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Sensornets for Remote Vehicle Classification (SRVC)

The motivation for this work is the need for short-term data collection about freight traffic movement in urban areas. Applications for CALTRANS and METRANS include (1) monitoring truck traffic to estimate diesel pollution around port or distribution facilities, and (2) collection of traffic data to improve traffic models. We seek to develop automated vehicle classification systems based on networks of small, battery-powered and wireless, intelligent sensors that can be easily deployed with brief setup time (tens of minutes), with accurate vehicle information (as good as or better than human observers), and communicate this information to a central monitoring site. Current approaches are not rapidly deployable, accurate enough, and lack the ability to relay data in real-time to central site. The main new research challenges addressed in this proposed work are understanding the communications requirements for traffic monitoring systems (both short-range wireless inside a traffic sensornet, and wide-area to a central Traffic Management System), developing self-configuring traffic monitoring systems, and integrating prior work with these new results.