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Jason Amadori's LIDAR GIS BlogArchive for budget forecasting
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.
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.
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.
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.
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.
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.
Sign Retroreflectivity Compliance and Asset Management
Over the past few years, there have been many projects designed to determine an agency’s sign retroreflectivity compliance across their road network. Each project has been unique in terms of how the agency collected the data and how they ultimately managed the data into the future. Recent MUTCD regulations require the development of an inventory management program that documents the installation, maintenance and construction characteristics of sign infrastructure. Many agencies are faced with the daunting task of funding a replacement program that will comply with these new regulations into the future. Ultimately, the replacement plan needs to address non-compliance issues that are identified during the inventory/inspection process.
Step 1 – Sign Inventory
The first step in the compliance process begins with an accurate inventory. Signs can be collected utilizing many different techniques and each technique can have its pluses and minuses. Field collection programs can involve inspectors walking the roads, mobile imaging vehicles taking pictures of the roads as well as other collection techniques designed to identify compliance issues along the road. No matter which solution is selected, it needs to satisfy the overall goals and objectives of the project while providing an accurate inventory of the agency’s sign infrastructure.
Next, an agency needs to be able to match their available funding to the technology solution that achieves their project goals and objectives. It also needs to understand the trade-offs that are the necessary evil in projects like this – available funding typically dictates the quality of the solution that can be provided by the service provider. Furthermore, the quality of the data collected and its usefulness can be impacted by the choice of the solution and available funding.
Remember that the ultimate goal of retroreflectivity compliance is centered on the replacement of signs once they fall below the minimum reflectivity standard as defined by FHWA. Many agencies would rather start replacing signs today instead of spending money to create their inventory and a management plan. This makes sense economically in the short-term, but can introduce problems from a long-term management perspective.
Step 2 – Estimating the Replacement Cost of the Sign Network
The next graphic illustrates the total replacement cost as calculated using the FHWA “Sign Retroreflectivity Guidebook” for an agency with a 4,383 centerline mile road network.
The cost to replace all signs for this agency approaches $17.5 million dollars. Please note that this does not include the cost of the labor, equipment and other material costs incurred for the actual installation of these signs. The inventory of signs for this agency cost approximately $800k or roughly 5% of the total replacement cost for these signs. Although significant, this investment is crucial to ensure the longevity of the Sign Management program designed to manage these assets throughout their life-cycle.
Step 3 – Choosing a FHWA-Approved Sign Management Methodology
The chart below illustrates the advantages and disadvantages related to a few of the FHWA-recommended methodologies. Most of these methods have been implemented in one way or another at various agencies across the Country.
The “Measured Retroreflectivity” method is popular at many DOTs and Toll Authorities. I believe this is the case because these agencies typically manage facilities that carry higher volumes of traffic that operate at higher speeds, thus increasing the risk and potential consequences of an accident. Many County and City agencies are utilizing the “Visual Nighttime Inspection, Expected Life, Control Sign, or Blanket Replacement” methods to manage their sign infrastructure. Each mentioned method is used for different reasons (financial vs. headcount) and has a lot to do with legacy management techniques (“We’ve always done it this way”).
There really isn’t a management method that can be considered “The Best” or “The Most Cost-Effective”. It is solely dependent upon an agency’s goals and objectives for the management of their sign infrastructure. I typically recommend conducting an inventory first and then implementing a management plan that uses the concepts of Condition, Risk, and Valuation to help prioritize which signs should be replaced along with the best timing for the replacement. This can prove very valuable since the highest risk signs can be replaced first and the least risky signs can be programmed for replacement as funding becomes available.
Finally, I also recommend that agencies utilize asset management software to manage the work performed on their sign infrastructure so that all replacements can then be managed according to their useful life and actual condition rating. This information can then be used in concert with one another to help develop a capital improvement plan that details the planned fiscal expenditures for the next 10 years, which is the typical life-cycle of a sign.
Automated Pavement Distress Analysis – The Final Frontier?
We have been working with some automated methods for quantifying crack measurements and have had some interesting results. How great would it be to collect pavement images, batch them on a server and have it spit out accurate crack maps that you can overlay in a GIS? The technology is here! Or, is it?
Most pavement inspections involve intricate processes where pavement experts rate segments visually, either from field visits or rating pavement images in the office. This introduces a lot of subjectivity in the rating results and typically culminates in a spreadsheet showing pavement ratings by segment. The data is then modeled using ASTM performance curves that have been built from industry proven pavement experiments.
There is no doubt that these curves are tried and true representations of how pavement performs in varying physical and environmental conditions and each project should take these factors into consideration when developing the preservation plans for an agency.
We have been working to develop a rating workflow that focuses on a combination of automated and manual processes to bridge the current gap of Quantitative and Qualitative pavement inspections. The way we are doing this is through the application of GIS to the automated rating process. Here’s how it works…
First, we begin with a pavement image from our LRIS pavement imaging system. Images are captured at a 1mm-pixel resolution and then analyzed through an automated image processing workflow.
The resulting image creates a “crack map” that identifies the type, severity and extent of the distresses on that section of pavement. The process is fully automated and handled by the computer.
Once we have the crack maps in place, we then apply a manual editing process that is GIS-centric by nature and the resulting crack map is a more accurate representation of the real-world conditions.
Once the edited crack maps are compiled, the data is exported to a GIS where the extents are calculated geospatially and then integrated with a pavement management system. This is where all of the Pavement Condition Indices (PCI) are calculated and applied to each agency’s specific pavement rating methodologies. Since the process is geospatial in nature, it is easily imported to ANY pavement management software and gives our clients the flexibility to apply any rating methodology they desire.
Of course, all agencies have a certain spending threshold and there are cases where automation is the only way to cost-effectively manage large volumes of data. We recognize this fact and are working hard to bridge the gap of available funding and high quality data.
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.
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.
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:
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Maximum Height of Encroachment Unit
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Average Height of Encroachment Unit
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Total Area (acres) of Encroachment Unit
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Total Area (acres) of Encroachment Units along a particular circuit
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.
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.
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.
Risk Analysis for Asset Management
One of the pet-peeves I have with asset management software is that they are basically Access on steroids. There’s not much to the inner workings of the software other than showing you information about an asset – its location, street name, type and maybe some historical information. Once you get past the attributes, the applications get very complicated because they need to handle some business logic and one-to-many relationships, etc. The list of attributes can be exhaustive, but how much of it is useful when making a decision about what to do to an asset with limited funding.
I’m working with a vendor who understands this relationship and the role it plays in prioritizing asset rehabilitation and we are getting some promising results. Imagine trying to prioritize which roads you will be resurfacing next year based on their condition alone. The worse condition they are in, the higher priority they will receive when you are ranking them based on condition alone.
What if you added risk analysis into the mix?
Then, you can ask these questions of your data – If this road fails, what kind of impact will it have on the travelling public? Does the road carry large volumes of traffic, or is it in the boonies?
If you can answer these questions, you add another level of intelligence to your data. Start incorporating traffic volumes, functional classification, detour and access constraints into your model and you’re on your way to prioritizing with intelligence.
