摘要
为了缓解交通拥堵、道路服务水平低、市民出行效率低等问题,提出基于相关向量机和模糊综合评价的路况预测模型.利用遗传算法和粒子群算法作为参数寻优算法,优化组合核相关向量机.基于Spark并行化参数寻优算法,提高模型的训练效率.提出基于Spark并行化的遗传算法和粒子群算法,优化组合核相关向量机(SPGAPSO-CKRVM).使用SPGAPSO-CKRVM对车流量和车速进行预测,利用预测结果计算3个交通路况评价参数:平均车速、路段饱和度和交通流密度.将3个参数输入到模糊综合评价模型中,通过熵值法确定高峰时段和平常时段的各指标权重系数,将路况划分为6个等级.使用加拿大Whitemud Drive公路的真实数据进行验证,证明了该模型与传统方法相比具有更高的预测精度和扩展性,路况预测准确率达到90.28%.
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%.
作者
林浩
李雷孝
王慧
马志强
万剑雄
LIN Hao;LI Lei-xiao;WANG Hui;MA Zhi-qiang;WAN Jian-xiong(College of Data Science and Application,Inner Mongolia University of Technology,Hohhot 010080,China;Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service,Hohhot 010080,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2021年第6期1072-1082,共11页
Journal of Zhejiang University：Engineering Science
基金
内蒙古自治区科技重大专项资助项目(2019ZD015)
内蒙古自治区关键技术攻关计划资助项目(2019GG273).
关键词
路况预测
相关向量机(RVM)
组合核函数
模糊综合评价
SPARK
road condition prediction
relevance vector machine(RVM)
combined kernel function
fuzzy comprehensive evaluation
Spark
