Introduction
Travel patterns of a transportation system depend on driver characteristics, local/regional development and the available transportation network. These patterns are studied through the implementation of traffic monitoring programs, which measure traffic volume and characteristics. In the U.S., Traffic counts are generally classified based vehicle type and gross weight. Accurate counts are very important to transportation planning activities. Garder, 1999, says "with the movement towards design-build highway projects and warranties on performance, accurate measurement of vehicular movement is required to ascertain if the roadway has met or exceeded the design requirements". [1]
The Federal Highway Administration (FHWA) mandates the states to collect traffic count information at specified intervals to meet the needs of the Highway Performance Monitoring System (HPMS). Each state may have its own method of implementation while satisfying the minimum federal requirements. In the state of Iowa, approximately 10,000 mechanical counts and 1,000 manual counts are collected every year.
The scope of traffic monitoring as discussed in this paper deals with the procedures for appropriate sample selection for efficient and timely monitoring. This paper proposes using multi-temporal spatial datasets as input into change detection procedures for improving design and efficiency of traffic monitoring programs. Results could enable redirecting traffic count activities, and related data management resources, to areas that are experiencing the greatest changes in land use and related traffic volume. Conversely, areas where traffic counts are static or changes are statistically insignificant over time could be counted less frequently, while other, less costly methods might be employed to generate the data needed for these locations. Due to recent study and data availability, the city of Maquoketa, locat
