Title :
Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction
Author :
Yanyan Xu ; Qing-Jie Kong ; Klette, Reinhard ; Yuncai Liu
Author_Institution :
Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
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.
Keywords :
Markov processes; Monte Carlo methods; regression analysis; road traffic; splines (mathematics); support vector machines; ARIMA; Bayesian MARS; Bayesian inference; MCMC simulation; Markov chain Monte Carlo; ST-BMARS model; autoregressive integrated moving averaging; freeway traffic flow; kernel method support vector regression; optimal models; road network; road segment; short-term freeway traffic flow; spatiotemporal Bayesian multivariate adaptive-regression splines; target road segment; traffic flow prediction; traffic managers; traffic prediction; Bayes methods; Data models; Monte Carlo methods; Predictive models; Splines (mathematics); Traffic control; Bayesian inference; Markov chain Monte Carlo (MCMC); interpretable model; multivariate adaptive-regression splines (MARS); spatiotemporal relationship analysis; traffic flow prediction;
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/TITS.2014.2315794