Railway Traffic Volume Prediction Method Based on Hadoop Big Data Platform

Abstract

AbstractIn order to improve the accuracy and efficiency of railway traffic volume prediction, a railway traffic volume prediction method based on Hadoop big data platform is proposed. Firstly, the traffic big data preprocessing mainly includes three parts: redundant data processing, numerical abnormal data processing and missing data processing. Then the spatial cross-correlation characteristics of traffic flow are calculated. Finally, a combined prediction model based on multi features and multifractals is established to realize the railway traffic volume prediction based on Hadoop big data platform. The experimental results show that the prediction method in this study has high prediction accuracy, reduces the prediction time, and meets the needs of method design.KeywordsHadoop big data platformRailway transportationTransportation volume forecastRedundant dataThreshold methodRelevance