Skip to main content

Research Repository

Advanced Search

Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems

Angarita-Zapata, Juan S.; Masegosa, Antonio D.; Triguero, Isaac

Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems Thumbnail


Authors

Juan S. Angarita-Zapata

Antonio D. Masegosa



Contributors

Orestes Llanes Santiago
Editor

Carlos Cruz Corona
Editor

Ant�nio Jos� Silva Neto
Editor

Jos� Luis Verdegay
Editor

Abstract

© Springer Nature Switzerland AG 2020. Traffic forecasting is a well-known strategy that supports road users and decision-makers to plan their movements on the roads and to improve the management of traffic, respectively. Current data availability and growing computational capacities have increased the use of machine learning methods to tackle traffic forecasting, which is mostly modelled as a supervised regression problem. Despite the broad range of machine learning algorithms, there are no baselines to determine what are the most suitable methods and their hyper-parameters configurations to approach the different traffic forecasting regression problems reported in the literature. In machine learning, this is known as the model selection problem, and although automated machine learning methods have proved successful dealing with this problem in other areas, it has hardly been explored in traffic forecasting. In this work, we go deeply into the benefits of automated machine learning in the aforementioned field. To this end, we use Auto-WEKA, a well-known AutoML method, on a subset of families of traffic forecasting regression problems characterised by having loop detectors, as traffic data source, and scales of predictions focused on the point and the road segment levels within freeway and urban environments. The experiments include data from the Caltrans Performance Measurement System and the Madrid City Council. The results show that AutoML methods can provide competitive results for TF with low human intervention.

Citation

Angarita-Zapata, J. S., Masegosa, A. D., & Triguero, I. (2020). Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems. In O. Llanes Santiago, C. Cruz Corona, A. J. Silva Neto, & J. L. Verdegay (Eds.), Computational intelligence in emerging technologies for engineering applications (187-204). Springer. https://doi.org/10.1007/978-3-030-34409-2_11

Acceptance Date Jun 4, 2019
Online Publication Date Feb 15, 2020
Publication Date Feb 15, 2020
Deposit Date Jun 24, 2019
Publicly Available Date Mar 29, 2024
Publisher Springer
Pages 187-204
Book Title Computational intelligence in emerging technologies for engineering applications
ISBN 9783030344085
DOI https://doi.org/10.1007/978-3-030-34409-2_11
Keywords Traffic forecasting ; Supervised learning ; Machine learning ; Automated machine learning ; Computational intelligence ; Intelligent transport systems
Public URL https://nottingham-repository.worktribe.com/output/2223531
Publisher URL https://www.springer.com/gp/book/9783030344085
Additional Information First Online: 15 February 2020

Files




You might also like



Downloadable Citations