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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2019

01.03.2019

Short-Term Traffic Flow Prediction Based on Least Square Support Vector Machine with Hybrid Optimization Algorithm

verfasst von: Chuan Luo, Chi Huang, Jinde Cao, Jianquan Lu, Wei Huang, Jianhua Guo, Yun Wei

Erschienen in: Neural Processing Letters | Ausgabe 3/2019

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Abstract

Accurate short-term traffic flow prediction plays an indispensable role for solving traffic congestion. However, the structure of traffic data is nonlinear and complicated. It is a challenge to get high precision. The least square support vector machine (LSSVM) has powerful capabilities for time series and nonlinear regression prediction problems if it can select appropriate parameters. To search the optimal parameters of LSSVM, this paper proposes a hybrid optimization algorithm which combines particle swarm optimization (PSO) with genetic algorithm. The main contributions are twofold: (1) A hybrid optimization method is proposed, which can skip the local optimal pitfall with less learning time by introducing a selection strategy, crossover and mutation operators into PSO; (2) the crossover and mutation operators are controlled by adaptive probability functions. The crossover and mutation probabilities increase when the population fitness is concentrated, and decrease when the fitness is dispersed. It can effectively improve the precision and speed of convergence. The proposed model is verified based on the measured data. The experimental results show that our new model yields better prediction ability and relatively high computational efficiency compared with other related models.
Vorheriger Artikel Feature Selection for Recognition of Online Handwritten Bangla Characters
Nächster Artikel Alignment Based Feature Selection for Multi-label Learning

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Metadaten
Titel
Short-Term Traffic Flow Prediction Based on Least Square Support Vector Machine with Hybrid Optimization Algorithm
verfasst von
Chuan Luo
Chi Huang
Jinde Cao
Jianquan Lu
Wei Huang
Jianhua Guo
Yun Wei
Publikationsdatum
01.03.2019
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-09994-8

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