Imputation of Missing Traffic Data during Holiday Periods

Zhaobin Liu, Satish Sharma and Sandeep Datla

Transportation Planning and Technology, 2007, vol. 31, issue 5, 525-544

Abstract: Highway and transportation agencies implement large-scale traffic monitoring programs to fulfill the planning, operation and management needs of highway systems. These monitoring programs typically use inductive loops as detectors to collect traffic data. Because of the harsh environment in which they operate, they are highly prone to malfunctioning and providing erroneous or missing data. If this occurs during holiday periods when the increase in highway traffic is often substantial, there is a good chance that traffic peaking and variation will be underestimated. This paper discusses the adaptability of available imputation techniques for holiday traffic and then introduces a new procedure using non-parametric regression -- the k-nearest neighbor (k-NN) method. It is found that the performance of the k-NN method is consistent and reasonable for different holidays and types of highway. In addition, it is also concluded that the data requirements for this method are flexible.

Date: 2007
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03081060802364505 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:31:y:2007:i:5:p:525-544

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20

DOI: 10.1080/03081060802364505

Access Statistics for this article

Transportation Planning and Technology is currently edited by Dr. David Gillingwater

More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().