Traffic data, including traffic load and traffic volume, are necessary for pavement design and management of the road networks. However, in practice, such data is also the largest source of uncertainty amongst the various pavement design inputs. Traffic volume often exceeds predicted volume, and truck overloading occurs frequently. This results in pavement premature deterioration, early or mistimed maintenance activities and eventually high life cycle costs. The significance of highway preservation and budget allocation constraints have motivated development of sensing technologies for collecting accurate and detailed traffic information. While static scales had been used widely to collect vehicle weights, Weigh-In-Motion (WIM) systems have been focused on utilizing state-of-the-art technologies to collect various types of traffic data. These systems continuously collect data, including gross vehicle weights (GVW), vehicle speeds, axle loads, and vehicle classification, as vehicles travel over a set of sensors without interruption of traffic flows. Many up-to-date pavement design protocols require traffic input, and in particular the new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) requires axle load, axle spacing, and Average Annual Daily Truck Traffic (AADTT) obtained from WIM. This paper identifies different WIM sensing technologies, with particular emphasis on piezoelectric, bending plate, load cell, and quartz piezoelectric sensor systems. It qualitatively compares the advantages and disadvantages of these WIM systems, with respect to cost, accuracy, applicability, reliability and sensitivity. In addition, the new MEPDG was run for scenarios to provide insight into the impact of traffic load on pavement design and management, and the economic value of WIM systems.
