KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Hybrid CSA optimization with seasonal RVR in traffic flow forecasting

  • Shen, Zhangguo (College of Computer Science and Technology, Zhejiang University of Technology) ;
  • Wang, Wanliang (College of Computer Science and Technology, Zhejiang University of Technology) ;
  • Shen, Qing (School of Information Engineering, Huzhou University) ;
  • Li, Zechao (School of Computer Science and Engineering, Nanjing University of Science and Technology)
  • Received : 2016.09.17
  • Accepted : 2017.06.27
  • Published : 2017.10.31
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Abstract

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

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References

  1. Chan, Kit Yan et al., "Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg-Marquardt Algorithm," IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 2, pp. 644-654, June 2012. https://doi.org/10.1109/TITS.2011.2174051
  2. Jeong, Young-Seon et al., "Supervised weighting-online learning algorithm for short-term traffic flow prediction," IEEE Transactions on Intelligent Transportation Systems, vol.14, no.4, pp.1700-1707, December 2013. https://doi.org/10.1109/TITS.2013.2267735
  3. Li, Ming-Wei, Duan-Feng Han and Wen-long Wang, "Vessel traffic flow forecasting by RSVR with chaotic cloud simulated annealing genetic algorithm and KPCA," Neurocomputing, vol.157, pp.243-255, June 2015. https://doi.org/10.1016/j.neucom.2015.01.010
  4. Thomas, Tom, W. Weijermars, and E. Van Berkum, "Predictions of urban volumes in single time series," IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 1, pp.71-80, March 2010. https://doi.org/10.1109/TITS.2009.2028149
  5. Zheng, Zuduo and D. Su, "Short-term traffic volume forecasting: A k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm," Transportation Research Part C Emerging Technologies, vol. 43, pp. 143-157, June 2014. https://doi.org/10.1016/j.trc.2014.02.009
  6. Hong, Richang et al., "Cross-Domain Multi-View Object Retrieval via Multi-Scale Topic Models," IEEE Transactions on Image Processing, pp. 99, September 2016.
  7. http://www.google.com/patents/US20110128127.
  8. Asensio, Javier et al., "Studying Individualized Transit Indicators Using a New Low-Cost Information System," Handbook of Research on Embedded System Design, pp. 388-407, January 2014.
  9. Fernandezares, Antonio, et al., "Comparing Wireless Traffic Tracking with Regular Traffic Control Systems for the Detection of Congestions in Streets," ACM Symposium on Applied Computing, pp. 552-553, June 2016.
  10. Tipping, Michael E, "Bayesian inference: An introduction to principles and practice in machine learning," Advanced lectures on machine Learning. Springer Berlin Heidelberg, pp. 41-62, 2004.
  11. Mingjun, Ji and Tang Huanwen, "Application of chaos in simulated annealing," Chaos, Solitons & Fractals, vol.21, no.4, pp.933-941, August 2004. https://doi.org/10.1016/j.chaos.2003.12.032
  12. Khader, Abdelhaleem I. and Mac McKee, "Use of a relevance vector machine for groundwater quality monitoring network design under uncertainty," Environmental Modelling & Software, vol. 57, pp. 115-126, July 2014. https://doi.org/10.1016/j.envsoft.2014.02.015
  13. Li, Hong, Donghui Pan and CL Philip Chen, "Intelligent prognostics for battery health monitoring using the mean entropy and relevance vector machine," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, no. 7, pp. 851-862, July 2014. https://doi.org/10.1109/TSMC.2013.2296276
  14. Juang, Chia Feng, "A hybrid of genetic algorithm and particle swarm optimization for recurrent network design," IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, vol. 34, no. 2, pp. 997, March 2004. https://doi.org/10.1109/TSMCB.2003.818557
  15. http://www.dumn.edu/tdrl/traffic/
  16. Williams, Billy, "Multivariate vehicular traffic flow prediction: evaluation of ARIMAX modeling," Transportation Research Record: Journal of the Transportation Research Board, vol. 1776, pp. 194-200, 2001. https://doi.org/10.3141/1776-25
  17. Hong, Wei-Chiang, "Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm," Neurocomputing, vol. 74, no. 12-13, pp. 2096-2107, June 2011. https://doi.org/10.1016/j.neucom.2010.12.032
  18. Lippi, Marco, Matteo Bertini and Paolo Frasconi, "Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning," IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 871-882, March 2013. https://doi.org/10.1109/TITS.2013.2247040
  19. Ghosh, B, B. Basu, and M. O'Mahony, "Multivariate Short-Term Traffic Flow Forecasting Using Time-Series Analysis," Intelligent Transportation Systems IEEE Transactions on, vol. 10, no. 2, pp. 246-254, July 2009. https://doi.org/10.1109/TITS.2009.2021448
  20. Stathopoulos, Anthony and M. G. Karlaftis, "A multivariate state space approach for urban traffic flow modeling and prediction," Transportation Research Part C Emerging Technologies, vol. 11, no. 2, pp. 121-135, April 2003. https://doi.org/10.1016/S0968-090X(03)00004-4
  21. Kamarianakis, Yiannis, and P. Prastacos, "Space-time modeling of traffic flow," Computers & Geosciences, vol. 31, no. 2, pp. 119-133, March 2005. https://doi.org/10.1016/j.cageo.2004.05.012
