期刊
KNOWLEDGE-BASED SYSTEMS
卷 163, 期 -, 页码 472-484出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2018.09.003
关键词
Traffic speeds; Short-term; Speed prediction; Adjacent roads; Traffic attributes
资金
- National Natural Science Foundation of China [61100043]
- Key Science and Technology Project of Zhejiang Province, China [2017C01010]
- CSIRO Data61
- ARC, Australia [DP140103499, DP160102412]
Forecasting the short-term speed of moving vehicles on roads plays a vital role on traffic control and trip planning, which however still remains a challenging task when the high accuracy is required. In this paper, we propose a novel approach to the short-term traffic speed forecasting, which takes into account the influence of different traffic attributes, such as traffic flow, traffic speed, road occupancy and traffic density, of adjacent roads on the traffic speed. In addition, in order to obtain the more accurate relation between traffic speed and traffic attributes, we employ the idea of piecewise correlation function and adopt the Jenks clustering method with dynamic programming to determine the segment intervals of relation. We validate our approach based on the real data collected from Wenzhou and Hangzhou, two large cities located in eastern China. The extensive experimental results show that, compared with the state-of-the-art approaches, our approach has the higher stability and accuracy, especially for 5-minute and 10-minute speed prediction. (C) 2018 Elsevier B.V. All rights reserved.
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