Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems

TL;DR: This work uses 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 to go deeply into the benefits of automated machine learning in traffic forecasting.

Abstract: 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.