Mobile LiDAR to Support Positive Train Control

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.

IMAG0166IMAG0168

The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).

imageimage

imageimage

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.

image

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.

image

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.

image

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.

image

One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!

image

What are you going to do with your NERC data?

So, you’ve collected your entire Transmission network using LiDAR, built your PLS-CADD models and identified your encroachments – what’s next?  How about leveraging that data to manage the Work Activities required to upgrade/maintain your Transmission network?

We have all heard about Asset Management and how it can help an agency extend the useful life of its infrastructure.  We all know that in principal it makes all the sense in the world, but the actual application of these concepts require investment in software, hardware and personnel.  What we will never know is – How much should we invest in the management of our assets?  Using the NERC regulation and the frenzied data collection going on in our industry as an example, consider the following.

Most Airborne LiDAR companies are collecting and delivering data in the $500 – $1,500 per linear mile range, depending on the downstream processing requirements.  Most of this data is delivered to the end user as .LAS point clouds, PLS-CADD .BAK, files and some other CAD or GIS formats.  Once it is delivered, the agency has a unique opportunity to leverage the delivered products for future value.

If we use Vegetation Encroachment data as an example, we can illustrate how the encroachment information can be used to create a vegetation Asset Class and managed throughout its life-cycle.  Most likely, the data delivered to an agency will include .LAS point clouds with classified data reflecting terrain, conductors, towers, buildings, etc.  In addition to this, vector data is also delivered and can be used to support maintenance management activities.  The graphic below illustrates a common Transmission LiDAR deliverable.

image

Note the Red vegetation in the graphic above.  It shows the vegetation points that have been flagged as encroachment violations based on its proximity to the conductors.  These points can then be mapped in a GIS or Asset Management program for further analysis.  In doing so, an agency can gather more value from this information.  For example, the graphic below illustrates the “grow-in” (light blue) and “fall-in” (red) violations for a section of Transmission line.

image

GIS mapping provides the user the spatial context necessary to make informed vegetation management decisions.  First, the location of vegetation encroachments are known and with a little manipulation, the volume and area of the vegetation can be determined very easily.  This gives an agency the ability to control the costs associated with their vegetation management program.  Asset management software that leverages GIS can provide the tools necessary to develop an immediate return-on-investment of the software purchase and associated data collection expenditures.

First, the user creates the geospatial layers from the classified point cloud.  Vegetation violations can be exported as points and then aggregated into vegetation encroachment units.  These units are then integrated with the Work and Asset management system through the use of GIS.  Since the geometry of the encroachment units are known based on its GIS attributes, an agency can then determine the following characteristics about their encroachments:

  1. Maximum Height of Encroachment Unit
  2. Average Height of Encroachment Unit
  3. Total Area (acres) of Encroachment Unit
  4. Total Area (acres) of Encroachment Units along a particular circuit

image

Since the agency knows so much about their encroachments, they can very accurately determine the volume of vegetation that needs to be removed.  The agency also knows other geospatial characteristics of the vegetation units and can then apply specific cost factors to the removal process.  In addition, GIS also provides a great way to provide contractors with maps and exhibits that will help them generate more accurate bids based on relevant information.  The graphic below shows a KMZ export of Vegetation Encroachments that can be provided to field units in charge of vegetation removal.

image

A typical vegetation removal contract is assigned to a forestry company who heads to the field and clears vegetation based on their perception of what needs to be removed.  Now, agencies can tell the forestry companies exactly how much (estimated) vegetation needs to be removed and WHERE it is.  Pretty amazing concept to embrace because now an agency can accurately predict the costs of their vegetation management program.

Another factor that can be applied to this information is the concept of Risk.  Risk takes into consideration the consequences of failure of a particular asset and then provides a Criticality Index for specific Asset Classes and Asset Types.  The more critical the Asset – the higher the priority it gets when determining an agency’s primary work focus.  In other words, this concept helps to identify the most critical components of your infrastructure and helps you to prioritize its maintenance over less critical assets.  By prioritizing using Risk, an agency can take measures to minimize the Risk that exists in its Asset portfolio by fixing these pieces and parts first.

image

None of this stops once you get to the Work Management piece of the puzzle.  I’ll be providing more information related to tracking the work activities as they are completed in the field and using this information to develop more accurate budget forecasts for the future.

Mobile LiDAR to Support Positive Train Control

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.

IMAG0166IMAG0168

The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).

imageimage

imageimage

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.

image

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.

image

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.

image

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.

image

One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!

image

Mobile LiDAR and Cross-Slope Analysis

DTS/EarthEye just completed a 9-mile mobile LiDAR scan of I-95 here in Florida and provided one of our partners with cross-slope information in a period of days.   The data was collected with our buddies at Riegl USA using their VMX-250 mobile LiDAR.  This information will be used to generate pavement resurfacing plans for the Florida Department of Transportation (FDOT).

