Using context and behavioral patterns for intelligent traffic management

The integration of information and communications technologies across existing transportation infrastructure, systems and vehicles is fundamental to reducing traffic congestion, to improving driver safety, and to improving traveler experiences. Central to such intelligent traffic management are techniques and algorithms that are capable of analyzing the wealth of available contextual sensor data in “real time”. Initial existing approaches tend to apply probability models and inference techniques to optimize traffic flow but fail to take into account certain aspects of human behavior that can affect the flow of traffic, such as patterns in human travel behavior. In this paper we explore how vehicle context information can be combined with the behavioral patterns of travelers to facilitate and improve intelligent traffic management. We present services for deriving reports on vehicle journeys that assist in the analysis of route performance, for enabling passengers to have remote access to real-time route performance information, and for the observation, learning, and utilization of human travel behavior patterns. These services provide essential traffic analysis information that is ultimately expected to lead to further improvements in intelligent traffic management, which aims at easing the flow of traffic in urban and suburban environments