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A Learning-Based Multimodel Integrated Framework for Dynamic Traffic Flow Forecasting

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Abstract

Accurate and timely traffic flow forecasting is essential for many intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Over the past two decades, a variety of traffic flow forecasting models have been proposed. While each model has its merits and can achieve satisfactory forecasting results under certain traffic conditions, it is difficult for a single model to deal with various conditions well. In this paper, we proposed a novel deep learning-based multimodel integration framework in order to overcome the limitations of previous methods in dealing with large variations and uncertainties of traffic flow and hence improve the forecasting accuracy. Our framework can dynamically choose an optimal model or an optimal subset of models from a set of candidate models to forecast the future traffic flow conditions according to current input data. We employ stacked autoencoder (SAE), a simple yet efficient deep learning architecture, to extract the implicit relationships hidden in the traffic flow data and employed labeled data to fine tune the parameters of the architecture. Compared with the hand-crafted features and explicable dependence relations leveraged in previous models, the features learning from SAE are more representative and hence have more powerful forecasting capability. In addition, we propose a model-driven scheme to automatically label the training data and develop three strategies to integrate multiple models. Extensive experiments performed on three typical traffic flow datasets demonstrate the proposed framework outperforms state-of-the-art models and achieves much more accurate forecasting results under large and sudden variations.

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References

  1. Agarwal M, Maze TH, Souleyrette R (2005) Impacts of weather on urban freeway traffic flow characteristics and facility capacity. Transp Res Symp Mid-Cont 20(5):1121–1134

    Google Scholar 

  2. Ahmed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using Box–Jenkins techniques. 722, Transportation Research Record, Washington

  3. Ahmed SA, Cook AR (1982) Discrete dynamic models for freeway incident detection systems. Transp Plan Technol 7(4):231–242

    Article  Google Scholar 

  4. Akata Z, Perronnin F, Harchaoui Z, Schmid C (2014) Good practice in large-scale learning for image classification. IEEE Trans Pattern Anal Mach Intell 36(3):507–520

    Article  Google Scholar 

  5. Barimani N, Kian AR, Moshiri B (2014) Real time adaptive non-linear estimator/predictor design for traffic systems with inadequate detectors. Intell Transp Syst IET 8(3):308–321

    Article  Google Scholar 

  6. Basu S, Karki M, Ganguly S, DiBiano R, Mukhopadhyay S, Nemani R (2015) Learning sparse feature representations using probabilistic quadtrees and deep belief nets. In: European symposium on artificial neural networks, ESANN, pp 367–375

  7. Bhavathrathan B, Patil GR (2013) Analysis of worst case stochastic link capacity degradation to aid assessment of transportation network reliability. Procedia Soc Behav Sci 104(Supplement C):507–515

    Article  Google Scholar 

  8. Bohlin T (1976) Four cases of identification of changing systems. Math Sci Eng 126:441–518

    Article  Google Scholar 

  9. Boto-Giralda D, Díaz-Pernas FJ, González-Ortega D, Díez-Higuera JF, Antón-Rodríguez M, Martínez-Zarzuela M, Torre-Díez I (2010) Wavelet-based denoising for traffic volume time series forecasting with self-organizing neural networks. Comput Aided Civ Infrastruct Eng 25(7):530–545

    Article  Google Scholar 

  10. Chan KY, Dillon TS, Singh J, Chang E (2012) Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm. IEEE Trans Intell Transp Syst 13(2):644–654

    Article  Google Scholar 

  11. Comert G, Bezuglov A (2013) An online change-point-based model for traffic parameter prediction. IEEE Trans Intell Transp Syst 14(3):1360–1369

    Article  Google Scholar 

  12. Davarynejad M, Wang Y, Vrancken J, van den Berg J (2011) Multi-phase time series models for motorway flow forecasting. In: 2011 14th international IEEE conference on intelligent transportation systems (ITSC). IEEE, pp 2033–2038

  13. Davis GA, Nihan NL (1991) Nonparametric regression and short-term freeway traffic forecasting. J Transp Eng 117:178–188

    Article  Google Scholar 

  14. Dou Q, Chen H, Yu L, Zhao L, Qin J, Wang D, Mok VC, Shi L, Heng PA (2016) Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans Med Imaging 35(5):1182–1195

    Article  Google Scholar 

  15. Ghosh B, Basu B, O’Mahony M (2009) Multivariate short-term traffic flow forecasting using time-series analysis. IEEE Trans Intell Transp Syst 10(2):246–254

    Article  Google Scholar 

  16. Guo J, Huang W, Williams BM (2014) Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp Res Part C Emerg Technol 43:50–64

