| Although the highway traffic data collected by the Department of Transportation is a set of comprehensive recordings of highway travel activities, the inherent problems in the data have deterred tourism researchers from using the data up to their full potential for many tourism studies and planning purposes. In addition, using untreated data, which contain tourism and non-tourism traffic, in tourism reports can produce misleading results.; This exploratory study was launched to examine the characteristics of highway traffic data, to enhance connections between highway traffic data and tourism studies, and to demonstrate that valuable tourism information can be derived from underutilized highway traffic data. These goals were achieved by the development of a new data processing method called the Removal of Routine Traffic Method. The method is designed to mitigate the problem of separating tourism traffic from non-tourism traffic thereby facilitating greater and more meaningful use of highway traffic data in the field of tourism.; Based on the patterned behavior of highway travelers, a conceptual model was developed to link highway traffic data to tourism studies. Traveler behavior theory suggests that removing routine traffic from total traffic can improve data relevancy for tourism studies.; As a measurement of tourism traffic, the Removal of Routine Traffic Method provides face and construct validity in estimating tourism traffic. In a nutshell, the method functions as a filter screening out non-tourism traffic from total traffic and leaving the residual as an improved estimate of tourism traffic. Although the concept is relatively straightforward, it has been proved to be powerful. Using 1998 Michigan highway traffic data as an example, the method improved the overall data relevancy to tourism by 364%. Even simply performing the removal of truck traffic (i.e., non-recreational type vehicle traffic) can improve the overall data relevancy to tourism by 12%.; With the Removal of Routine Traffic Method, researchers not only can better understand the behavior of highway travelers and tourism traffic flows but also know how to utilize the extensive highway traffic data with the confidence that the estimates they derive are closer to their true values. Regional tourism planners or business operators can promptly estimate tourism traffic flow on a specific day or period of time if traffic counters are installed on vicinity highways. With only a small amount of initial investment in data storage and database programming, the removal of routine traffic operation can be highly automated. That is, the method is efficient and economical compared to other methods used in tourism studies. Hopefully, this study will encourage more researchers to use highway traffic data for regional tourism studies and planning, and to build upon this research to further improve tourism traffic volume estimates. |
