Municipal Pavement Performance Prediction based on Pavement Condition Data

A May 20, 2004 Special Report prepared by TD Economics announced “After a quarter century of under-investment, Canada’s system of public infrastructure is in need of major repair and upgrade”. The report suggests that the infrastructure gap, i.e. the backlog of deferred maintenance, rehabilitation, and replacement of public assets, could be as high as $125 billion or 6-10 times annual investment flows. Transportation facilities account for some 60 percent of the public infrastructure. These funding shortfalls are considerable and a significant increase in funding is not likely. Pavement maintenance and rehabilitation needs studies frequently indicate that current funding levels are not sufficient to maintain the road system at an optimal level of service. In consideration, municipalities are now focusing on maintaining the overall value of their roadway assets and are striving to make better-informed decisions on how they allocate funding to minimize the deterioration of their assets. One method municipalities have been using to priorize and justify expenditures is regular road surface condition ratings that are summarized using an index value such as, pavement condition index (PCI), distress manifestation index (DMI), or similar. In the absence of other overriding factors such as, level of service improvements (widening, etc) or underground utility maintenance/rehabilitation, road surface condition ratings can be successfully used to program short term road needs. In conjunction with construction history information, the road surface condition ratings can be used to generate pavement performance prediction models for each pavement surface and construction type. Although pavement prediction models generated in this manner are purely empirical, the technology is relatively simple and allows for longer term forecasting of reconstruction and rehabilitation needs. This paper provides an overview of the pavement management process and presents a relatively simple methodology for developing pavement prediction models using existing inventory and condition data available to most municipalities.