Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction
	Xu, Yanyan 1,2; Kong, Qing-Jie3 ; Klette, Reinhard 4; Liu, Yuncai 1,2
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2014-12-01
卷号	15 期号:6 页码:2457-2469
关键词	Bayesian inference interpretable model Markov chain Monte Carlo (MCMC) multivariate adaptive-regression splines (MARS) spatiotemporal relationship analysis traffic flow prediction
英文摘要	Current research on traffic flow prediction mainly concentrates on generating accurate prediction results based on intelligent or combined algorithms but ignores the interpretability of the prediction model. In practice, however, the interpretability of the model is equally important for traffic managers to realize which road segment in the road network will affect the future traffic state of the target segment in a specific time interval and when such an influence is expected to happen. In this paper, an interpretable and adaptable spatiotemporal Bayesian multivariate adaptive-regression splines (ST-BMARS) model is developed to predict short-term freeway traffic flow accurately. The parameters in the model are estimated in the way of Bayesian inference, and the optimal models are obtained using a Markov chain Monte Carlo (MCMC) simulation. In order to investigate the spatial relationship of the freeway traffic flow, all of the road segments on the freeway are taken into account for the traffic prediction of the target road segment. In our experiments, actual traffic data collected from a series of observation stations along freeway Interstate 205 in Portland, OR, USA, are used to evaluate the performance of the model. Experimental results indicate that the proposed interpretable ST-BMARS model is robust and can generate superior prediction accuracy in contrast with the temporal MARS model, the parametric model autoregressive integrated moving averaging (ARIMA), the state-of-the-art seasonal ARIMA model, and the kernel method support vector regression.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
研究领域[WOS]	Engineering ; Transportation
关键词[WOS]	REGRESSION ; SPLINES ; MODELS ; VOLUME
收录类别	SCI
语种	英语
WOS记录号	WOS:000345572900009
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3628]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
作者单位	1.Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China 2.Shanghai Jiao Tong Univ, China Key Lab Syst Control & Informat Proc, Minist Educ, Shanghai 200240, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Univ Auckland, Dept Comp Sci, Auckland 1020, New Zealand
推荐引用方式 GB/T 7714	Xu, Yanyan,Kong, Qing-Jie,Klette, Reinhard,et al. Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2014,15(6):2457-2469.
APA	Xu, Yanyan,Kong, Qing-Jie,Klette, Reinhard,&Liu, Yuncai.(2014).Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,15(6),2457-2469.
MLA	Xu, Yanyan,et al."Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 15.6(2014):2457-2469.