Lessons Learned Reviewing Road Data For Asset Management.

Though we will focus on road data, the principle holds true for all other asset categories.

We are all familiar with the fairy tale of the  children’s story of the three little pigs. One little pig built his house on straw, the other twigs and the third out of bricks. The wolf came by and destroyed the first two houses while the third one made of brick remained.  The moral of this story, is that a solid foundation is instrumental for success and longevity.

Having spent the past 5 years collaborating with educational institutes, municipal associations and municipalities, it has become evident that municipalities struggle with the concept of quality, quantity and accuracy with respect to tangible asset data.

All this in mind, whether you maintain your data in ancient cuneiform tablets, paper documents, excel files or advanced web based solutions, the key is the quantity, quality, accuracy, regular updates and validation of data. 

Road Data Inventory Collection

Proper data collection begins with establishing a proper hierarchy of asset category, defined by; linear, point, land and fleet.  Establishing criteria such as relationships between the road sections and all other infrastructure below grade or on road allowance, linking tabular data to various mapping solutions.

Road data attributes include;

·       Road dimension captured in Area m2

·       Road surface type, Gravel, LCB, HCB

·       Road to and from location

·       Historical Condition rating, remaining useful life, PPI, PNV

·       Ward or area that roads belong to

·       Road classification based on MMS

·       Road type such as urban rural, cottage, local arterial

·       Traffic count

Accurate Data

What is meant by accurate data? It means having sufficient detailed inventory information, having accurate data, updated data and data that is properly segmented and structured.  Why is segmentation so critical?  When utilizing road data you want to be able to assigning a risk factor to each segment and road in front of schools, hospitals, main street etc. contain a different risk factor.  Proper road segmentation with unique ID can be incorporated into all municipal electronic solutions.

Data Overload

Be careful with data overload. Of what cost and value and at is knowing the location and condition of every sign in the town?   Often warning and regulatory sign is sufficient.   Do you capture information related to associated road assets such as signs, streetlights, sidewalks, drainage, ditch, guardrails and others?  Do you utilize historical PCI ratings in a meaningful way of viewing trends of wear and tear on assets or simply rely on remaining Useful life.

Optimized Models

At what point do you convert a gravel road to a hard surface? How to quantify that the AM plan is based on accurate data?  Recently we undertook an exercise with a midsize municipality to review their road data.  After a detailed consideration a full month of staff time to validate the road sections, structure the appropriate fields, assign a unique road section value which was accessible by multiple software solutions. 

Data Rationalization

Data must be rationalized. With so many different data fields, score cards, and other standards which vary from (A,B,C / 1-10 / 1-100/ Good, Fair, Bad). Asset conditions are best utilized and cost effective when normalized to a value of 1-5.

Financial Implications

Properly structured data is critical in allowing the town/municipality to assign true dollar value to the various lifecycle events. Towns need to validate that the cost associated with each lifecycle event is derived from their own past and current records rather than studies done in other jurisdictions or other pricing mechanisms.  The cost of pavement in Ontario varies from region to region and a 10% discrepancies can equate to hundreds of thousands of dollars in savings or loss.

Level of Service

This data when properly structures facilitates the establishment of LOS and the risk that the town is willing to adopt.  LOS is based on probability of failure and consequence of failure.  By analyzing the road data certain educated decisions can be reached as to the ramifications associated with road conditions (probability of Failure) as well as the risk impact (consequence of failure) on road sections.

All this data will help create theoretical AM model.  This theoretical model can be augmented with practical data derived from electronic road patrol, and routine inspections.  Combined these models will offer a strong validation on the AM decision making process.

As consideration for these items and the challenges towns municipalities have with decision making models, Marmak in partnership with several provincial associations has developed a web- based solution called Balance which utilizes standardized data to derive an optimized model. Over the coming months Marmak will be sharing its findings with participating towns which will be used to support AM capital plans.