Abstract
Despite the broad range of Machine Learning (ML) algorithms, there are no clear baselines to find the best method and its configuration given a Short-Term Traffic Forecasting (STTF) problem. In ML, this is known as the Model Selection Problem (MSP). Although Automatic Algorithm Selection (AAS) has proved success dealing with MSP in other areas, it has hardly been explored in STTF. This paper deepens into the benefits of AAS in this field. To this end, we have used Auto-WEKA, a well-known AAS method, and compared it to the general approach (which consists of selecting the best of a set of algorithms) over a multi-class imbalanced classification STTF problem. Experimental results show AAS as a promising methodology in this area and allow important conclusions to be drawn on how to improve the performance of ASS methods when dealing with STTF.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Scikit-learn is a Python library of ML algorithms: http://scikit-learn.org.
- 2.
Category A indicates light to moderate traffic, whereas a category E means extended delays.
- 3.
References
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. Adv. Neural Inf. Process. Syst. 28, 2962ā2970 (2015)
Garcia, S., Fernandez, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 180(10), 2044ā2064 (2010)
Komer, B., Bergstra, J., Eliasmith, C.: Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn. In: Proceedings of SciPy, pp. 33ā39 (2014)
Krawczyk, B., McInnes, B.T., Cano, A.: Sentiment classification from multi-class imbalanced twitter data using binarization. In: Hybrid Artificial Intelligent Systems, pp. 26ā37 (2017)
Lana, I., Ser, J.D., Velez, M., Vlahogianni, E.I.: Road traffic forecasting: recent advances and new challenges. IEEE Intell. Transp. Syst. Mag. 10(2), 93ā109 (2018)
Lopez-Garcia, P., Onieva, E., Osaba, E., Masegosa, A.D., Perallos, A.: A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Trans. Intell. Transp. Syst. 17(2), 557ā569 (2016)
Luo, G.: A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Netw. Model. Anal. Health Inform. Bioinform. 5(1), 5ā18 (2016)
Ma, X., Yu, H., Wang, Y., Wang, Y.: Large-scale transportation network congestion evolution prediction using deep learning theory. PloS one 10(3), e0119,044 (2015)
Oh, S., Byon, Y.J., Jang, K., Yeo, H.: Short-term travel-time prediction on highway: a review of the data-driven approach. Transp. Rev. 35(1), 4ā32 (2015)
Sabharwal, A., Samulowitz, H., Tesauro, G.: Selecting near-optimal learners via incremental data allocation. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 2007ā2015 (2016)
Skycomp, I.B.M.: Major High- way Performance Ratings and Bottleneck Inventory. Maryland State Highway Administration, the Baltimore Metropolitan Council and Maryland Transportation Authority, State of Maryland (2009)
Sparks, E.R., Talwalkar, A., Haas, D., Franklin, M.J., Jordan, M.I., Kraska, T.: Automating model search for large scale machine learning. In: Proceedings of SoCC 2015, pp. 368ā380 (2015)
Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2013, pp. 847ā855 (2013)
Vlahogianni, E.I.: Optimization of traffic forecasting: intelligent surrogate modeling. Transp. Res. Part C: Emerg. Technol. 55, 14ā23 (2015)
Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where weāre going. Transp. Res. Part C: Emerg. Technol. 43, 3ā19 (2014)
Acknowledgements
This project has received funding from the European Unionās Horizon 2020 research and innovation programme under grant agreement No. 636220 and the Marie Sklodoska-Curie grant agreement No. 665959. This work has been also supported by the research projects TIN2014-56042-JIN from the Spanish Ministry of Economy and Competitiveness.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Angarita-Zapata, J.S., Triguero, I., Masegosa, A.D. (2018). A Preliminary Study on Automatic Algorithm Selection for Short-Term Traffic Forecasting. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-99626-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99625-7
Online ISBN: 978-3-319-99626-4
eBook Packages: EngineeringEngineering (R0)