    Article  Google Scholar 

  17. Hamed MM, Al-Masaeid HR, Said ZMB (1995) Short-term prediction of traffic volume in urban arterials. J Transp Eng 121(3):249–254

    Article  Google Scholar 

  18. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  MATH  Google Scholar 

  19. Hong WC, Pai PF, Yang SL, Theng R (2006) Highway traffic forecasting by support vector regression model with tabu search algorithms. In: International joint conference on neural networks, 2006, IJCNN’06. IEEE, pp 1617–1621

  20. Hong WC, Dong Y, Zheng F, Lai CY (2011) Forecasting urban traffic flow by SVR with continuous ACO. Appl Math Model 35(3):1282–1291

    Article  MathSciNet  MATH  Google Scholar 

  21. Hu W, Yan L, Liu K, Wang H (2015) A short-term traffic flow forecasting method based on the hybrid PSO-SVR. Neural Process Lett 43:155–172

    Article  Google Scholar 

  22. Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15(5):2191–2201

    Article  Google Scholar 

  23. Jeong YS, Byon YJ, Mendonca Castro-Neto M, Easa SM (2013) Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 14(4):1700–1707

    Article  Google Scholar 

  24. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093

  25. Kumar K, Jain V (1999) Autoregressive integrated moving averages (ARIMA) modelling of a traffic noise time series. Appl Acoust 58(3):283–294

    Article  MathSciNet  Google Scholar 

  26. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  27. Levin M, Tsao YD (1980) On forecasting freeway occupancies and volumes. J Transp Res Rec 773:47–49

    Google Scholar 

  28. Lippi M, Bertini M, Frasconi P (2013) Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans Intell Transp Syst 14(2):871–882

    Article  Google Scholar 

  29. Liu DC, Nocedal J (1989) On the limited memory BFGS method for large scale optimization. Math Program 45(1–3):503–528

    Article  MathSciNet  MATH  Google Scholar 

  30. Lv Y, Duan Y, Kang W, Li Z, Wang FY (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873

    Google Scholar 

  31. Maria J, Amaro J, Falcao G, Alexandre LA (2016) Stacked autoencoders using low-power accelerated architectures for object recognition in autonomous systems. Neural Process Lett 43(2):445–458

    Article  Google Scholar 

  32. Mathew TV, Rao KVK (2007) Introduction to transportation engineering. National Programme on Technology Enhanced Learning, Powai

  33. Messer CJ (1993) Advanced freeway system ramp metering strategies for Texas. Technical report, Texas Transportation Institute, Texas

  34. Moayedi HZ, Masnadi-Shirazi M (2008) ARIMA model for network traffic prediction and anomaly detection. In: International symposium on information technology, 2008. ITSim 2008, vol 4. IEEE, pp 1–6

  35. Mori U, Mendiburu A, lvarez M, Lozano JA (2015) A review of travel time estimation and forecasting for advanced traveller information systems. Transportmetrica A Transp Sci 11(2):119–157. https://doi.org/10.1080/23249935.2014.932469

    Article  Google Scholar 

  36. Okutani I, Stephanedes YJ (1984) Dynamic prediction of traffic volume through Kalman filtering theory. Transp Res Part B Methodol 18(1):1–11

    Article  Google Scholar 

  37. Pan T, Sumalee A, Zhong RX, Indra-Payoong N (2013) Short-term traffic state prediction based on temporal-spatial correlation. IEEE Trans Intell Transp Syst 14(3):1242–1254

    Article  Google Scholar 

  38. Peng Y, Lei M, Li JB, Peng XY (2014) A novel hybridization of echo state networks and multiplicative seasonal ARIMA model for mobile communication traffic series forecasting. Neural Comput Appl 24(3–4):883–890

    Article  Google Scholar 

  39. Ross P (1982) Exponential filtering of traffic data. 869, Transportation Research Board, Washington

  40. Singh K, Li B (2012) Estimation of traffic densities for multilane roadways using a Markov model approach. IEEE Trans Ind Electron 59(11):4369–4376

    Article  Google Scholar 

  41. Smith BL, Demetsky MJ (1994) Short-term traffic flow prediction: neural network approach. J Transp Res Rec 1453:98–104

    Google Scholar 

  42. Smith BL, Demetsky MJ (1997) Traffic flow forecasting: comparison of modeling approaches. J Transp Eng 123(4):261–266

    Article  Google Scholar 

  43. Srivastava N (2013) Improving neural networks with dropout. PhD thesis, University of Toronto

  44. Stephanedes YJ, Michalopoulos PG, Plum RA (1981) Improved estimation of traffic flow for real-time control. J Transp Res Rec 795:28–39

