Model based on relevance vector machine and fuzzy comprehensive evaluation for road condition prediction

被引：1
|
作者：
Lin H. [1 ,2 ]
Li L.-X. [1 ,2 ]
Wang H. [1 ,2 ]
Ma Z.-Q. [1 ,2 ]
Wan J.-X. [1 ,2 ]
机构：
[1] College of Data Science and Application, Inner Mongolia University of Technology, Hohhot
[2] Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service, Hohhot
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2021年 / 55卷 / 06期
关键词：
Combined kernel function; Fuzzy comprehensive evaluation; Relevance vector machine (RVM); Road condition prediction; Spark;
D O I：
10.3785/j.issn.1008-973X.2021.06.007
中图分类号：
学科分类号：
摘要：
A model based on relevance vector machine and fuzzy comprehensive evaluation was proposed in order to solve the problems of traffic congestion, low road service level, and low efficiency of citizens' travel. Genetic algorithm and particle swarm optimization algorithm were used as the parameter optimization algorithm in order to optimize relevance vector machine with combined kernel functions. Then the parameter optimization algorithm was parallelized by Spark to improve the training efficiency of model. Genetic algorithm and particle swarm optimization based on Spark parallelization were proposed, and combined kernel relevance vector machine (SPGAPSO-CKRVM) was optimized. Traffic flow and traffic speed were predicted by SPGAPSO-CKRVM, and the prediction results were used to calculate three traffic condition evaluation parameters: average speed, road occupancy and traffic density. These three parameters were input into fuzzy comprehensive evaluation model. Weight coefficients of these evaluation parameters in peak hours and normal hours were determined by entropy method. Road condition was divided into six levels. The proposed model was verified with the real data of Whitemud Drive in Canada. The experimental results show that the proposed model has better prediction accuracy and scalability than traditional methods. The accuracy of road condition prediction can reach 90.28%. Copyright ©2021 Journal of Zhejiang University (Engineering Science). All rights reserved.
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页码：1072 / 1082
页数：10
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