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Deep traffic congestion prediction model based on road segment grouping

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Abstract

Intelligent Transportation System (ITS) is now being widely built all over the world. Traffic congestion prediction, as a major part of ITS, not only provides reliable traffic information for travelers to save their time, but also assists traffic management agencies to manage the traffic system. We find that the existing papers, including neural networks, do not perform well due to the complex association features between the road sections. In addition, the lack of a better evaluation standard for traffic congestion also makes the effect of traffic congestion prediction worse. In this paper, we propose a method to generate traffic congestion index by mining free-stream speed and free-stream flow. Considering the association features of road segments in the road network, we propose a road segment grouping method based on association subgraph to pre-train deep learning model and realize information sharing between road segments. Combining the features of traffic data and CNN model, we propose a traffic congestion prediction model named SG-CNN which training process is optimized by road segment grouping algorithm. The experiments demonstrate that our proposed model has higher accuracy rate than other models.

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References

  1. Nicholson H, Swann CD (1974) The prediction of traffic flow volumes based on spectral analysis. Transp Res 8(6):533–538

    Article  Google Scholar 

  2. EDES YJS, Michalopoulos PG, Plum RA (1980) Improved estimation of traffic flow for real-time control. Characteristics 7(9):28

    Google Scholar 

  3. Kumar SV, Vanajakshi L (2015) Short-term traffic flow prediction using seasonal arima model with limited input data. Eur Transp Res Rev 7(3):21

    Article  Google Scholar 

  4. Liu J, Guan W (2004) A summary of traffic flow forecasting methods [j]. J Highway Transp Res Dev 3:82–85

    Google Scholar 

  5. Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley

  6. Ahmed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using box-jenkins techniques, vol 722. Elsevier

  7. Tchrakian TT, Basu B, O’Mahony M (2011) Real-time traffic flow forecasting using spectral analysis. IEEE Trans Intell Transp Syst 13(2):519–526

    Article  Google Scholar 

  8. 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 1644(1):132–141

    Article  Google Scholar 

  9. Surya S, Rakesh N (2016) Flow based traffic congestion prediction and intelligent signalling using markov decision process. In: 2016 International Conference on Inventive Computation Technologies (ICICT), vol 3. IEEE, pp 1–6

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

    Article  Google Scholar 

  11. Vythoulkas P (1993) Alternative approaches to short term traffic forecasting for use in driver information systems. Transp Traffic Theory 12:485–506

    Google Scholar 

  12. Emami A, Sarvi M, Bagloee SA (2019) Using kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment. J Modern Transp 27(3):222–232

    Article  Google Scholar 

  13. Yang F, Yin Z, Liu H, Ran B (2004) Online recursive algorithm for short-term traffic prediction. Transp Res Rec 1879(1):1–8

    Article  Google Scholar 

  14. Davis GA, Nihan NL (1991) Nonparametric regression and short-term freeway traffic forecasting. J Transp Eng 117(2):178–188

    Article  Google Scholar 

  15. Meng M, Shao C-f, Wong Y-d, Wang B-b, Li H-x (2015) A two-stage short-term traffic flow prediction method based on avl and aknn techniques. J Cent South Univ 22(2):779–786

    Article  Google Scholar 

  16. Xu QH, Yang R (2005) Traffic flow prediction using support vector machine based method. J Highway Transp Res Dev 22(12):131–134

    Google Scholar 

  17. Castro-Neto M, Jeong Y-S, Jeong M-K, Han LD (2009) Online-svr for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl 36(3):6164–6173

    Article  Google Scholar 

  18. Ahn J, Ko E, Kim EY (2016) Highway traffic flow prediction using support vector regression and bayesian classifier. In: 2016 International Conference on Big Data and Smart Computing (BigComp). IEEE, pp 239–244

  19. Cheng A, Jiang X, Li Y, Zhang C, Zhu H (2017) Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Physica A: Stat Mech Appl 466:422–434

