摘要
为公交规划科学合理的进行,建立公交客运量时间序列预测模型.通过对公交客运量影响因素进行分析,选取市区人口数、从业人员数、在校学生数、工业生产总值、职工年平均工资、公交车辆数、运营线路数等7个指标自变量,利用指数平滑法对自变量进行预测;在对自变量进行相关分析及因子分析的基础上,建立prais-winsten AR(1)自回归时间序列模型对公交客运总量进行预测;采用ARMA模型对哈尔滨市各分区公交客流量进行了拟合和预测.结果表明:所建立的时间序列预测模型预测效果良好,验证了模型的有效性和准确性.
To make the traffic plan reasonably, this paper established the passenger traffic volume time series forecasting model. Through the analysis of the affecting factors of passenger traffic volume, selecting the number of urban population, the number of employees, the number of students in the school, gross industrial output value, the average wage of workers, and bus number and operation line number as index variables, this study predicted the argument by the exponential smoothing method. On the basis of the correlation analysis, this paper established the prais-winsten AR（1） auteregressive time series models to forecast transit passenger traffic volume, fitted and predicted the bus passenger flow of each district of Harbin city by using the ARMA model. By observing the results of fitting and predicting, the results shows that the time prediction model that established works well, and the validity and accuracy of the model is verified.
出处
《辽宁工程技术大学学报（自然科学版）》
CAS
北大核心
2014年第12期1715-1720,共6页
Journal of Liaoning Technical University (Natural Science)
基金
黑龙江省交通运输厅重点科技资助项目(2011TZD037)
关键词
公共交通
公交客运量
时间序列
预测
指数平滑法
准确性
public transit
passenger traffic volume
time series
forecast
exponential smoothing method
Accuracy-
作者简介
徐文远（1969-），男，内蒙古根河人，博士，副教授，主要从事道路工程及道路交通环境等方面的研究．
