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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, located in eastern Iowa was chosen for development of the change detection procedure.

Traffic Monitoring
Traffic monitoring is undertaken to collect the volume, gross weight and classification of the vehicles in the road network. The data collected is utilized in different fields as shown in table 1.

In the absence of budgetary constraints, each road segment could be continuously monitored to determine the values of the AADT, vehicle mix by type and gross weight. "However, in practice, a few road segments are monitored continuously every day of the year to produce annual characteristics of traffic flow."[2]

Table 1: Examples of Studies That Use Traffic Characteristics Data (TMG, May 2001)

There are two types of traffic monitoring schemes; portable short duration counts and permanent continuous counts. The first step in traffic monitoring is to select the number and location of the count locations. The number of sites is decided through statistical theory for achieving the desired precision and count station locations are based on the transportation network characteristics.

After sampling, the next step is factor generation for the traffic counts. These adjustment factors are needed to extrapolate short duration traffic counts into estimates of AADT. And they are also required for the remaining roads to determine the AADT based on permanent and short duration traffic counts at the sample locations. These factors are necessary to generate representative values for each day of the week, month and for each road type. Validation is the last step in the data analysis process wherein the results after using the adjustment factors are compared with control data.

FHWA publishes the Traffic Monitoring Guide (TMG), which is the backbone of all the traffic monitoring initiatives in different states. The guide is a set of recommendations for implementation of portable short duration counts and permanent continuous counts. The TMG also provides specific recommendations on the number, extent, and duration of monitoring efforts.

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