基于Pearson相关性分析的交通枢纽站客运量预测模型分析研究  

Research on Passenger Volume Prediction Model of Transportation Hub Station Based on Pearson Correlation Analysis

作　　者：张露康 王海蓉 古可言 刘春红 ZHANG Lu-kang;WANG Hai-rong;GU Ke-yan;LIU Chun-hong(School of Transportation,Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学交通运输学院,重庆400074

出　　处：《黑龙江交通科技》2023年第3期137-139,共3页Communications Science and Technology Heilongjiang

摘　　要：2020年初,新冠疫情爆发,居民日常出行量大幅降低,未来客运量难以预测。客运量作为道路交通规划、设计、建设的重要指标,预测结果将直接影响城市道路运营效率和经济效益。为精准预测后疫情时代客运量未来发展趋势,研究以重庆四公里交通枢纽站交通客运量为预测对象,运用多种定量预测方法分别预测未来客运量,利用Pearson相关性分析,找到了适用于未来客运量预测较优的预测模型。At the beginning of 2020,due to the outbreak of COVID-19,the daily travel volume of residents has significantly decreased,making it difficult to predict the future passenger volume.As an important index of road traffic planning,design and construction,the forecast results will directly affect the operation efficiency and economic benefits of urban roads.In order to accurately predict the future development trend of passenger volume in the post-epidemic era,this study takes the passenger volume of Chongqing four-kilometer transportation hub as the forecast object,and uses a variety of quantitative prediction methods to predict the future passenger volume respectively.By using Pearson correlation analysis,a better prediction model for future passenger volume prediction is found.

关 键 词：客运量预测 指数趋势外推法 定量预测方法 指数曲线趋势外推法 

分 类 号：U492[交通运输工程—交通运输规划与管理]