Skip to main content
SpringerLink
Log in

QPSO-AHES-RC: a hybrid learning model for short-term traffic flow prediction

  • Mathematical methods in data science
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Accurate assessment of road conditions can effectively alleviate traffic congestion and guide people’s travel plans, traffic control decisions of transportation departments, and formulation of traffic-related laws and regulations. This paper proposes a quantum particle swarm optimization (QPSO) and adaptive hybrid exponential smoothing with residual correction (AHES-RC) for the nonlinearity and randomness of traffic flow. In the proposed algorithm, the single–double-exponential smoothing method is mixed with adaptive weights to form AHES, a new method of mixing weights according to real-time traffic trend changes. After the residual correction of AHES is calculated by the extreme learning machine algorithm, the parameters of AHES-RC are optimized using QPSO to improve the prediction accuracy further. This paper comprehensively compares the QPSO-AHES-RC algorithm with other benchmark models through testing on 26 real-world data sets. The results show that the proposed algorithm can adaptive forecasting under various traffic conditions, yielding the best forecasting performance in terms of various forecast error metrics. Compared with advanced machine learning algorithms such as XGBoost and CatBoost, the mean RMSE and mean MAPE have been improved by over 20% on average. In addition, it is revealed that the real-time dynamic capture of traffic flow can effectively improve prediction accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

Data availability

Enquiries about data availability should be directed to the authors.

References

  • Alghamdi T, Elgazzar K, Bayoumi M, Sharaf T, Shah S (2019) Forecasting traffic congestion using arima modeling. 2019 15th International wireless communications and mobile computing conference (IWCMC). IEEE, Kenzi Solazur Tanger, pp 1227–1232

    Google Scholar 

  • Chan KY, Dillon T, Singh J, Chang E (2011) Traffic flow forecasting neural networks based on exponential smoothing method. In: 2011 6th IEEE conference on industrial electronics and applications, Waikoloa Village, pp 376–381. IEEE

  • Chan KY, Dillon TS, Singh J, Chang E (2011) 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 

  • Chen X, Wu S, Shi C, Huang Y, Yang Y, Ke R, Zhao J (2020) Sensing data supported traffic flow prediction via denoising schemes and ann: a comparison. IEEE Sens J 20(23):14317–14328

    Article  Google Scholar 

  • Chen Y, Lv Y, Ye P, Zhu F (2020) Traffic-condition-awareness ensemble learning for traffic flow prediction. IFAC PapersOnLine 53(5):582–587

    Article  Google Scholar 

  • Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd international conference on knowledge discovery and data mining, pp 785–794. ACM, San Francisco

  • Clerc M, Kennedy J (2002) The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Comput Intell Mag 6(1):58–73

    Google Scholar 

  • Dimitrakopoulos G, Demestichas P (2010) Intelligent transportation systems. IEEE Vehicular Technol Mag 5(1):77–84

    Article  Google Scholar 

  • Feng X, Ling X, Zheng H, Chen Z, Xu Y (2018) Adaptive multi-kernel svm with spatial-temporal correlation for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 20(6):2001–2013

    Article  Google Scholar 

  • Giraka O, Selvaraj VK (2020) Short-term prediction of intersection turning volume using seasonal arima model. Transp Lett 12(7):483–490

    Article  Google Scholar 

  • Giraka O, Selvaraj VK (2020) Short-term prediction of intersection turning volume using seasonal arima model. Transp Lett 12(7):483–490

    Article  Google Scholar 

  • Guo jiang S, (2010) An intelligent hybrid forecasting model for short-term traffic flow. In: 2010 8th World Congress on intelligent control and automation. IEEE, Jinan, pp 486–491

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

    Article  Google Scholar 

  • Guo L, Shi X, Cao J (2022) Exponential convergence of primal-dual dynamical system for linear constrained optimization. IEEE/CAA J Auto Sin 9(4):745–748

    Article  Google Scholar 

  • Hancock JT, Khoshgoftaar TM (2020) Catboost for big data: an interdisciplinary review. J Big Data 7(1):1–45

