This article was originally written in 2011, but is being re-posted based on recent events…
DTS/Earth Eye just completed a positive train control (PTC) project for a national train company who was evaluating the differences between Airborne LiDAR and Mobile LiDAR to support the collection of PTC data. They are currently collecting airborne data for approximately 15,000 linear miles of rail. In certain areas, the airborne data does not provide enough fidelity to accurately map the rails or the asset infrastructure that support the railroad operations.
From Wikipedia – “The main concept in PTC (as defined for North American Class I freight railroads) is that the train receives information about its location and where it is allowed to safely travel, also known as movement authorities. Equipment on board the train then enforces this, preventing unsafe movement. PTC systems will work in either dark territory or signaled territory and often use GPS navigation to track train movements. The Federal Railroad Administration has listed among its goals, “To deploy the Nationwide Differential Global Positioning System (NDGPS) as a nationwide, uniform, and continuous positioning system, suitable for train control.”
The project involved the collection of Mobile LiDAR using the Riegl VMX-250 as well as forward-facing video to support PTC Asset Extraction. The system was mounted on a Hi-Rail vehicle and track access was coordinated through the master scheduler with the Railroad company. Once we had access to the tracks, we had one shot to make sure the data was collected accurately and we had complete coverage. All data was processed on-site to verify coverage and we had a preliminary solution by the end of the day that was checked against control to verify absolute accuracies. We collected the 10-mile section of rail in about 2 hours and this timing included a couple of track dismounts required to let some freight trains move on through.
The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).
Mapping the rails in 3D was accomplished by developing a software routine designed to track the top of the rail and minimize any “jumping” that can occur in the noise of the LiDAR data. Basically, a linear smoothing algorithm is applied to the rail breakline and once it is digitized the algorithm fits it to the top of the rail. The following graphic illustrates how this is accomplished – the white cross-hairs on the top of the rail correspond to the breakline location in 3D.
So, back to the discussion about Airborne PTC vs Mobile PTC data. Here is a signal tower collected by Airborne LiDAR. The level of detail needed to map and code the Asset feature is lacking, making it difficult to collect PTC information efficiently without supplemental information.
The next graphic shows the detail of the same Asset feature from the mobile LiDAR data. It is much easier to identify the Asset feature and Type from the point cloud. In addition to placing locations for the Asset feature, we also provided some attribute information that was augmented by the Right-of-Way camera imagery. By utilizing this data fusion technique, we can provide the rail company with an accurate and comprehensive PTC database.
This graphic shows how the assets are placed in 3D, preserving the geospatial nature of the data in 3D which is helpful when determining the hierarchy of Assets that share the same structure.
One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!