Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information |
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| Authors: | Linchao Li Shanglu He Jian Zhang Bin Ran |
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| Affiliation: | 1. Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China;3. Research Center for Internet of Mobility, Southeast University, Nanjing, China |
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| Abstract: | Short‐term traffic flow prediction is fundamental for the intelligent transportation system and is proved to be a challenge. This paper proposed a hybrid strategy that is general and can make use of a large number of underlying machine learning or time‐series prediction models to capture the complex patterns beneath the traffic flow. With the strategy, four different combinations were implemented. To consider the spatial features of traffic phenomenon, several different state vectors including different observations were built. The performance of the proposed strategy was investigated using the traffic flow measurements from the Traffic Operation and Safety Laboratory in Wisconsin, USA. The results show the overall performance of hybrid strategy is better than a single model. Also, incorporating observations from adjacent junctions can improve prediction accuracy. Copyright © 2017 John Wiley & Sons, Ltd. |
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| Keywords: | traffic flow prediction hybrid strategy spatial– temporal information state vector |
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