Coupled application of deep learning model and quantile regression for travel time and its interval estimation using data in different dimensions

摘要

The rapid development of sensing and computing methods and their application to transportation engineering in recent years provide us data support to traffic flow prediction. However, the travel time prediction is still a complex and difficult task in the intelligent transportation system because of its nonlinear and nonstationary characteristics. In this study, a hybrid model coupling the deep learning model and the quantile regression (QR) has been proposed to achieve the deterministic and probabilistic travel time prediction. To consider multiple correlations of the traffic flow, a spatial-temporal state-space matrix has been developed. Then, a novel deep belief network stacked by several Gaussian Bernoulli Restricted Boltzmann Machine (GBRBM) to extract important features and a regression layer to finish the prediction were developed. Moreover, to strengthen the reliability of results, the QR was applied to generate a prediction interval. Using real-world data sets, the proposed hybrid model was evaluated and contrasted with several benchmark models. The results show the deep learning model outperform the shallow learning model. The prediction interval providing by QR is better than that provided by the traditional method. It indicates that our proposed hybrid model can obtain a more perfect and reliable prediction for travel time which is meaningful to the advanced traveler information system. (C) 2020 Elsevier B.V. All rights reserved.