TY  - JOUR
PY  - 2008//
TI  - Imputation of Missing Traffic Data during Holiday Periods
JO  - Transportation planning and technology
A1  - Liu, Zhaobin
A1  - Sharma, Satish
A1  - Datla, Sandeep
SP  - 525
EP  - 544
VL  - 31
IS  - 5
N2  - 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.<p /> 
LA  - en
SN  - 0308-1060
UR  - http://dx.doi.org/10.1080/03081060802364505
ID  - ref1
ER  -