基于Spark的多阶空间权重矩阵STARIMA交通流预测分析方法

Spark-based traffic flow prediction analysis using multi-order spatial weighting matrix STARIMA

为了缓解城市拥堵,建立可以预测交通流量的智能交通管理平台。在Spark框架基础上利用孤立点检测算法对实时海量增长的交通流数据进行清洗统计,设计负载均衡规则对数据进行并行注册与存储,通过语义解析和逻辑优化实现分布式语义查询,并利用基于多阶空间权重矩阵STARIMA模型完成交通流预测。通过对比实验证明:(1)交通流数据清洗、统计、注册和存储方法,有效利用了Spark框架的内存计算和迭代计算优势,在大数据环境下,此方法比基于MPI或MapReduce的方法耗时减少60%左右,可以在预测周期内完成数据预处理工作;(2)语义查询方法将所需数据提供给交通流预测模型,模型中的多阶空间权重矩阵可以更加准确的体现交通流多阶分配规律,与动态STARIMA模型相比预测分析的准确度可提高25%左右,可以为交通诱导信息发布提供参考依据。

In order to alleviate urban congestion,it is necessary to establish an intelligent traffic management platform that can predict traffic flow.In this paper,the outlier detection algorithm based on the Spark is used to clean the real-time massive traffic flow data.Load balancing rules are designed for the parallel data registration and storage.Semantic parsing and logical optimization are used to realize distributed semantic queries.And the STARIMA model based on multi-order spatial weight matrix is designed to realize the traffic flow forecasting.By the comparison experiments,it is proved that:①The traffic flow data cleaning,statistics,registration and storage methods effectively utilize the advantages of memory computing and iterative computing of the Spark framework.In the big data environment,this method reduces the time consumption by about 60%compared with the MPI method or MapReduce method.And it can complete the data preprocessing in one prediction cycle.②The semantic query method provides data to the traffic flow prediction model.The multi-order spatial weight matrix of the model can reflect the multi-order traffic flow assignment law more accurately.Compared with the dynamic STARIMA model,the accuracy of prediction analysis can be increased by about 25%.And the method can provide reference for traffic guidance information publication.