Abstract Manager

The City of Toronto has thousands of kilometres of roadway and sidewalks, and every year repairs, resurfacing, and reconstruction are required to keep cars and pedestrians moving through the City. The City determines the group of streets for resurfacing based on state of good repair and issues a group program. The development of the contract package and scoping of the construction project starts with detailed data collection. RV Anderson Associates has had the privilege of participating in the Local Road Resurfacing Program (LRRP) for nearly 10 years, and has developed a coordinated system of data collection to produce the final tender quantities and drawings.

The data collection done for the City of Toronto LRRP was initially completed using pen and paper cataloging in conjunction with wheel and tape measurements. Data needed to be transcribed digitally and drawings produced from the recorded measurements. This simple data collection system was highly adaptable to the large variance of infrastructure between the dozens of streets in each years program list. The downsides were risk of clerical errors from data translation required a large amount of QA effort and duplication of data collection to ensure assets requiring repair had been properly recorded in the field.

To improve the data collection QA and simplify the data collection procedure further, a GIS based collection method was proposed. An ArcGIS map, with layers for all asset types encountered was created. Within each layer various fields were added where the condition and material details of each asset could be recorded. For instance, different driveway paving types such as commercial asphalt, stamped concrete, or brick. Catchbasin lids could be checked off for replacement, and guiderail could be identified as needing repair or replacement. Field collection staff can draw directly onto the aerial imagery within the map to denote all areas where repair or replacement was required. Tape and wheel measurements were still recorded to the asset layers to provide a redundant quantity and allow QA on the fly.  

All spatial data could be converted directly to drawings, enabling drafting staff to work directly with the collected data.  Additionally, using python scripting, all collected data could be extracted and converted into the City of Toronto’s quantity sheet format automatically, eliminating the risk of clerical errors.

By implementing the GIS based data collection method more accurate contracts could be prepared, and better decisions could be made for which streets to prioritize.