This project shows the value that this type of project can provide to the end user on both sides of the fence.

First, the paving contractor can use this data to develop their 30% plans for submittal to FDOT when bidding on a resurfacing or re-design contract.  Having accurate and relevant data related to the roadway’s characteristics gives the paving contractor an edge over the competition because they know what the field conditions are before preparing an over-engineered design specification.  This happens all of the time because the detailed field conditions are unknown while they are preparing their plans and they only have historical information to work from.

On the other side of the fence resides the FDOT.  They can benefit from this information because if they can provide this detailed information as part of a bid package, they can reap the benefits that are gained from better information.  If all contractors have the detailed as-built information (or in this case, accurate cross-slopes), they can all prepare their submittals using the same base information.  This will provide the FDOT project manager with more accurate responses based on true field conditions, resulting in more aggressive pricing and decreased project costs.

Here are some screenshots of the information.

image

LiDAR Data Viewed by Intensity and Corresponding Cross-Slope Profile

Once the data has been collected and calibrated, we generate cross-slopes at a defined interval and export those out as 3D vectors.

image

These vectors are then symbolized based on their cross-slope percentages and exported as a KML file for ease of use.

image

Although this is a pretty simple step, the presentation of the data in Google Earth makes it easy for the end-user to visually identify problem areas and design the corrective actions according to field measurements.

image

Tunneling through the Trees

Just finished collecting a site for a project with some massive overhead trees.  This wreaks havoc on the GPS signal and validates the importance of having a good Inertial Measuring Unit (IMU) on board to carry the trajectory of the vehicle during the GPS outage.  This section of road is a virtual “tunnel” of trees which makes it difficult to nail the accuracy specification of 0.1-foot with GPS alone!



Elevation Data of Roadway Obscured by Trees

The next graphic shows the same area displayed by Intensity:

Intensity Image of Canopy Road

Here is a 3D Version of the Elevation point cloud:

3D Elevation Point Cloud

And here is an elevation profile of the same area:

Elevation Profile of 3D Point Cloud

Over the next couple of weeks, we will be creating 3D vectors from these point clouds that will be used to determine the geometric characteristics of the roads.  This information will then be used as parts of safety audits related to the following information:

  • Radius of Curvature
  • Horizontal Curve (Cross-Slope)
  • Vertical Curve (Grade)

This data will then be used with crash data to determine if a road needs to be re-aligned based on its geometric characteristics.  For example, a tight curve with a high speed may be contributing to crashes on a segment of road.  This information can be used to understand precisely how a road is constructed and functioning in the real world.

Airborne / Bathymetric Fusion

We just completed a project for a private landfill here in FL to help settle a contractor dispute about how much dirt was moved/removed from a retention pond.  The problem stemmed from the fact that the design engineer estimated the volume as one amount of cubic yards and the earthworks guys sent a bill for twice that amount!

Project Site

We thought it would be easy by collecting it with airborne LiDAR as part of our flight testing, but then realized that the area in question was a pond that was under water!  So, back to the drawing board…

Back in my RCID/Disney days, I worked with some smart people and we learned how to integrate GPS and Bathymetric sensors to map the Hydrilla in their lakes.  We also gathered some useful Bathymetric data that could be used to determine target concentrations of herbicides based on a specific dilution factor.  The most important part of that equation was knowing the amount of water in the lake and it was a math formula from there on forward.  Divide the volume by the target concentration level and you had the amount of herbicide needed to make the brew.

GPS Track of Bathymetric Data

So, we went old school and used our RTK rover to supply a GPS location and the Bathymetric sensor to grab the Z (depth) values for the lake in question.  The collection took about an hour and we had a processed and calibrated bathymetric surface before leaving the project site.  From there, we integrated the bathy data with the airborne LiDAR to get a continuous representation of the underwater surface.

Solid Rendering of Bathymetric Data with Airborne LiDAR

There was a small discrepancy between the water elevation on the day of airborne collection and the bathy collection.  This was handled by surveying the water elevation on the day of the bathy collection and then adjusting all of the depths to this elevation (corrected for the transducer offset which was about 0.1 foot).  This gave us the correct elevations relative to the airborne LiDAR data set.

Profile of Bathymetric Data Showing an "Empty" Lake

We determined that the volume of dirt removed was the same as the yield as determined by the design engineer.  It turns out that the contractor might have to come to the table to prove that they moved more material then the design engineer predicted and we confirmed with this cool project!