Learning Common Metrics for Homogenous Tasks in Traffic Flow Prediction

Abstract

Nearest neighbor based nonparametric regression is a classic data-driven method for traffic flow prediction in intelligent transportation systems (ITS). Performances of those models depend heavily on the similarity or distance metric used to search nearest neighborhood. Metric learning algorithms have been developed to learn the distance metrics from data in recent years. In real-world transportation application, multiple forecasting tasks are set since there are lots of road sections and detector points in the traffic network. Previous works tend to learn only one global metric to be used for all the tasks or learn multiple local metrics for each task which may lead to under-fitting or over-fitting problem. To balance these two kinds of methods and improve the generalization of learned metrics, we propose a common metric learning algorithm under the intuition that homogenous tasks tend to have similar local metrics. Then the learned common metrics are used in common metric KNN (CM-KNN) for traffic flow prediction. Experimental results show that our algorithm to learn common metrics are reasonable and CM-KNN method for traffic flow prediction outperforms other competing methods.