Skip to main content

Advertisement

SpringerLink
Log in

A novel methodology to predict urban traffic congestion with ensemble learning

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Urban traffic congestion prediction is a very hot topic due to the environmental and economical impacts that currently implies. In this sense, to be able to predict bottlenecks and to provide alternatives to the circulation of vehicles becomes an essential task for traffic management. A novel methodology, based on ensembles of machine learning algorithms, is proposed to predict traffic congestion in this paper. In particular, a set of seven algorithms of machine learning has been selected to prove their effectiveness in the traffic congestion prediction. Since all the seven algorithms are able to address supervised classification, the methodology has been developed to be used as a binary classification problem. Thus, collected data from sensors located at the Spanish city of Seville are analyzed and models reaching up to 83 % are generated.

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

Similar content being viewed by others

References

  • Adewumi A, Kagamba J, Alochukwu A(2016) Application of chaos theory in the prediction of motorised traffic flows on urban networks. Mathematical Problems in Engineering, ID5656734:1–15

  • Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Machine learning 6(1):37–66

    Google Scholar 

  • Ahmane M, Abbas-Turki A, Perronnet F, Wu J, El-Moudni A, Buisson J, Zeo R (2013) Modeling and controlling an isolated urban intersection based on cooperative vehicles. Transportation Research Part C: Emerging Technologies 28:44–62

    Article  Google Scholar 

  • Amin SM, Liu A-P, Rodin EY, Rink K, García-Ortiz A (1998) Traffic prediction and management via RBF neural nets and semantic control. Computer-Aided Civil and Infrastructure Engineering 13(5):315–327

    Article  Google Scholar 

  • Asencio-Cortés G, Martínez-Álvarez F, Morales-Esteban A, Reyes J, Troncoso A (2015) Improving earthquake prediction with principal component analysis: Application to chile. Lecture Notes in Artificial Intelligence 9121:393–404

    Google Scholar 

  • Asencio-Cortés G, Martínez-Álvarez F, Reyes J, Morales-Esteban A, Reyes J (2016) A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction. Knowledge-Based Systems 101:15–30

    Article  Google Scholar 

  • Breiman L (1996) Bagging predictors. Machine learning 24(2):123–140

    MathSciNet  MATH  Google Scholar 

  • Breiman L (2001) Random forests. Machine learning 45(1):5–32

    Article  MathSciNet  MATH  Google Scholar 

  • Carlson RC, Papamichail I, Papageorgiou M, Messmer A (2010) Optimal mainstream traffic flow control of large scale motorway networks. Transportation Research Part C: Emerging Technologies 18:193–212

    Article  Google Scholar 

  • Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3):27

    Article  Google Scholar 

  • Cohen WW (1995) Fast effective rule induction. In Proceedings of the International Conference on Machine Learning, pages 115–123

  • Collins JF (1993) Automatic incident detection: Experience with TRRL algorithm HIOCC. TRRL Supplementary Report 775:1–6

    Google Scholar 

  • Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research 12:2121–2159

    MathSciNet  MATH  Google Scholar 

  • Dunkel J, Fernandez A, Ortiz R, Ossowski S (2011) Event-driven architecture for decision support in traffic management systems. Expert Systems with Applications 38:6530–6539

    Article  Google Scholar 

  • Florido E, Castaño O, Troncoso A, Martínez-Álvarez F(2015) Data mining for predicting traffic congestion and its application to spanish data. In Proceedings of the International Conference of Soft Computing Models in Industrial and Environmental Applications, pages 341–352

  • Freund Y, Schapire RE et al (1996) Experiments with a new boosting algorithm. In Proceedings of the International Conference on Machine Learning 96:148–156

    Google Scholar 

  • Friedman J, Hastie T, Tibshirani R et al (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics 28(2):337–407

    Article  MathSciNet  MATH  Google Scholar 

  • Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Machine learning 29(2–3):131–163

    Article  MATH  Google Scholar 

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1):10–18

    Article  Google Scholar 

  • Hernández JZ, Ossowski S, García-Serrano A (2002) Multiagent architectures for intelligent traffic management system. Transportation Research Part C: Emerging Technologies 10(5):473–506

    Article  Google Scholar 

  • Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. IEEE Transactions on Intelligent Transportation Systems 15(5):2191–2201

