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Collecting Road Inventory using LIDAR surface models  Mr. Sitansu B Pattnaik*  Dr. Shauna Hallmark  Dr. Reginald Souleyrette Center for Transportation Research and Education, Iowa State University Mailing Address: ISU Research Park, 2901 S. Loop Drive, Suite 3100, Ames, Iowa 50010-8632 Tel: +1(515)-294-7188*, +1(515)-294-5249, +1(515)-294-0467, Fax: +1(515)-294-0467 Email: pattnaik@iastate.edu*, shallmar@iastate.edu and reg@iastate.edu
Introduction
Grade and cross-slope of the roadway influence the operational characteristics of vehicles. Heavy vehicles operation is mainly impacted by the roadway grade, which affects their stopping and passing sight distance. Emission characteristics are also influenced by the roadway grade. Cross-slope of the road segments influences the drainage across the road pavement and can affect vehicle movement along the roads. Presently available road inventory databases do not accurately measure the existing grade for it to be useful in analyzing the effect on the issues described above. The grade and cross-slope values are usually grouped into intervals for ease of storage and analysis. The grade and cross-slope of the roadway can change with the use of the facility mainly due to settling of the pavement and structural failures. The grade and cross-slope in a facility could also be different from the design specifications and hence to assess the functionality and the safety of a facility the current grade and cross-slope are important elements of analysis. Cross slope is often a compromise between the need for a relatively steep cross slope for drainage and a relatively flat cross slope for driver comfort. [1] In Iowa, two-lane highways usually have a cross slope of 2.0%. The outside lanes in multi-lane facilities usually have a cross slope of 3.0%. The cross-slope is varied to allow the water to drain across the pavement. The grade along the roadway is necessary to avoid stagnation of water hence a minimum grade of 0.4 % is required along road sections. Surface models created from Light Detection and Ranging (LIDAR) can be used to determine the artifacts defining the roadway condition which include the cross-slope, grade and surface roughness. This form of remote sensing can be crucial in rapid data collection and analysis for developing a timely maintenance and inventorying procedure. The objective of this research is to estimate the roadway characteristics from LIDAR data by developing regression models relating the elevation changes with the grade and the cross-slope of the road segment. Presently, the major concern is to correctly identify the points defining the pavement surface from the point cloud for accurate analysis. Use of LIDAR data Road inventorying is a difficult and prolonged process requiring on-site presence. The task of management of these widely spread networks becomes even more challenging due to the weather conditions, which make it difficult for on-site measurements. On-site surveys are time consuming and are a safety risk, as data collectors have to be very close to the vehicles using the facility. Surface models created from LIDAR data are useful in visualizing the entire study area and can potentially minimize ground survey for road inventorying. |