[1]倪富健,方昱,薛智敏.时间序列在路面平整度预测中的应用[J].东南大学学报(自然科学版),2006,36(4):634-637.[doi:10.3969/j.issn.1001-0505.2006.04.030]
　Ni Fujian,Fang Yu,Xue Zhimin.Prediction of pavement roughness with time series autoregression model[J].Journal of Southeast University (Natural Science Edition),2006,36(4):634-637.[doi:10.3969/j.issn.1001-0505.2006.04.030]
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时间序列在路面平整度预测中的应用()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
36
期数:
2006年第4期
页码:
634-637
栏目:
交通运输工程
出版日期:
2006-07-20

文章信息/Info

Title:
Prediction of pavement roughness with time series autoregression model
作者:
倪富健1;  方昱2;  薛智敏3
1 东南大学交通学院, 南京 210096; 2 安徽省高速公路总公司, 合肥 230001; 3 福建省高速公路养护工程有限公司, 福州 350001
Author(s):
Ni Fujian1;  Fang Yu2;  Xue Zhimin3
1 College of Transportation, Southeast University, Nanjing 210096, China
2 Anhui Province Highway Corporation, Hefei 230001, China
3 Fujian Province Highway Corporation, Fuzhou 350001, China
关键词:
时间序列;  logistic回归;  多元回归;  IRI
Keywords:
time series;  logistic model;  regression model;  IRI
分类号:
U418.62
DOI:
10.3969/j.issn.1001-0505.2006.04.030
摘要:
为了解决国际平整度指数IRI预测模型准确性不高的问题,以京沪高速公路实测IRI数据为基础,对logistic回归、多元回归、时间序列这3种建模方法分别进行分析.并根据京沪高速公路平整度实测数据,建立了几个有不同数量滞后值的时间序列路面平整度预测模型,根据与实测值的比较,找出最优的时间序列路面平整度预测模型.分析结果表明:利用传统的logistic回归和多元回归方法难以建立准确预测路面平整度发展趋势的模型; 时间序列方法具有较高的预测精度,且其易修正性是其他预测方法所不具备的.
Abstract:
Prediction model of international roughness index(IRI)has a disadvantage of poor precision. Based on the IRI data of Jinghu freeway, three kinds of IRI prediction methods are analyzed in the paper: logistic regression method, multi-regression method, and time series method. With the IRI of Jinghu freeway, a time series prediction model of IRI with different number lag values is established, and by the comparison with actual IRI value, the best prediction model, time series prediction model of IRI is found. The result shows that: logistic regression model and multi-regression model can not work well to predict the trend of IRI; time series model of IRI can predict the trend of IRI very well, and its easiness of correction is unique.

参考文献/References:

[1] 张锋.公路工程中的平整度评价指标[J].公路与汽运,2004,13(1):23-26.
　　Zhang Feng.The index value is used in road roughness[J].Highways and Automotive Applications,2004,13(1):23-26.(in Chinese)
[2] 潘玉利.高速公路资产现代化管理技术的研究[J].公路交通科技,2005,4(3):23-26.
　 Pan Yuli.Modern technologies for expressway asset management[J].Journal of Highway and Transportation Research and Development,2005,4(3):23-26.(in Chinese)
[3] Al-Mansour A.Flexible pavement distress prediction model for the city of Riyadh[J].Emirates Journal for Engineering Research,2004,9(1):375-387.
[4] Fabrício J M.Prediction models of cracking,roughness and raveling developed in Brazil[C] // TRB2003.Washington,2003:227-301.
[5] 刘伯莹,姚祖康.沥青路面使用性能预测[J].中国公路学报,1991,3(2):34-54.
　 Liu Baiying,Yao Zukang.Use performance forecast of asphalt pavement on expressway[J]. China Journal of Highway and Transport,1991,3(2):34-54.(in Chinese)
[6] 王振龙.时间序列分析[M].北京:中国统计出版社,2000.
[7] Ahmed K,Abu-Lebdeh G.Prediction of pavement distress index with limited data on casual factors[C] //TRB2004. Washington,2004:454-616.
[8] 杨振成.自回归滑动平均模型中阶数及参数的确定[J].理论新探,2002,14(2):25-27.
　 Yang Zhengcheng.Parament and ladder used in glide average model[J].Statistics and Decision,2002,14(2):25-27.(in Chinese)
[9] Abu-Lebdeh G.Development of alternative pavement distress index models[R].Michigan State University,2002.
[10] 倪富健,屠伟新,黄卫.基于神经网络技术的路面性能预估模型[J].东南大学学报(自然科学版),2003,23(3):9-13.
　　Ni Fujian,Tu Weixin,Huang Wei.Pavement performance forecasting model by using neural network[J].Journal of Southeast University(Natural Science Edition),2003,23(3):9-13.(in Chinese)

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备注/Memo

备注/Memo:
作者简介: 倪富健(1968—),男,博士,教授,博士生导师,nifujian@jsmail.com.cn.
更新日期/Last Update: 2006-07-20