详情

原文传递 Multi-step-ahead traffic speed forecasting using multi-output gradient boosting regression tree

题名：	Multi-step-ahead traffic speed forecasting using multi-output gradient boosting regression tree
正文语种：	英文
作者：	Xingbin Zhan;Shuaichao Zhang;Wai Yuen Szeto;Xigun (Michael) Chen
作者单位：	University of Hong Kong
关键词：	Direct strategy; iterated strategy; multivariate GBRT; multi-step-ahead prediction;traffic speed forecasting
摘要：	Short-term traffic speed forecasting is an important component of Intellige nt Trans portation Systems (ITS). Multi-step-ahead prediction can provide more information and predict the longer trend of traffic speed than single-step-ahead prediction. This paper presents a multistep-ahead traffic speed prediction approach by improving the gradient boosting regression tree (GBRT). The traditional multiple output strategies, e.g., the direct strategy and iterated strategy, share a common feature that they model the samples through multi-input singleoutput mapping rather than multi-input multi-output mapping. This paper proposes multivariate GBRT to realize simultaneous multiple outputs by considering correlations of the outputs which have not been fully considered in the existing strategies. For illustrative purposes, traffic detection data are extracted at the 5-min aggregation time interval from three loop detectors in US101-N freeway through the Performanee Measurement System (PeMS). The support vector regression (SVR) is used as the benchmark. Assessments on the three models are based on the three criteria, i.e., prediction accuracy, prediction stability, and prediction time. The results indicate that (I) Multivariate GBRT and GBRT using the direct strategy have higher prediction accuracies compared with SVR; (II) GBRT using the iterated strategy has a good prediction accuracy in short-step-ahead prediction and the prediction accuracy decreases significantly in Iong-step-ahead prediction; (III) Multivariate GBRT has the best stability which means the higher reliability in multi-step-ahead prediction while iterated GBRT has the worst stability; and (IV) Multivariate GBRT has an enormous advantage in the prediction efficiency and this advantage will expand with the increasing prediction horizons.
出版日期：	2020
出版年：	2020
期刊名称：	Journal of Intelligent Transportation Systems Technology Planning and Operations
卷：	Vol24
期：	No01-06
页码：	125-141