    Article  Google Scholar 

  20. Liu Z, Du W, Yan D-, Chai G, Guo J- (2018) Short-term traffic flow forecasting based on combination of k-nearest neighbor and support vector regression. J Highway Transp Res Dev (Engl Edn) 12(1):89–96

    Article  Google Scholar 

  21. Adewumi A, Kagamba J, Alochukwu A (2016) Application of chaos theory in the prediction of motorised traffic flows on urban networks. Math Probl Eng 2016

  22. Tan H, Wu Y, Shen B, Jin PJ, Ran B (2016) Short-term traffic prediction based on dynamic tensor completion. IEEE Trans Intell Transp Syst 17(8):2123–2133

    Article  Google Scholar 

  23. Li F, Gong J, Liang Y, Zhou J (2016) Real-time congestion prediction for urban arterials using adaptive data-driven methods. Multimed Tools Appl 75(24):17573–17592

    Article  Google Scholar 

  24. Pattanaik V, Singh M, Gupta PK, Singh SK (2016) Smart real-time traffic congestion estimation and clustering technique for urban vehicular roads. In: 2016 IEEE region 10 conference (TENCON). IEEE, pp 3420–3423

  25. Jabbarpour MR, Malakooti H, Noor RM, Anuar NB, Khamis N (2014) Ant colony optimisation for vehicle traffic systems: applications and challenges. Int J Bio-Inspired Comput 6(1):32–56

    Article  Google Scholar 

  26. Ando Y, Masutani O, Sasaki H, Iwasaki H, Fukazawa Y, Honiden S (2005) Pheromone model: Application to traffic congestion prediction. In: International workshop on engineering self-organising applications. Springer, pp 182–196

  27. Xiao J, Xiao Z, Wang D, Bai J, Havyarimana V, Zeng F (2019) Short-term traffic volume prediction by ensemble learning in concept drifting environments. Knowl-Based Syst 164:213–225

    Article  Google Scholar 

  28. Smith BL, Demetsky MJ (1994) Short-term traffic flow prediction: neural network approach. Transp Res Rec 6(1453)

  29. Pattara-Atikom W, Peachavanish R (2007) Estimating road traffic congestion from cell dwell time using neural network. In: 2007 7th international conference on its telecommunications. IEEE, pp 1–6

  30. Jin F, Sun S (2008) Neural network multitask learning for traffic flow forecasting. In: 2008 IEEE international joint conference on neural networks (ieee world congress on computational intelligence). IEEE, pp 1897–1901

  31. Chan K Y, Dillon T, Chang E, Singh J (2012) Prediction of short-term traffic variables using intelligent swarm-based neural networks. IEEE Trans Control Syst Technol 21(1):263–274

    Article  Google Scholar 

  32. Chan KY, Khadem S, Dillon TS, Palade V, Singh J, Chang E (2011) Selection of significant on-road sensor data for short-term traffic flow forecasting using the taguchi method. IEEE Trans Ind Inf 8(2):255–266

    Article  Google Scholar 

  33. Elleuch W, Wali A, Alimi AM (2016) Intelligent traffic congestion prediction system based on ann and decision tree using big gps traces. In: International conference on intelligent systems design and applications. Springer, pp 478–487

  34. Kumar K, Parida M, Katiyar VK (2013) Short term traffic flow prediction for a non urban highway using artificial neural network. Procedia-Soc Behav Sci 104:755–764

    Article  Google Scholar 

  35. 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 

  36. Shen Q, Ban X, Guo C, Wang C (2016) Kernel based semi-supervised extreme learning machine and the application in traffic congestion evaluation. In: Proceedings of ELM-2015, vol 1. Springer, pp 227–236

  37. Wang J, Gu Q, Wu J, Liu G, Xiong Z (2016) Traffic speed prediction and congestion source exploration: A deep learning method. In: 2016 IEEE 16th international conference on data mining (ICDM). IEEE, pp 499–508

  38. Lai G, Chang W-C, Yang Y, Liu H (2018) Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st international ACM SIGIR conference on research & development in information retrieval. ACM, pp 95–104

