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.

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

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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.

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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.

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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.

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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.

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

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Roadway Characteristics Inventory for DOTs Using Mobile LiDAR Technology

Roadway Characteristics Inventory for DOTs Using Mobile LiDAR Technology

DOTs 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 next graphic illustrates the typical technology solution utilized for these projects. It is composed of 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.

Mobile_LiDAR_DOTs_RCI

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 Feds on an annual basis. The mobile LiDAR data provides a 3D surface 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 efficiently for the DOTs and provides 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 vary a bit in accuracy. Since the relative accuracy inherent in the LiDAR is very precise, it is 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 Feds in a way that is pretty basic, but effective to achieve their level of funding. For example, the data capture is very technical in nature and focuses on high precision and accuracy. Then, the RCI data is extracted from this source data, maintaining a level of precision that is dictated by the source data. Then, the DOT takes this precise data and aggregates it up to a higher level and reports the total number of Signs or the lineal feet of guardrail. Even though the reporting of this data is pretty basic in nature, the origins of the data can still have precision and accuracy and can be used for other purposes related to Engineering Design or Asset Management.

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.

Utilizing Mobile LiDAR to Support Pavement Resurfacing

Many Departments of Transportation are looking for ways to save money while increasing safety on the roads. In order to do this, they are seeking out innovative ways to do this while utilizing new technology. Mobile LiDAR is being used to determine roadway geometry information for long stretches of roadways that are candidates for resurfacing. The typical DOT procurement process involves the selection of a resurfacing vendor through a competitive bid solicitation and then the selection of the most qualified and “cost-effective” bidder. As budgets have become leaner, the competition for these projects has increased and thus, drives the innovation curve to find the most cost-effective solution for the DOT.

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To achieve this goal, pavement vendors have sometimes turned to the use of LiDAR information to develop their bid packages for the DOT. Historically, vendors would use the as-built information that was available from the DOT which might be inaccurate, old or obsolete. This obviously leads to issues with the information that the pavement vendor uses to develop their bid packages. They are most interested in determining the correct amount of cut/fill needed to resurface the road while using the least amount of new material. One of the most important pieces of this puzzle relates to the cross-slop of the road which facilitates roadway drainage and ultimately makes a road safer for the traveling public.

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Mobile LiDAR provides a high-precision, digital terrain model of the roadway surface that can be used to generate very accurate cross-slope measurements at specific intervals. For example, the road surface is continuous for the entire length of the project. Cross-Slopes can be generated for each travel lane as well as for the shoulders. The extracted cross-slope is then compared to the design specification and colored based on whether it is in compliance or out of compliance.

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Once the areas have been identified that are out of compliance, it is easy for the pavement vendor to target those for the re-design effort. Instead of applying an average value across the entire section of road, specific areas can be identified and re-designed so that the pavement vendor can save the DOT money on materials. The ultimate benefit for both the pavement vendor and the DOT lies in the fact that everyone benefits – Pavement vendors can design roads more accurately and limit their risk of material over-runs while the DOT can select the most cost-effective vendor and have more budget available to pave their ever-increasing network mileage of roads.

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Since mobile LiDAR data is very cumbersome to manage (2Gb/mile) it is important to deliver the data in a format that is usable by the client. Sometimes raw LAS files work and sometimes the client can only deal with vector files that will be used in GIS, Autocad or Microstation, to name a few. We have found that KMZ files are useful as a delivery mechanism because they can be easily loaded and viewed by the client in very short order. Any derivative of these delivery mechanisms will work – it just depends on the expertise of the client and their computing environment.

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Future discussions will focus on the DOTs and their collection of mobile LiDAR data so that they can provide it to all of the pavement vendors and receive the most cost-effective bid packages. Although there is an up-front cost associated with the LiDAR collection, it is believed that the downstream cost savings for both the DOT and the pavement vendor will more than outweigh the up-front cost of collecting the mobile LiDAR data.

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

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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.

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

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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.

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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.

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

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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.

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Getting Ready for Some Rail Data

We are going to have some rail data collected here soon and I’ll post the results as soon as I get them.  We’ll be collecting a 17-mile section of track in the DC area, so it will be full of urban canyons, tunnels and highway overpasses.  At least we’ll know how well the equipment will be performing in the field because this will be a true field test of the equipment.

We just finished getting e-Rail certified to work with the railroad, so now it is time to provide some value to this market.  They are looking to collect features that will support Positive Train Control as well as giving them engineering-grade information that can be used for maintenance & rehabilitation efforts.

Rail Intensity

Our goal will be to collect all of this information in one pass and give them tools to automate the extraction of other features such as the rail, ballast, gauge, etc.  Data collection will happen next week and we’ll have some results to share at that point.

Rail LiDAR with Elevation

Rail Data Themed by Elevation

Rail Data with Intensity

Mobile LiDAR to Support Roadway Resurfacing

We just completed a mobile LiDAR project that was designed to support a roadway resurfacing project in Orlando.  The project was centered on the use of mobile LiDAR to generate roadway profile data that Engineers could use to design a resurfacing project.  Obviously the data would need to be accurate and we were able to hit the mark and best of all – prove it!

Overview of SR417 Project

We collected the data using the new Riegl VMX-250 mobile LiDAR unit using a single pass in the north and southbound directions.  We only required one pass in each direction to collect the road data which makes it very efficient from the data collection standpoint.  In the past, we had to collect “strips” of data and then “sew” them all together during the calibration process.  In this case, we took the opposing (NB and SB) strips and calibrated them relative to one another and then they are brought down to control as a final step.

LiDAR Coverage by Flight Line

Most of our clients are interested in the overall accuracies of the data, so we have built accuracy assessment tools that make it easy to review the LiDAR against survey control.  The tool is simple to use and allows us to sort the results and dig deeper into the least accurate points to see why there might be discrepancies in the control vs the TIN surface.

417 Accuracy Control Report

For this project, we achieved an RMSE of .0525 ft – calculated by comparing the control elevations (Z) against the TIN elevations (Z TIN).  This is important because we can check the point cloud against known control that was collected throughout the project and provide detailed information about the accuracy of the data.

Once the data has been calibrated sufficiently, we can then generate all of the derivative products for this project.  We generated the following data for our roadway engineers:

  • Pavement Cross-Slope
  • Shoulder Cross-Slope
  • 3D Roadway Markings
  • Edge of Friction Course

3D Vector Data

This data set also supports detailed engineering analysis related to guardrail height above the roadway.  This is an important factor to consider because there are specific standards that define where the guardrail is placed, more specifically, its height above the roadway, to corral vehicles that end up impacting the guardrail in an accident situation.  The following graphic displays how this measurement can be made in the point cloud data.

Guardrail Height Measurements