    Google Scholar 

  45. Sumalee A, Zhong R, Pan T, Szeto W (2011) Stochastic cell transmission model (SCTM): a stochastic dynamic traffic model for traffic state surveillance and assignment. Transp Res Part B Methodol 45(3):507–533

    Article  Google Scholar 

  46. Szeto MW, Gazis DC (1972) Application of Kalman filtering to the surveillance and control of traffic systems. Transp Sci 6(4):419–439

    Article  Google Scholar 

  47. Tchrakian TT, Basu B, O’Mahony M (2012) Real-time traffic flow forecasting using spectral analysis. IEEE Trans Intell Transp Syst 13(2):519–526

    Article  Google Scholar 

  48. Tomczak JM (2015) Learning informative features from restricted Boltzmann machines. Neural Process Lett pp 1–16

  49. Tomczak JM, Gonczarek A (2016) Learning invariant features using subspace restricted Boltzmann machine. Neural Process Lett 45:173–182

    Article  Google Scholar 

  50. Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning, ACM, pp 1096–1103

  51. van Hinsbergen CP, Schreiter T, Zuurbier FS, Van Lint J, van Zuylen HJ (2012) Localized extended Kalman filter for scalable real-time traffic state estimation. IEEE Trans Intell Transp Syst 13(1):385–394

    Article  Google Scholar 

  52. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408

    MathSciNet  MATH  Google Scholar 

  53. Vlahogianni EI, Karlaftis MG, Golias JC (2005) Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp Res Part C Emerg Technol 13(3):211–234

    Article  Google Scholar 

  54. Wang Y, van Schuppen JH, Vrancken J (2014) Prediction of traffic flow at the boundary of a motorway network. IEEE Trans Intell Transp Syst 15(1):214–227

    Article  Google Scholar 

  55. Wang Y, Cheng JZ, Ni D, Lin M, Qin J, Luo X, Xu M, Xie X, Heng PA (2016) Towards personalized statistical deformable model and hybrid point matching for robust MR-TRUS registration. IEEE Trans Med Imaging 35(2):589–604

    Article  Google Scholar 

  56. Williams BM, Hoel LA (2003) Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J Transp Eng 129(6):664–672

    Article  Google Scholar 

  57. Williams BM, Durvasula PK, Brown DE (1998) Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transp Res Rec J Transp Res Board 1644(1):132–141

    Article  Google Scholar 

  58. Xie Y, Zhang Y, Ye Z (2007) Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition. Comput Aided Civ Infrastruct Eng 22(5):326–334

    Article  Google Scholar 

  59. Yuan Y, Van Lint J, Wilson RE, van Wageningen-Kessels F, Hoogendoorn SP (2012) Real-time Lagrangian traffic state estimator for freeways. IEEE Trans Intell Transp Syst 13(1):59–70

    Article  Google Scholar 

  60. Zhou T, Han G, Xu X, Lin Z, Han C, Huang Y, Qin J (2017) \(\delta \)-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting. Neurocomputing 247:31–38. https://doi.org/10.1016/j.neucom.2017.03.049

    Article  Google Scholar 

  61. Zhu JZ, Cao JX, Zhu Y (2014) Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections. Transp Res Part C Emerg Technol 47:139–154

    Article  Google Scholar 

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Acknowledgements

This work was supported partially by the National Natural Science Foundation of China (Nos. 61472145, 61772206, U1611461), in part by Special Fund of Science and Technology Research and Development on Application From Guangdong Province(SF-STRDA-GD No. 2016B010124011), in part by Guangdong High-level personnel of special support program (No. 2016TQ03X319) and the Guangdong Natural Science Foundation (No. 2017A030311027, No. 2016A030313047). The authors would like to thank Dr. Yubin Wang, from SIM Industries, Sassenheim, Netherlands, who provides the careful collected and preprocessed traffic flow data from the motorways of Amsterdam.

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Authors and Affiliations

  1. Department of Computer Science, College of Engineering, Shantou University, Shantou, China

    Teng Zhou

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Guoqiang Han & Xuemiao Xu

  3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong

    Chu Han

  4. College of Mathematics and Information, South China Agricultural University, Guangzhou, China

    Yuchang Huang

  5. Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Jing Qin

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  1. Teng Zhou
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  4. Chu Han
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Correspondence to Xuemiao Xu.

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Zhou, T., Han, G., Xu, X. et al. A Learning-Based Multimodel Integrated Framework for Dynamic Traffic Flow Forecasting. Neural Process Lett 49, 407–430 (2019). https://doi.org/10.1007/s11063-018-9804-x

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