CAREER: Sensor Fusion for Multi-Modal Traffic Sensing

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The objective of this project is to lay the foundation for comprehensive real-time monitoring of traffic conditions throughout our urban streets and highways. The end goal: a scalable, model-based sensor fusion system capable of merging data from a large number of heterogeneous and geographically diverse sensors, and maintaining a coherent view of sensed, and inferred, traffic conditions. A city-wide, detailed and accurate view of current traffic conditions enables improvements in a wide range of applications in transportation. A key to achieving this goal affordabily is to find ways to extract more information from existing sensor infrastructure. Through collaborations with the City of Chicago, NAVTEQ, GCM Travel and the Chicago Transit Agency, the PI has access to Chicago-area data from sensing modalities including underground magnetic loops as well as bus, city-vehicle and crowd-sourced GPS traces. Complementing these sources are opportunistic video feeds from security, red-light and other traffic cameras, as well as vehicle re-identification based on radio frequency transmitters. With increasing congestion, rising fuel prices, and the threat of global warming, the need for improving the efficiency of our transportation systems has never been greater. Comprehensive sensing and inference of traffic conditions will help travelers make better travel decisions and help traffic engineers identify and fix problems. This project combines research with public service and open-source software development. The PI is partnering with a local high-school to host traffic sensing research interns, and the results from this project are used in a graduate course on transportation sensing at UIC.