  22. Clark, Stephen, "Traffic Prediction Using Multivariate Nonparametric Regression," Journal of Transportation Engineering, vol. 129, no. 2, pp. 161-168, March 2003. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:2(161)
  23. Okutani, Iwao and Y. J, Stephanedes, "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B Methodological, vol. 18, no. 1, pp. 1-11, February 1984.
  24. Sun, Hongyu, and H. X. Liu, "Short-Term Traffic Forecasting Using the Local Linear Regression Model," Center for Traffic Simulation Studies, 2002.
  25. Cai, Pinlong et al., "A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting," Transportation Research Part C Emerging Technologies, vol. 62, pp. 21-34, January 2016. https://doi.org/10.1016/j.trc.2015.11.002
  26. Xia, Dawen, et al, "A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting," Neurocomputing, vol. 179, pp. 246-263, February 2016. https://doi.org/10.1016/j.neucom.2015.12.013
  27. Chen, Cheng et al., "Distributed Modeling in a MapReduce Framework for Data-Driven Traffic Flow Forecasting," IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 22-33, March 2013. https://doi.org/10.1109/TITS.2012.2205144
  28. Daraghmi, Yousef Awwad, C. W. Yi and T. C. Chiang, "Negative Binomial Additive Models for Short-Term Traffic Flow Forecasting in Urban Areas," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 2, pp. 784-793, April 2014. https://doi.org/10.1109/TITS.2013.2287512
  29. Tchrakian, T. T., B. Basu and M. O'Mahony, "Real-Time Traffic Flow Forecasting Using Spectral Analysis," IEEE Transactions on Intelligent Transportation Systems, vol.13, no.2, pp.519-526, June 2012. https://doi.org/10.1109/TITS.2011.2174634
  30. Sun, Shiliang and C. Zhang, "The Selective Random Subspace Predictor for Traffic Flow Forecasting," IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 2, pp. 367-373, June 2007. https://doi.org/10.1109/TITS.2006.888603
  31. Oh, Se Do, Y. J. Kim, and J. S. Hong, "Urban Traffic Flow Prediction System Using a Multifactor Pattern Recognition Model," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 1-12, October 2015. https://doi.org/10.1109/TITS.2015.2393752
  32. Sun, Shiliang and X. Xu, "Variational Inference for Infinite Mixtures of Gaussian Processes With Applications to Traffic Flow Prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 466-475, June 2011. https://doi.org/10.1109/TITS.2010.2093575
  33. Simroth, Axel and H. Zahle, "Travel Time Prediction Using Floating Car Data Applied to Logistics Planning," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 1, pp. 243-253, January 2011. https://doi.org/10.1109/TITS.2010.2090521
  34. Vlahogianni, Eleni I., M. G. Karlaftis and J. C. Golias, "Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach." Transportation Research Part C Emerging Technologies, vol. 13, no. 3, pp. 211-234, June 2005. https://doi.org/10.1016/j.trc.2005.04.007
  35. Yin, Hongbin et al., "Urban traffic flow prediction using a fuzzy-neural approach," Transportation Research Part C Emerging Technologies, vol. 10, no. 2, pp. 85-98, April 2002. https://doi.org/10.1016/S0968-090X(01)00004-3
  36. Li, Chungui, S. Xu and X. Wen, "Traffic Flow Forecasting Algorithm Using Simulated Annealing Genetic BP Network," in Proc. of 2010 International Conference on Measuring Technology and Mechatronics Automation, vol. 3, pp. 1043-1046, March 2010.
  37. Chan, Kit Yan, et al, "Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg-Marquardt Algorithm," IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 2, pp. 644-654, June 2012. https://doi.org/10.1109/TITS.2011.2174051
  38. Yang, Wen et al., "Traffic flow prediction based on wavelet transform and Radial Basis Function network," in Proc. of Logistics Systems and Intelligent Management, 2010 International Conference on IEEE, vol. 2, pp. 969-972, January 2010.
  39. Chan, Kit Yan, T. S. Dillon and E. Chang, "An Intelligent Particle Swarm Optimization for Short-Term Traffic Flow Forecasting Using on-Road Sensor Systems," IEEE Transactions on Industrial Electronics, vol. 60, no. 10, pp. 4714-4725, August 2012. https://doi.org/10.1109/TIE.2012.2213556
  40. Tipping, Michael E, "Sparse bayesian learning and the relevance vector machine." Journal of Machine Learning Research, vol. 1, no. 3, pp. 211-244, September 2001.
  41. Yu, Shiwei, Ke Wang and Yi-Ming Wei, "A hybrid self-adaptive Particle Swarm Optimization-Genetic Algorithm-Radial Basis Function model for annual electricity demand prediction," Energy Conversion and Management, vol. 91, pp.176-185, February 2015. https://doi.org/10.1016/j.enconman.2014.11.059
  42. Jeong, Young Seon, et al, "Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction," IEEE Transactions on Intelligent Transportation Systems, vol.14, no. 4, pp. 1700-1707, December 2013. https://doi.org/10.1109/TITS.2013.2267735
  43. Metropolis, Nicholas et al., "Equation of state calculations by fast computing machines," The journal of chemical physics, vol. 21, no. 6, pp. 1087-1092, 1953. https://doi.org/10.1063/1.1699114