Vehicles can be identified in high-resolution satellite imagery that recently has become available to the civilian community. The vehicle information contained in this imagery, and in air-based imagery, could be used in annual average daily traffic (AADT) estimation, a task conducted by many transportation agencies around the world. However, because the imagery provides information equivalent to traffic counts of very short duration, it is possible that the information is too noisy to be of use. Empirical differences between AADT estimated from 14 satellite images and air photos of Interstate segments in Ohio and the corresponding AADT estimated from traditional, ground-based estimates are presented. The distribution in differences appears relatively unbiased, implying that averaging the estimates of several images of the same segment can decrease estimate errors. The empirical errors are small enough to indicate that AADT estimation errors and ground-based sampling efforts could both be reduced by combining satellite-based data with traditional ground-based data. The differences between the image-based and the ground-based estimates are smaller in the few cases in which ground-based estimates inspired greater confidence, implying that the image-based estimates may be better than what is indicated in the distribution of differences. Evidence also suggests that the differences tend to decrease when the image leads to longer equivalent traffic count duration, indicating the potential to condition the use of the image-based data on this readily available parameter.
Abstract