NDLI: Improved Particle Swarm Optimized SVM for Short-term Traffic Flow Predication

Content Provider	IEEE Xplore Digital Library
Author	Cao Chengtao Xu Jianmin
Copyright Year	2007
Description	Author affiliation: South China Univ. of Technol., Guangzhou (Cao Chengtao; Xu Jianmin)
Abstract	Short-term traffic flow predication has played a key role in ITS. Since traffic flow has the property of periodicity and randomicity, a new short-tern traffic flow predication model based on support vector machine (SVM) is proposed, the parameter performance of SVM for regression estimation, the parameter of SVM is optimized by an improved particle swarm optimization (IPSO). The IPSO uses the dynamic best inertia weight and acceleration coefficient, which avoids the PSO plunging into local optima and make it converge faster. As the proposed model can reduce the dimensionality of data space and preserve features of traffic flow time series, it can predict traffic flow efficiently. The simulation results of traffic flow collected from Chinese national highway G107 show that the IPSO-SVM has greater efficiency and better performance than PSO-SVM. The average predication error is 3.3%, which proves the proposed model's validity.
Starting Page	6
Ending Page	9
File Size	168485
Page Count	4
File Format	PDF
ISBN	9787811240559
DOI	10.1109/CHICC.2006.4346809
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Support Vector Machine Particle swarm optimization Machine intelligence Intelligent transportation systems Convergence Support vector machines Road transportation Traffic Flow predication Traffic control Communication system traffic control Acceleration Improved Particle Swarm Optimization
Content Type	Text
Resource Type	Article

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