    Article  Google Scholar 

  • Hühn J, Hüllermeier E (2009) FURIA: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery 19(3):293–319

  • Kong X, Xu Z, Shen G, Wang J, Yang Q, Zhang B (2016) Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Generation Computer Systems 61:97–107

    Article  Google Scholar 

  • Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. John Wiley & Sons,

  • Landwehr N, Hall M, Frank E (2005) Logistic model trees. Machine Learning 59(1–2):161–205

    Article  MATH  Google Scholar 

  • Lee WH, Tseng SS, Shieh WY (2010) Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system. Information Sciences 180:62–702

    Article  Google Scholar 

  • Li F, Gong J, Liang Y, Zhou J (2016) Real-time congestion prediction for urban arterials using adaptive data-driven methods. Multimedia Tools and Applications 13:1–20

    Google Scholar 

  • Liang Z, Wakahara Y (2014) Real-time urban traffic amount prediction models for dynamic route guidance systems. EURASIP Journal on Wireless Communications and Networking 85:1–13

    Google Scholar 

  • Ma X, Yu H, Wang Y, Wang Y (2015) Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE 10(3):1–17

    Google Scholar 

  • Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta - Protein Structure 405:442–451

    Article  Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calulus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  • Nellore K, Hancke GP (2016) A survey on urban traffic management system using wireless sensor networks. Sensors 16:1–25

    Article  Google Scholar 

  • Ogunwolu L, Adedokun O, Orimoloye O, Oke SA (2014) A neuro-fuzzy approach to vehicular traffic flow prediction for a metropolis in a developing country. Journal of Industrial Engineering International 7(13):52–66

    Google Scholar 

  • Parejo Maestre JA, García J, Ruiz-Cortés A, Riquelme JC (2012) Statservice: Herramienta de análisis estadístico como soporte para la investigación con metaheurísticas. In Actas del VIII Congreso Expañol sobre Metaheurísticas, Algoritmos Evolutivos y Bio-inspirados, pp 1–8

  • Pescaru D (2013) Urban traffic congestion prediction based on routes information. In: Proceedings of the IEEE International Symposium on Applied Computational Intelligence and Informatics, pages 121–126

  • Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers,

  • Rao AM, Rao KM (2012) Measuring urban traffic congestion - a review. International Journal for Traffic and Transport Engineering 2(4):286–305

    Article  Google Scholar 

  • Salzberg SL (1994) C4. 5: Programs for machine learning. Machine Learning 16(3):235–240

    MathSciNet  Google Scholar 

  • Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press,

  • Wei Qu Z, Xing Y, Song XM, Duan YZ, Wei F (2012) A study on the coordination of urban traffic control and traffic assignment. Discrete Dynamics in Nature and Society 2012(12):367–368

    MATH  Google Scholar 

  • Wolpert DH (1992) Stacked generalization. Neural networks 5(2):241–259

    Article  MathSciNet  Google Scholar 

  • Yang S (2013) On feature selection for traffic congestion prediction. Transportation Research Part C: Emerging Technologies 26:160–169

    Article  Google Scholar 

Download references

Acknowledgments

This study was funded by the Spanish Ministry of Economy and Competitiveness and by the Junta de Andalucía under projects TIN2014-55894-C2-R and P12-TIC-1728, respectively.

Author information

Authors and Affiliations

  1. Division of Computer Science, Universidad Pablo de Olavide, 41013, Seville, Spain

    G. Asencio-Cortés, E. Florido, A. Troncoso & F. Martínez-Álvarez

Authors
  1. G. Asencio-Cortés
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Florido
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Troncoso
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. Martínez-Álvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Martínez-Álvarez.

Ethics declarations

Conflict of interest

G. Asencio-Cortés declares that he has no conflict of interest. E. Florido declares that he has no conflict of interest. A. Troncoso declares that she has no conflict of interest. F. Martínez-Álvarez declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by A. Herrero.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Asencio-Cortés, G., Florido, E., Troncoso, A. et al. A novel methodology to predict urban traffic congestion with ensemble learning. Soft Comput 20, 4205–4216 (2016). https://doi.org/10.1007/s00500-016-2288-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2288-6

Keywords

Search

Navigation