    Article  Google Scholar 

  • Harrou F, Zeroual A, Sun Y (2020) Traffic congestion monitoring using an improved knn strategy. Measurement 156:107534

    Article  Google Scholar 

  • Hou Y, Edara P, Sun C (2014) Traffic flow forecasting for urban work zones. IEEE Trans Intell Transp Syst 16(4):1761–1770

    Article  Google Scholar 

  • Hu W, Yan L, Liu K, Wang H (2016) A short-term traffic flow forecasting method based on the hybrid pso-svr. Neural Process Lett 43(1):155–172

    Article  Google Scholar 

  • Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. 2004 IEEE Int Joint Conf Neural Netw, vol 2. IEEE, Budapest, pp 985–990

  • Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  • Iosifidis A, Tefas A, Pitas I (2015) On the kernel extreme learning machine classifier. Pattern Recogn Lett 54:11–17

    Article  Google Scholar 

  • Kai C, Zhang KW, Hamamatsu Y (2006) Traffic information real-time monitoring based on a short-long term algorithm. In: 2006 IEEE international conference on systems, man and cybernetics, vol 1. IEEE, Taipei, pp 651–656

  • Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T.-Y (2017) Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst, 30

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol 4, pp 1942–1948. IEEE, Perth

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

    Article  Google Scholar 

  • Li C, Anavatti SG, Ray T (2011) Short-term traffic flow prediction using different techniques. In: IECON 2011–37th annual conference of the IEEE industrial electronics society. IEEE, Melbourne, pp 2423–2428

  • Li Z, Tao M, Cao J, Shi X, Ma T, Huang W (2022) An augmented model of rutting data based on radial basis neural network. Symmetry 15(1):33

    Article  Google Scholar 

  • Li Z, Shi X, Cao J, Wang X, Huang W (2022) CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction. Sci China Technol Sci 65(7):1470–1481. https://doi.org/10.1007/s11431-021-1972-7

    Article  Google Scholar 

  • Lin X, Huang Y (2021) Short-term high-speed traffic flow prediction based on arima-garch-m model. Wirel Pers Commun 117(4):3421–3430

    Article  Google Scholar 

  • Liu F, Wei Z, Huang Z, Lu Y, Hu X, Shi L (2019) A multi-grouped ls-svm method for short-term urban traffic flow prediction. In: 2019 IEEE global communications conference (GLOBECOM). IEEE, Waikoloa Village, pp 1–6

  • Lu S, Zhang Q, Chen G, Seng D (2021) A combined method for short-term traffic flow prediction based on recurrent neural network. Alex Eng J 60(1):87–94

    Article  Google Scholar 

  • Luo C, Huang C, Cao J, Lu J, Huang W, Guo J, Wei Y (2019) Short-term traffic flow prediction based on least square support vector machine with hybrid optimization algorithm. Neural Process Lett 50(3):2305–2322

    Article  Google Scholar 

  • Ma C, Tan L, Xu X (2020) Short-term traffic flow prediction based on genetic artificial neural network and exponential smoothing. Promet Traffic Transp 32(6):747–760

    Google Scholar 

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  • Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C Emerg Technol 79:1–17

    Article  Google Scholar 

  • Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation-CEC99, vol 3, pp 1945–1950. IEEE, Washington

  • Shi Y, et al. (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol 1, pp 81–86. IEEE, Seoul

  • Sidqi F, Sumitra I (2019) Forecasting product selling using single exponential smoothing and double exponential smoothing methods. In: IOP conference series: materials science and engineering, vol 662, p 032031. IOP Publishing, Chongqing

  • Stefenon SF, Ribeiro MHDM, Nied A, Yow K-C, Mariani VC, dos Santos Coelho L, Seman LO (2022) Time series forecasting using ensemble learning methods for emergency prevention in hydroelectric power plants with dam. Electric Power Syst Res 202:107584

    Article  Google Scholar 

  • Sun Y, Hou Z et al (2018) A novel abnormal traffic incident detection method based on improved support vector machine. J Appl Sci Eng 21(1):45–50

