DOTs (Departments of Transportation) across the country are mandated by the federal government to keep track of their roadway assets and to report against these assets to receive federal funding for their maintenance and repair. Many DOTs conduct roadway characteristics inventories (RCI) on an annual basis to update and maintain their data relative to these assets. Traditionally, this has been completed using a boots-on-the-ground approach which has been very effective at building these inventories. Many DOTs are experimenting with other technologies, namely mobile LiDAR, to conduct these inventories and to achieve many other benefits from the 3D data captured in the process.
The graphic illustrates the typical technology solution utilized for these projects. It is composed of the point cloud produced by the Riegl VMX-450 LiDAR unit, coupled with high-definition right-of-way (ROW) imagery. This system can collect at rates up to 1.1 KHz (1,100,000 pts/sec) at a precision of 5mm. It collects points in a circular (360-degree) pattern along the right-of-way from 2 scanner heads facing forward and to the rear of the vehicle in a crossing pattern. The laser captures 3D points at a density of 0.3 foot at speeds up to 70mph. This scanner can be adjusted to scan at a rate that is applicable for the project specifications to limit the amount of data collected and to ensure that the resulting point cloud data is manageable.
Right-of-way imagery is also co-collected along with this LiDAR point cloud data. These images are used to identify appropriate attribution for each feature type being extracted from the point cloud. In this example, the DOT has digitized shoulder, driveway culvert ends, and drainage features (culverts, ditches and bottom of swale). Additional features such as signs, signals, striping, and markings will also be extracted and then reported to the FHWA on an annual basis.
The mobile LiDAR data provides a 3D environment from which to compile the data and then the ROW imagery can be used for contextual purposes to support attribution. This methodology provides an effective process that can be used to create 3D vector layers and accurate attribution used to build a robust enterprise GIS.
Both the ROW imagery and the mobile LiDAR can be used to collect and extract the RCI data for the DOTs, providing the DOT with a robust data set that can be leveraged into the future. The ROW imagery is typically used to map features at a mapping-grade level while the LiDAR can be used for a number of applications depending on the accuracy. The LiDAR can be used to conduct dimensional measurements related to clearances, sign panel sizes, lane widths, and other measurements that require a higher precision.
The DOT utilizes the derivative products from this RCI exercise to report to the FHWA in a way that is fairly basic, but effective to support the desired level of funding. The RCI data is extracted from the source data, maintaining a level of precision that is dictated by the accuracy of the data collection. The DOT takes this data and aggregates it to a higher level, reporting on such items as the total number of signs or the lineal feet of guardrail. Even though the reporting of this data is fairly basic, the source data can be used for other purposes including engineering design or asset management depending on the level of accuracy.
In conclusion, mobile LiDAR and Right-of-Way imagery are a safe and accurate way to collect and report against RCI variables for DOTs. This methodology promotes a safe working environment for both the DOT worker and the traveling public. It is also a cost-effective way to collect large amounts of 3D point cloud data which can be utilized for other purposes within the same agency.