Efficient Local AADT Estimation via SCAD Variable Selection Based on RegressionModels
In transportation networks, Annual Average Daily Traffic (AADT) estimation is very important to decision making, planning, air quality analysis, etc. Regression method may be the most popular one for estimating AADT on non-counters roads. Most literatures focus on how to collect different groups of predicting variables, and select significant variables by t-test and F-test. However, there is no theory on the validity of these multiple selecting steps. Furthermore, variables they collected for high functional class roads may be not suitable for the estimation of local AADT because of lacking counters. This paper focuses on the estimation and variable selection for the local AADT using different groups of variables. The variable selection by smoothly clipped absolute deviation penalty (SCAD) procedure is proposed. It can select significant variables and estimate unknown regression coefficients simultaneously at one step. The estimation algorithm and the tuning parameters selection are presented. The used data is from Mecklenburg County of North Carolina in 2007 for demonstration. The proposed method shows that our selection procedure is valid and it further improves the local AADT estimation by incorporating satellite information. The proposed method outperforms some other regression method when it is applied to local AADT estimation.
AADT Local traffic estimation Regression Satellite information Deviation penalty
Bingduo Yang Sheng-Guo Wang Yuanlu Bao
Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, Xiamen 361005, China; and t Lee College of Engineering, University of North Carolina at Charlotte (UNCC), Charlotte, NC 28223-00 Dept. of Automation, University of Science & Technology of China, Hefei, Anhui 230026, China
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
1898-1902
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）