Abstract
Real-time prediction of the traffic condition is an important ingredient for a variety of applications. In this paper, we propose an Ensemble based Traffic Condition Prediction System (ETCPS) for predicting the traffic conditions of any roads in a city based on the current and historical GPS data collected from floating vehicles. We have observed two useful correlations in the traffic condition time series, which are the bases of our design. In order to exploit these two correlations for prediction, we propose two different models called Predictive Regression Tree (PR-Tree) and Spatial Temporal Probabilistic Graphical Model (STPGM). Our best quality prediction is achieved by a careful ensemble of the two models. Our system provides high-quality prediction and can easily scale to very large datasets. We conduct extensive experimental evaluations with a large GPS data set collected from more than 12,000 taxis in Beijing during two months. The experimental results demonstrate the effectiveness, efficiency, and scalability of our system.
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Acknowledgment
This work was supported in part by the National Basic Research Program of China grants 2015CB358700, 2011CBA00300, 2011CBA00301, and the National NSFC grants 61033001, 61361136003.
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Wang, D., Cao, W., Xu, M., Li, J. (2016). ETCPS: An Effective and Scalable Traffic Condition Prediction System. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_26
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DOI: https://doi.org/10.1007/978-3-319-32049-6_26
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