    Google Scholar 

  • Sun Shiliang, Zhang Changshui, Guoqiang Yu (2006) A bayesian network approach to traffic flow forecasting. IEEE Trans Intell Transp Syst 7(1):124–132

    Article  Google Scholar 

  • Tan MC, Wong SC, Xu JM, Guan ZR, Zhang P (2009) An aggregation approach to short-term traffic flow prediction. IEEE Trans Intell Transp Syst 10(1):60–69

    Article  Google Scholar 

  • Tang J, Xu G, Wang Y, Wang H, Zhang S, Liu F (2013) Traffic flow prediction based on hybrid model using double exponential smoothing and support vector machine. In: 16th International IEEE conference on intelligent transportation systems (ITSC 2013). IEEE, Hague, pp 130–135

  • Wang J, Li L, Niu D, Tan Z (2012) An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl Energy 94:65–70

    Article  Google Scholar 

  • Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85

    Article  Google Scholar 

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

  • Wu T, Cao J, Ma T, Huang W, Zhou Z, Chen B (2023) Development of rutting forecasting models for distinct asphalt pavement structures in rioh testing track using different approaches. Construct Build Mater 368:130483

  • Wu T, Gorbachev S, Lam H-K, Park JH, Xiong L, Cao J (2022) Adaptive event-triggered space-time sampled-data synchronization for fuzzy coupling rdnns under hybrid random cyber attacks. IEEE Trans Fuzzy Syst

  • Xu Y, Kong Q-J, Liu Y (2013) Short-term traffic volume prediction using classification and regression trees. In: 2013 IEEE intelligent vehicles symposium. IEEE, Gold Coast, pp 493–498

  • Yang HF, Dillon TS, Chang E, Chen YPP (2018) Optimized configuration of exponential smoothing and extreme learning machine for traffic flow forecasting. IEEE Trans Ind Inf 15(1):23–34

    Article  Google Scholar 

  • Yeganeh A, Pourpanah F, Shadman A (2021) An ann-based ensemble model for change point estimation in control charts. Appl Soft Comput 110:107604

    Article  Google Scholar 

  • Yun M, Qin W, Yang X, Liang F (2019) Estimation of urban route travel time distribution using Markov chains and pair-copula construction. Transportmetrica B Transp Dyn 7(1):1521–1552

    Article  Google Scholar 

  • Zhang Y, Zhang Y, Haghani A (2014) A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model. Transp Res Part C Emerg Technol 43:65–78

    Article  Google Scholar 

  • Zhang Y, Ren G, Liu X, Gao G, Zhu M (2022) Ensemble learning-based modeling and short-term forecasting algorithm for time series with small sample. Eng Rep 4(5):12486

    Google Scholar 

Download references

Funding

This work was supported by the National Key Research and Development Program of China under Grants No. 2020YFA0714300, the National Natural Science Foundation of China under Grant Nos. 61833005 and 62003084, and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20200355.

Author information

Authors and Affiliations

  1. School of Mathematics, Southeast University, Nanjing, 210096, China

    Zhuoxuan Li & Jinde Cao

  2. Nanjing Modern Multimodal Transportation Laboratory, Nanjing, 211100, China

    Jinde Cao

  3. Yonsei Frontier Lab, Yonsei University, Seoul, 03722, South Korea

    Jinde Cao

  4. School of Cyber Science and Engineering, Southeast University, Nanjing, 210096, China

    Xinli Shi

  5. Intelligent Transportation System Research Center, Southeast University, Nanjing, 210096, China

    Wei Huang

Authors
  1. Zhuoxuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinde Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinli Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Each author has equally contributed to conceptualization, model building, calculation, and writing of the article.

Corresponding author

Correspondence to Jinde Cao.

Ethics declarations

Conflict of interest

Not applicable.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Cao, J., Shi, X. et al. QPSO-AHES-RC: a hybrid learning model for short-term traffic flow prediction. Soft Comput 27, 9347–9366 (2023). https://doi.org/10.1007/s00500-023-08291-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-08291-w

Keywords

Search

Navigation