Pavement Condition Monitoring with Crowdsourced Connected Vehicle Data

Abstract

The Michigan Department of Transportation (DOT) is interested in new technologies to improve transportation asset management (TAM) practices. A central component of TAM is pavement condition and performance assessment. To investigate methods to improve TAM data collection, the Michigan DOT and the Center for Automotive Research researched the potential for connected vehicle data to contribute to pavement condition and performance assessment. This effort included the review, critical analysis, and synthesis of several research reports and pilot projects to determine feasible methods of using connected vehicle data for pavement condition and performance assessment. The scope of connected vehicle data was defined to include data collected by sensors installed on consumer available vehicles and smartphones. The technology to allow for data capture from embedded vehicle systems and smartphones was established, but implementing such practices into TAM had not yet proved to be practical. One possibility for effective pavement data collection was to employ crowdsourcing, with the use of numerous probe vehicles. Crowdsourcing was not a common practice in pavement condition assessment. Using crowdsourced data in existing TAM programs may require innovative methods of data collection, processing, and management. This report concluded that by focusing on consumer available equipment and crowdsourced data, new connected vehicle data–based pavement condition metrics could be used for TAM in 3 to 5 years.