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Data Description:
LIDAR data and 12- inch resolution orthophotos were collected for the Iowa highway 1 corridor in October 2001. In addition to the LIDAR dataset and 12-inch orthophotos, a set of 6-inch resolution orthophotos was available with the DOT. A commercial vendor for the project collected data. The density of LIDAR points in the dataset was 1 every 27 square feet. The vendor also provided the gridded DEM of the area with 5 feet postings.

A GIS street database was also available from the Office of Transportation Data, Division of Planning and Programming at the Iowa DOT. GIMS dataset contains roadway characteristics for all public roadways in the state of Iowa, such as lane width, grade, traffic volume, surface and shoulder type (CTRE final report, May 2001).

Methodology:
Ten test segments were selected along the Iowa 1 corridor as shown in figure 1. Seven straight segments were selected which avoided horizontal or vertical curves. Two segments were chosen along locations with a vertical and horizontal curve and one segment was chosen along a vertical curve. Figure 2 shows the location of the road segments selected for the analysis.


Figure 1: Location of the test segments along Iowa Highway 1
Least squares regression was used to calculate grade and cross-slope. Using regression analysis, a plane was fit to the dataset in question. It was theorized that grade and cross-slope could be determined by fitting a plane to LIDAR data from roadway sections with constant grade and cross-slope (lane-groups). As a result, each 2-lane roadway segment was defined by two planes delineated by the center of the roadway crown and the edge of pavement. Shoulder sections were evaluated separately, since cross slopes are frequently different than the roadway cross slope. Consequently four sections were analyzed on each roadway segment.

The LIDAR data consisted of a randomly spaced point cloud with average point density of 1 point per 27 square feet. In order to satisfy the minimum number of LIDAR points for assuming a normally distributed dataset for least square regression analysis, each section was 120 feet in length. This length was determined by considering the average density of LIDAR points throughout the corridor and the lane width allowing adequate number of points to be used for regression analysis.

Length = 30 / (Minimum width * density)

The number 30 is the minimum data points required for assuming normal distribution. (Rule of thumb) Longer sections would be preferred as the number of points for regression analysis will increase, but the increased length of the segment may be unsuitable as only monotonously increasing or decreasing sections can be estimated by using linear regression.

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