Crowd-sourced road roughness mapping

Focus Area

Construction and Maintenance Practices


Environmental Process, Natural Resources






1-2 years

Research Idea Scope

The purpose of this study is to demonstrate the use of smartphone sensors for automatically assessing the roughness of pavement and then efficiently managing the pavement network. By developing appropriate mobile applications that “connect” pertinent sensors with PMS severs via the “cloud (virtual space),” a much efficient and effective pavement management system (PMS) can be accomplished. At this initial stage, the proposed study will be focused on the “proof-of-concept.” If verified sufficiently through laboratory and field experiments, this concept will be rigorously tested for the large-scale network-level applications, where road users can simultaneously log and transmit data from different locations using their smartphones. This smartphone based crowdsourcing technology may hold a great promise to significantly improve the current PMS practice. Not only will it reduce the operation cost, but improve the efficiency and effectiveness of PMS, leading to significant savings in expenditure on pavement rehabilitation and maintenance.

Urgency and Payoff

The U.S. faces significant infrastructure challenges. More than 150,000 miles-or 45 percent-of federal highways and major roads in the U.S. are not in good condition, according to the Federal Highway Administration (U.S. Public Interest Research Group, 2010). The American Society of Civil Engineers (ASCE) scored “D” for the US roads in 2017, indicating there have not been enough improvements made so far. (ASCE, 2017). Considered as one of key performance indicators in modern pavement management systems (PMSs), pavement roughness represents both current and future conditions of public roads, and thus it has been widely used to determine and prioritize the maintenance options. Pavement roughness affects both vehicle dynamics and ride quality and is typically quantified with the International Roughness Index (IRI) that is based on vehicle responses such as road meter response, vehicle acceleration, and tire load. The US Department of Transportation (DOT) currently classifies the ride quality of roads into five groups (very good, good, fair, poor, and very poor) according to the IRI values expressed in either inch/mile or cm/km. Currently, most state highway agencies (SHAs) collect pavement roughness data using a field survey vehicle. Also known as a digital road analyzer, this vehicular data collection system costs more than one million dollars to build and needs to be maintained and calibrated every year at varying expenses ranging from $20,000 to $50,000. This data collection vehicle is operated under some data acquisition schemes (range, period and frequency, etc.) depending on the size of the road network and available budget. A previous study showed that the cost of conducting IRI measurements with automated data collection systems ranges from $2.23 to $10.0 per mile, suggesting an estimated annual expenditure of $1.2 million for the state of Georgia considering its 123,546 miles of public roads as of 2011. Because of the high operation costs, few SHAs collect IRI information for the entire or large portions of their roadway network on a yearly basis. Thus, maintenance and rehabilitation decisions are often made based on outdated and fragmented roughness data, yielding less effective implementations of PMS.

Suggested By

Youngguk Seo GPTRC at Kennesaw State University 6789155496

[email protected]