计算机应用研究2018，Vol.35Issue(2)：405-409,416,6.DOI:10.3969/j.issn.1001-3695.2018.02.019
基于Spark的分布式交通流数据预测系统
Distributed traffic flow data prediction system based on Spark
摘要
Abstract
In the era of big data and complex urban traffic environment,real-time and accurate traffic flow forecast is a prerequisite to implementing intelligent transportation system.This paper presented a distributed urban traffic flow forecasting model which based on gradient optimization decision tree on Spark platform.It also proposed the optimization method of gradient optimization decision tree model in distributed case,which included sampling points,feature packing and layer-by-layer training.All of them could improve the training efficiency of gradient optimization decision tree in distributed case.The characteristics of time,road condition and weather were established based on the advantages of efficient,reliable and flexible expansibility of Spark distributed computing platform and the advantages of high accuracy and time complexity of gradient optimization decision tree model.The DUTP-GBDT model implemented real-time and accurate traffic flow prediction.Compared with GA-BP,GA-KNN and MSTAR models,the results prove that,the accuracy and training speed of DUTP-GBDT model using Spark platform in the distributed environment are both improved.In line with the requirements of urban traffic flow forecasting system.关键词
交通流预测/分布式计算/Spark平台/梯度优化决策树模型Key words
traffic flow forecast/distributed computing/Spark platform/gradient optimization decision tree model分类
信息技术与安全科学引用本文复制引用
黄廷辉,王玉良,汪振,崔更申..基于Spark的分布式交通流数据预测系统[J].计算机应用研究,2018,35(2):405-409,416,6.基金项目
赛尔网络下一代互联网技术创新项目(NGII20160306) （NGII20160306）
广西科技攻关项目(PD160189) （PD160189）
