Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model

Bing Qichun; Gong Bowen; Yang Zhaosheng; Shang Qiang; Zhou Xiyang

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Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model

机译：基于组合核函数相关向量机模型的短时交通流局部预测

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Short-term traffic flow prediction is one of the most important issues in the field of adaptive traffic control system and dynamic traffic guidance system. In order to improve the accuracy of short-term traffic flow prediction, a short-term traffic flow local prediction method based on combined kernel function relevance vector machine (CKF-RVM) model is put forward. The C-C method is used to calculate delay time and embedding dimension. The number of neighboring points is determined by use of Hannan-Quinn criteria, and the CKF-RVM model is built based on genetic algorithm. Finally, case validation is carried out using inductive loop data measured from the north-south viaduct in Shanghai. The experimental results demonstrate that the CKF-RVM model is 31.1% and 52.7% higher than GKF-RVM model and GKF-SVM model in the aspect of MAPE. Moreover, it is also superior to the other two models in the aspect of EC.

机译：短期交通流量预测是自适应交通控制系统和动态交通引导系统领域中最重要的问题之一。为了提高短期交通流量预测的准确性，提出了一种基于组合核函数相关向量机（CKF-RVM）模型的短期交通流量局部预测方法。 C-C方法用于计算延迟时间和嵌入尺寸。利用Hannan-Quinn准则确定相邻点的数量，并基于遗传算法建立了CKF-RVM模型。最后，使用从上海南北高架桥测得的感应环路数据进行案例验证。实验结果表明，在MAPE方面，CKF-RVM模型分别比GKF-RVM模型和GKF-SVM模型高31.1％和52.7％。而且，它在EC方面也优于其他两个模型。

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《Mathematical Problems in Engineering》 |2015年第19期|154703.1-154703.9|共9页
作者
Bing Qichun; Gong Bowen; Yang Zhaosheng; Shang Qiang; Zhou Xiyang;
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Jilin Univ, Coll Transportat, Changchun 130025, Peoples R China;

Jilin Univ, Coll Transportat, Changchun 130025, Peoples R China|Jilin Univ, State Key Lab Automobile Simulat & Control, Changchun 130025, Peoples R China|Jilin Univ, Jilin Prov Key Lab Rd Traff, Changchun 130025, Peoples R China;

Jilin Univ, Coll Transportat, Changchun 130025, Peoples R China|Jilin Univ, State Key Lab Automobile Simulat & Control, Changchun 130025, Peoples R China|Jilin Univ, Jilin Prov Key Lab Rd Traff, Changchun 130025, Peoples R China;

Jilin Univ, Coll Transportat, Changchun 130025, Peoples R China;

Jilin Univ, Coll Transportat, Changchun 130025, Peoples R China;

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1. Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model [J] . QichunBing, BowenGong, ZhaoshengYang, Mathematical Problems in Engineering: Theory, Methods and Applications . 2015,第5期

机译：基于组合核函数相关向量机模型的短时交通流局部预测
2. Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory: [J] . Qiang Shang, Ciyun Lin, Zhaosheng Yang, Advances in Mechanical Engineering . 2016,第8期

机译：基于粒子群算法的组合核函数最小二乘支持向量机的短期交通流量预测模型与混沌理论的结合：
3. Traffic Flow Prediction of Highway Based on Wavelet Relevance Vector Machine [J] . Kefei Liu, Zhisheng Xu Journal of information and computational science . 2011,第9期

机译：基于小波相关向量机的公路交通流预测。
4. Short-term traffic flow prediction with optimized Multi-kernel Support Vector Machine [C] . Xianyao Ling, Xinxin Feng, Zhonghui Chen, IEEE Congress on Evolutionary Computation . 2017

机译：优化的多核支持向量机在短期交通流量预测中的应用
5. Bi-Level Optimization Algorithm for Dynamic Reversible Lane Control Based on Short-Term Traffic Flow Prediction [D] . Liu, Chenxi . 2020

机译：基于短期交通流量预测的动态可逆车道控制双级优化算法
6. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine [O] . Qiang Shang, Ciyun Lin, Zhaosheng Yang, 2011

机译：基于奇异谱分析和核极限学习机的混合短时交通流预测模型
7. Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory [O] . Qiang Shang, Ciyun Lin, Zhaosheng Yang, 2016

机译：基于粒子群优化的组合核函数 - 最小二乘支持向量机结合混沌理论的短时交通流预测模型
8. Dynamic Traffic Flow Modeling for Incident Detection and Short-Term Congestion Prediction Project summary [R] . Balke, K. N. 2006

机译：事件检测和短期拥塞预测的动态交通流建模项目总结

Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model

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