While there’s no replacement for the collective human intelligence that manages our planet’s transportation systems, Linear Referencing Systems (LRS) provide a way to automate repetitive tasks that are required to efficiently maintain data about our road networks. Find out how LRS supports effective knowledge transfer.
At the Esri Canada User Conference in Fredericton, New Brunswick, I sat through a number of exciting presentations, which were part of our first Transportation Special Interest Group track.
The first that I found intriguing was aimed at bringing an understanding of the fundamentals of Linear Referencing Systems (LRS), which I wrote about in a previous blog post. While some of the audience members were engineers in charge of provincial highway networks and intimately familiar with LRS, others were just beginning to explore what LRS is all about. The delivery was exceptional given that the presenter only had 20 minutes to compress what’s usually a half-day course’s worth of knowledge. Those new to the concepts readily saw the value of adopting such methods and were eager to learn more.
Next up was a presenter from an organization with a relatively small road network that runs extremely efficiently without any robust LRS tools. They proved that it’s possible to implement a Linear Referencing System using a simple text file. As long as the owner of that text file understands LRS concepts with great detail and is flawless in the regular maintenance of that text file, it can be done.
Although they use LRS concepts in the daily management of their transportation highway network, there’s no easy way to manipulate the data, or expose the data to different people in the environment so that others can interact with it. It is essentially a closed “black box” that works exceptionally well for them.
Business units go about their regular workflow and provide data to the “human owner of the LRS” so that it can be imported. The owner can also export portions of the network based on a specific segmentation upon the request of other business units. In essence, the “human owner of the LRS” replaces the functionality that a robust LRS toolset provides by using their deep understanding of the system and ability to carefully craft various views of the data.
This got me thinking about the potential issues with succession planning or departmental growth. Although the knowledge that exists currently is undoubtedly thorough, might there be a risk associated with future knowledge transfer?
There is no replacement for the collective human intelligence that manages our planet’s transportation system. Indeed, we have to be able to innovate, transform and adapt our existing systems to future challenges, but many of the repetitive tasks that are required to maintain an LRS for a road network can certainly be automated.
One of the greatest benefits of having a robust LRS toolset to perform the iterative data management required for an LRS is that it’s a clearly defined process. It uses a systematic approach, which by its very definition, can be packaged and distributed to other systems or resources who don’t have the same skill level of current LRS experts. Thus, it makes succession planning less of an issue for LRS resources and enables growth by dramatically decreasing learning curves. The next manager of the road network may certainly do things in their own way, but that doesn’t mean they can’t build on the knowledge of the current manager. LRS makes this easy.
LRS supports effective knowledge transfer within an organization, especially for transportation systems.
Exposing the road network data to the rest of the department gives others the freedom to interact with the data in the way they want. Combining that with the shared knowledge on how LRS can enable interaction between these departments makes having a robust LRS toolset a very attractive option – even when the status quo is doing just fine.