Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Online Prediction Model of Short-Term Traffic Flow Based on Improved LS-SVM
The change of urban road traffic flow has strong randomness and strong timeliness. The traditional short-term traffic flow prediction model has high complexity and strong closeness, and it is difficult to make full use of the previous learning results. In this paper, we analyze the support vector sparsity loss and parameter optimization time-consuming of Least Squares Support Vector Machine (LS-SVM) and propose a Dynamic Parameter Optimization LS-SVM named DPO-LSSVM for predicting short-term traffic flow. The experiments conducted on the real-world data set demonstrate the effectiveness of DPO-LSSVM on the training speed. Comparing with the Particle Swarm Optimization LS-SVM (PSO-LSSVM) and the RBF neural network online prediction model, the average training time of our model is only 30–60% of the other two.
Online Prediction Model of Short-Term Traffic Flow Based on Improved LS-SVM
The change of urban road traffic flow has strong randomness and strong timeliness. The traditional short-term traffic flow prediction model has high complexity and strong closeness, and it is difficult to make full use of the previous learning results. In this paper, we analyze the support vector sparsity loss and parameter optimization time-consuming of Least Squares Support Vector Machine (LS-SVM) and propose a Dynamic Parameter Optimization LS-SVM named DPO-LSSVM for predicting short-term traffic flow. The experiments conducted on the real-world data set demonstrate the effectiveness of DPO-LSSVM on the training speed. Comparing with the Particle Swarm Optimization LS-SVM (PSO-LSSVM) and the RBF neural network online prediction model, the average training time of our model is only 30–60% of the other two.
Online Prediction Model of Short-Term Traffic Flow Based on Improved LS-SVM
Stud. in Distributed Intelligence
Yuan, Xiaohui (Herausgeber:in) / Elhoseny, Mohamed (Herausgeber:in) / Ma, Zhongjun (Autor:in) / Feng, Lin (Autor:in) / Wei, Zhenchun (Autor:in) / Lyu, Zengwei (Autor:in) / Huang, Zhensheng (Autor:in) / Liu, Fei (Autor:in)
26.06.2020
13 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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