Abstract
Intelligent Transportation Systems (ITS) can be a major component for improving transportation efficiency, safety, and environmental sustainability. However, many ITS strategies require accurate and appropriate data in order to function properly. The use of inductive signatures in an intelligent fashion can produce many types of data that are ideal as an input to these ITS strategies. A major benefit of using inductive loops is the wide availability of inductive loops in local and state roadways. Inductive signature analysis exploits the existing infrastructure to obtain valuable measures such as section density, section travel time, vehicle classification, single loop speed, lane change, and partial dynamic origin/destination demand. These measures are derived by using pattern recognition and optimization techniques such as multi-criteria optimization, artificial neural networks, and heuristics.