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The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.


Optimizing Traffic Counting Procedures
Bayat-Mokhtari, Faramarz.
Nicholas J. Garber
Year: 1986
VTRC No.: 86-R40
Abstract: Estimates of annual average daily traffic volumes are important in the planning and operations of state highway departments. These estimates are used in the planning of new construction and improvement of existing facilities, and, in some cases, in the allocation of maintenance funds. It is, therefore, important that any method used in obtaining the estimates provide data of sufficient accuracy for the intended use. This importance of having reliable and current data on traffic volumes at hand is generally recognized, and over the years data collection programs have tended to expand. This expansion has led to huge amounts of money being spent annually for the collection and analysis of traffic data. Efforts are, however, now being made to reduce the annual expenditure on traffic counts while at the same time maintaining the desired level of accuracy. A study was, therefore, carried out by the Council to develop an optimal counting program for the state. Firstly, the study entailed breaking down all highways in the primary system into homogeneous links such that the traffic characteristics along any given link were constant. A total of 2,510 links were obtained. The links in each district were then grouped into clusters, such that the links within a given cluster had similar traffic volume characteristics. The McQueen's K-means Method was used in the grouping procedure. A total of 82 clusters were obtained. A counting procedure was then developed based on an accuracy level of ± 10% with 95% confidence. Counting stations were then randomly selected. The counting program developed requires 927 counting stations for the whole state compared with the 1,345 currently being used.