基于相关向量机和模糊综合评价的路况预测模型

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

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%.