  39. Zhao L, Zhou Y, Lu H, Fujita H (2019) Parallel computing method of deep belief networks and its application to traffic flow prediction. Knowl-Based Syst 163:972–987

    Article  Google Scholar 

  40. Li L, Qin L, Qu X, Zhang J, Wang Y, Ran B (2019) Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm. Knowl-Based Syst 172:1–14

    Article  Google Scholar 

  41. Yang H-F, Dillon TS, Chen Y-PP (2016) Optimized structure of the traffic flow forecasting model with a deep learning approach. IEEE Trans Neural Netw Learn Syst 28(10):2371–2381

    Article  Google Scholar 

  42. Shao H, Soong B-H (2016) Traffic flow prediction with long short-term memory networks (lstms). In: 2016 IEEE region 10 conference (TENCON). IEEE, pp 2986–2989

  43. Luo X, Li D, Yang Y, Zhang S (2019) Spatiotemporal traffic flow prediction with knn and lstm. J Adv Transp

  44. Zhao F, Zeng G-Q, Lu K-D (2019) Enlstm-wpeo: Short-term traffic flow prediction by ensemble lstm, nnct weight integration and population extremal optimization. IEEE Trans Veh Technol

  45. Osipov V, Nikiforov V, Zhukova N, Miloserdov D (2020) Urban traffic flows forecasting by recurrent neural networks with spiral structures of layers. Neural Comput Appl:1–13

  46. He P, Jiang G, Lam S-K, Sun Y (2020) Learning heterogeneous traffic patterns for travel time prediction of bus journeys. Inf Sci 512:1394–1406

    Article  Google Scholar 

  47. Guo S, Lin Y, Li S, Chen Z, Wan H (2019) Deep spatial-temporal 3d convolutional neural networks for traffic data forecasting. IEEE Trans Intell Transp Syst

  48. Zhu J, Huang C, Yang M, Fung GPC (2019) Context-based prediction for road traffic state using trajectory pattern mining and recurrent convolutional neural networks. Inf Sci 473:190– 201

    Article  Google Scholar 

  49. Lin Y, Dai X, Li L, Wang FY (2019) Pattern sensitive prediction of traffic flow based on generative adversarial framework. IEEE Trans Intell Transp Syst 20(6):2395–2400

    Article  Google Scholar 

  50. Peng H, Wang H, Du B, Bhuiyan MZA, Ma H, Liu J, Wang L, Yang Z, Du L, Wang S et al (2020) Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Inf Sci 521:277–290

    Article  Google Scholar 

  51. Gill DNJA (1998) Comparing measures of sample skewness and kurtosis. J R Stat Soc 47(1):183–189

    Article  Google Scholar 

  52. Urzúa CM (1996) On the correct use of omnibus tests for normality. Econ Lett 53(3):247–251

    Article MathSciNet  Google Scholar 

  53. Tan H, Xuan X, Wu Y, Zhong Z, Ran B (2016) A comparison of traffic flow prediction methods based on dbn. In: CICTP 2016. Springer, pp 273–283

  54. Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873

    Google Scholar 

  55. Asif MT, Dauwels J, Goh CY, Oran A, Fathi E, Xu M, Dhanya MM, Mitrovic N, Jaillet P (2013) Spatiotemporal patterns in large-scale traffic speed prediction. IEEE Trans Intell Transp Syst 15(2):794–804

    Article  Google Scholar 

  56. Li Y, Yu R, Shahabi C, Liu Y (2017) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv:1707.01926

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Yue Tu, Shukuan Lin, Jianzhong Qiao & Bin Liu

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  1. Yue Tu

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  2. Shukuan Lin

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  3. Jianzhong Qiao

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  4. Bin Liu

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Correspondence toShukuan Lin.

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Tu, Y., Lin, S., Qiao, J.et al. Deep traffic congestion prediction model based on road segment grouping.Appl Intell51, 8519–8541 (2021). https://doi.org/10.1007/s10489-020-02152-x

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