OPEN TRAFFIC DATA OF ENGLAND AND SHORT-TERM TRAFFIC CONGESTION PREDICTION WITH AUTOML

Abstract

In order to study how to utilize open data on traffic, we downloaded traffic data of England, where the data are available to the public. We focused on short-time traffic congestion prediction as one method of utilization, selected a relatively congested area in southern England and used PyCaret, a type of AutoML, to predict traffic congestion. Our model performed slightly better than models that assumed that the current situation would continue as is or that only the day of the week and time of day were used as input variables, indicating that machine learning can be used to improve traffic congestion prediction. On the other hand, the prediction performance of the as-is model varied greatly depending on the direction of travel at the same location, and the performance of the machine learning model also varied significantly accordingly. In order to compare the performance of machine learning traffic congestion predicts, it is necessary to establish a baseline forecast and show the improvement in performance against that baseline forecast.