Automobile traffic is a major problem in developed societies. We collectively waste huge amounts of time and resources traveling through traffic congestion. Drivers choose the route that they believe will be the fastest; however traffic congestion can significantly change the duration of a trip. Significant savings of fuel and time could be achieved if traffic congestion patterns could be effectively discovered and disseminated to drivers. We propose a system that uses a standard GPS driving aid, augmented with peer-to-peer wireless communication. The prosed system uses a combination of clustering and epidemic communication to find and disseminate dynamic traffic patterns.

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We describe ELVIS (the Ecosystem Location Visualization and Information System), a suite of tools for constructing food webs for a given location. We express both ELVIS input and output data in OWL, thereby enabling its integration with other semantic web resources. In particular, we describe using a Triple Shop application to answer SPARQL queries from a collection of semantic web documents. This is an end-to-end case study of the semantic web's utility for ecological and environmental research.

Traffic Congestion is a multi-billion dollar national problem and worsening every year with population growth and increase in freight traffic. We present a model for realistic simulation studies to mitigate congestion in urban areas using dynamic congestion pricing on express toll lanes. Specifically, we identify and address the design issues needed to develop a real time event driven sensor web observing system for traffic monitoring that provides dynamic congestion pricing. To assess the feasibility of this sensor web system, we are in the process of conducting simulation studies based on real data for various system configurations to validate the mitigating impact of dynamic congestion pricing on intermodal freight flow to and from the ports in urban areas. In this paper, we focus on freight flow into the Baltimore corridor from its ports.

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