X., Hu, Z., Zong, F., Han, C., & Li, L. (2019). Traffic flow prediction based on combination of support vector machine and data denoising schemes. Physica A: Statistical Mechanics and Its Applications, 534, 120642, 1–19. doi:10.1016/j.physa.2019.03.007
  • Tang, J., Liu, F., Zou, Y., Zhang, W., & Wang, Y. (2017). An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2340–2350. doi:10.1109/TITS.2016.2643005
  • Tang, J., Zhang, G., Wang, Y., Wang, H., & Liu, F. (2015). A hybrid approach to integrate fuzzy c-means based imputation method with genetic algorithm for missing traffic volume data estimation. Transportation Research Part C: Emerging Technologies, 51, 29–40. doi:10.1016/j.trc.2014.11.003
  • Tight, M. R., Redfern, E. J., & Watson, S. M. (1993). Outlier detection and missing value estimation in time series traffic count data. Leeds, UK: Institute of Transport Studies, University of Leeds.
  • Wilby, M. R., Díaz, J. J. V., Rodríguez Gonz´Lez, A. B., & Sotelo, M. Á. (2014). Lightweight occupancy estimation on freeways using extended floating car data. Journal of Intelligent Transportation Systems, 18(2), 149–163. doi:10.1080/15472450.2013.801711
  • Williams, B. M., & Hoel, L. A. (2003). Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering, 129(6), 664–672. doi:10.1061/(ASCE)0733-947X(2003)129:6(664)
  • Xie, Y., & Huynh, N. (2010). Kernel-based machine learning models for predicting daily truck volume at seaport terminals. Journal of Transportation Engineering, 136(12), 1145–1152.
  • Yan, Y., Zhang, S., Tang, J., & Wang, X. (2017). Understanding characteristics in multivariate traffic flow time series from complex network structure. Physica A: Statistical Mechanics and Its Applications, 477, 149–160. doi:10.1016/j.physa.2017.02.040
  • Zhang, W., Yu, Y., Qi, Y., Shu, F., & Wang, Y. (2019). Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transportmetrica A: Transport Science, 15(2), 1688–1711. doi:10.1080/23249935.2019.1637966
  • Zhang, W., Zou, Y., Tang, J., Ash, J., & Wang, Y. (2016). Short-term prediction of vehicle waiting queue at ferry terminal based on machine learning method. Journal of Marine Science and Technology, 21(4), 729–741. doi:10.1007/s00773-016-0385-y
  • Zhong, M., Lingras, P., & Sharma, S. (2004). Estimation of missing traffic counts using factor, genetic, neural, and regression techniques. Transportation Research Part C, 12(2), 139–166. doi:10.1016/j.trc.2004.07.006

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