Adaptive Multi-Kernel SVM With Spatial-Temporal Correlation for Short-Term Traffic Flow Prediction

Accurate estimation of the traffic state can help to address the issue of urban traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm based on an adaptive multi-kernel support vect...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems 2019-06, Vol.20 (6), p.2001-2013
Hauptverfasser:	Feng, Xinxin, Ling, Xianyao, Zheng, Haifeng, Chen, Zhonghui, Xu, Yiwen
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive algorithms adaptive multi-kernel support vector machine adaptive particle swarm optimization Algorithms Correlation Driving conditions Forecasting Kernel Kernels Particle swarm optimization Polynomials Prediction algorithms Predictive models Real-time systems Short term Short-term traffic flow prediction spatial-temporal correlation Support vector machines Traffic congestion Traffic flow
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