GPS-Based Traffic Conditions Classification Using Machine Learning Approaches

Abstract

This paper addresses the problem of accurately estimating traffic conditions based on sparse GPS information. GPS data have limited spatial-temporal availability, particularly at a regional scale. Therefore, it lacks reliability to accurately estimate traffic conditions of a transportation network. This study proposes a novel methodology to address this problem. First, instead of estimating traffic conditions on a geographic road segment, traffic conditions are estimated for trip segments, which span multiple road segments. Second, machine learning methods are applied to classify traffic conditions. In this study, traffic conditions are defined as the combination of congestion level and road type. This study develops two machine learning models—a random forest (RF) model and a supervised clustering method—to classify traffic conditions, using trip characteristics such as average speed and acceleration. The two models are compared in relation to their accuracy and computational efficiency. Results show that speed-related trip characteristics, such as average instantaneous speed, are the most important variables for classifying traffic conditions in both methods. In addition, the proportion of idling in a trip is essential in distinguishing the Congested Highway and Uncongested Urban traffic conditions when applying the supervised clustering method. The comparison shows that the RF model has a higher estimation accuracy (81%) than the supervised clustering method (72%). Overall, this study shows that traffic conditions can be determined efficiently even in cases of limited GPS data.