Jason Amadori.com
Jason Amadori's LIDAR GIS BlogArchive for Asset Management
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.
Pavement Management for Cities/Counties
I just spent a great week in Klamath Falls, OR training their staff on pavement management techniques with our Engineer, Will Cook. We deployed an asset management solution for their Pavement, Curb & Gutter, and Sign infrastructure. We were able to determine the amount of funding necessary to keep their pavement network at steady-state and how fast it is deteriorating at their current funding levels.
Believe it or not, most agencies have no idea what they own, what it is worth, and how much funding they really need to maintain it at a specific level-of-service.
Inventory
It all begins with a Network-Level inventory of everything which gives us an idea of what they own.
Condition Assessment
Then, we need to know what its condition is. We use this data to help prioritize assets for repair and rehabilitation.
Budgeting
Once the prioritization is complete, we apply budgeting scenarios to determine what gets fixed and when. This leads into Capital Improvement Plan (CIP) development which evolves into an agency’s work program.
This discussion makes it all sounds easy, but at this point, it is imperative to have the local subject-matter-experts make real-world decisions on what to really do. We’ll never rely completely on computer modeling to make these decisions, but they do help with a lot of the heavy lifting involved with managing large asset networks.
I will be posting a few articles next week detailing this process with some case studies of clients who I have worked with in the past…Stay tuned!
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.
