An effective data management strategy allows professionals to focus on their work so they can do the best job possible. It creates an environment where true collaboration across departments can happen.
Think about your daily commute into the office. If you drive, how many kilometres is your typical route? Take your daily round trip distance and multiply the number of kilometres by 75. Then ask yourself if you would be willing to pay that dollar figure every day to drive to and from work.
Typically, we drive our cars on public roads. Many Canadians believe that we indirectly pay for these roads through our taxes, and while partially true, this only covers a portion of the total cost to build and maintain our roads. According to Transport Canada, it costs nearly $20,000 per two lane kilometre every year just to maintain Canada’s roads. Few of us realize how much money is invested in our roads and highways so that the relatively mundane task of going out to get milk from the grocery store is safe, accessible and reliable.
The task of how that money is invested in maintaining our transportation infrastructure falls to a few, incredibly intelligent people, who not only have to evaluate the current maintenance spend, but also what the maintenance spend will be 20 years from now. After all, (most) roads last quite a long time. And even when those roads reach the end of their life, a plan must already be established for the construction of road that will take its place.
The information that is needed to help support these investment decisions comes from many different places: traffic counts, historic construction, planned road projects, future land use/zoning, emergency services, environmental sustainability plans, among others. Much of this information is stored in disparate data systems in various departments under the control of a wide variety of people with their own challenges. It is not easy, perhaps even inconceivable, and at the very least impractical to try and convince all these groups to share a data management system so that everyone can benefit from an organization-wide collaboration to better strategize our road investments.
However, that doesn’t mean that data-sharing and collaboration is impossible…
A concept utilized for decades by larger road authorities, such as Provincial Ministries of Transportation, may hold the answer to Canadian municipalities. The concept is known as Linear Referencing 1. It is indeed a technical concept that is often overlooked as a solution exclusively for highway agencies. However, it holds several advantages over the traditional segmented road network model that most Canadian municipalities utilize today that directly address the data need to support municipal road investment decisions.
In a segmented road model, roads are divided up into smaller chunks, each with data that describes that chunk. This data could include useful information like traffic counts, past construction projects, zoning information, address ranges, and more. Theoretically, all the data needed to make sound investment decisions could be packaged up into these road chunks or “segments”. In practice however, this rarely holds true.
The challenge stems from the fact that much of this data comes from different people who have vastly different business models. For instance, traffic counts are taken at specific locations and sometimes fit neatly into these predefined road segments and sometimes do not. Address ranges typically differ on either side of the street and often cover only a portion of the road segment. Speed limits may change in the middle of road segments. Paving projects often cover multiple road segments.
Finally, cities evolve – which means roads may change. As roads change, the segments that define those roads may also change. This presents a highly complex challenge for what is known as “temporal data management” or in other words, managing the change of data over time. And since the people who manage these different datasets have different segmentation standards and business rules, they may feel inclined to manage their own version of road segments because they know how to best represent their data. When this happens, suddenly it becomes extremely difficult to match different pieces of data through time across departments, so we can make road investment decisions.
By employing the Linear Referencing concept, we do away with traditional road segments, and instead use a technique known as Dynamic Segmentation 2. This is where we allow the data to define the segments, rather than predefining the segments and stuffing data into it. The result is that querying the road network is as easy as picking two arbitrary points on the road, say an intersection or a rail crossing, or a water crossing, or even an offset to a specific bridge pier, and asking the data management system to return all the useful data such as traffic counts, constructions projects, zoning, etc., between those two points.
Figure 1: Dynamic Segmentation of two linear events: Speed Limit combined with Functional Classc
Taking this one step further, we could ask the system to provide the data for an entire corridor, rather than two arbitrary points. Or perhaps we are interested in all the data along a specific bus route.
Using this technique, we allow each of the contributors (all the disparate departments) to continue using the segmentation that serves them best, while enabling an environment where consumers can extract data using whatever segmentation that suits them best. It’s the best of both worlds.
Figure 2: A Linear Referenced road network showing Surface Condition, Rail Crossings and Traffic Counts
In the traditional sense of collaboration, an environment is created where all the contributors to the information set their expectations in a way that accommodates each other, make certain compromises to the way they manage their data and share a common digital workspace. These individual sacrifices, in theory, are of less consequence than the greater benefit to all. That is why all contributors agree to collaborate.
By leveraging Linear Referencing technology within a robust data management platform that can accommodate multiple location referencing methods 3, such as Esri Roads and Highways, the contributors benefit from a more efficient methodology for collaboration using commercial off-the-shelf (COTS) technology where one can have their proverbial cake and eat it too.
Ultimately, our objective is to leverage a data management technology that enables people who make road investment decisions to focus on their work, so they can do a better job. After all, we all want our roads to be smooth, safe and last a very long time. Let’s not force our governments to wrestle with data when they can be far more effective at what they do best; spend our $20,000 per km in the best way possible so we don’t have to think about the road when we go and buy milk.
Provincial Ministries of Transportation across Canada have known about this concept and have been using it for decades. Isn’t it about time you considered using it for your municipality?
Register for our upcoming webinar to learn how to collaborate better and build data integration across departments.
3 Adams, Teresa & Koncz, N & Vonderohe, J. (2000). Functional requirements for a comprehensive transportation location referencing system.