This project aims to radically transform traffic management, emergency response, and urban planning practices via predictive analytics on rich data streams from increasingly prevalent instrumented and connected vehicles, infrastructure, and people. Road safety and congestion are a formidable challenge for communities. Current incident management practices are largely reactive in response to road user reports. With the outcome of this project, cities could proactively deploy assets and manage traffic. This would reduce emergency response times, saving lives, and minimizing disruptions to traffic. Efforts are planned in Kindergarten-12 outreach, undergraduate education, outreach to women and minority students, and incorporation of the research into courses, with the goal to inspire and train a diverse cohort for the next-generation of scientists and prepare them for taking on challenges arising from smart and connected communities.

To realize the envisioned system, an integrated research approach is taken to tackle the following closely related research tasks: (1) integration of heterogeneous data streams using a new sparse multi-task multi-view feature fusing method; (2) prediction of traffic incidents by designing a novel high-order low-rank model; (3) teaming of connected vehicles and roadside sensor systems; (4) verification of traffic condition prediction by crowdsourcing the ground truth from user reports in real-time; (5) selection of crowdsourcing participants that recruits and selects voluntary operators of instrumented connected vehicles to provide onboard sensing readings; (6) selection of high quality and diverse images and videos from crowdsourcing vehicles to provide better data for traffic prediction; and 7) design of optimal rerouting strategies to improve commuters' routes in the time of potential traffic disruption.