Travel Speed Forecasting by Means of Continuous Conditional Random Fields

This paper explores the application of the recently proposed continuous conditional random fields (CCRF) to travel forecasting. CCRF is a flexible, probabilistic framework that can seamlessly incorporate multiple traffic predictors and exploit spatial and temporal correlations inherently present in traffic data. In addition to improving prediction accuracy, the probabilistic approach provides information about prediction uncertainty. Moreover, information about the relative importance of particular predictor and spatial–temporal correlations can be easily extracted from the model. CCRF is fault-tolerant and can provide predictions even when some observations are missing. Several CCRF models were applied to the problem of travel speed prediction in a range from 10 to 60 min ahead and evaluated on loop detector data from a 5.71-mi section of I-35W in Minneapolis, Minnesota. Several CCRF models, with increasing levels of complexity, are proposed to assess performance of the method better. When these CCRF models were compared with the linear regression model, they reduced the mean absolute error by around 4%. The results imply that modeling spatial and temporal neighborhoods in traffic data and combining various baseline predictors under the CCRF framework can be beneficial.