The objective of the Faculty Early Career Development (CAREER) program award is to develop the foundations for a new area of transportation science that takes advantage of, and addresses the challenges of using mobile traffic sensors for traffic knowledge extraction and dynamic system management. This research will study the fundamental questions of how to use widely-deployed mobile sensors for (i) estimating/predicting traffic states on arterial networks; (ii) discovering new traffic knowledge and behaviors under normal and post-disaster situations; and (iii) developing path-based dynamic network management strategies utilizing the knowledge obtained from mobile sensors. This research will develop a mobile-data-based modeling framework that will enrich current fixed-location-sensor-based modeling techniques. It will also develop theories, models, and algorithms for path-based dynamic network management strategies, with explicit consideration of multiple equilibria and real time information from mobile sensors, which will bridge various fields such as optimization, network modeling, and control theory.
This research project is expected to transform current traffic data collection and modeling from a fixed-location-sensor-based to a mobile-sensor-based paradigm and will help policy makers and industry make informed decisions on how mobile data can be best used for knowledge extraction and traffic system management. This project will develop new educational tools by integrating project-based learning in the civil engineering curriculum and will develop short courses and research seminars that will foster the communication among academia, government agencies, and industry. This work will conduct outreach activities by disseminating research and education outcomes to a wide audience that includes students in universities and K-12 schools, and in particular students in minority universities. This project will also use an external advisory panel with members from multidisciplinary backgrounds so that the research can address a broad set of questions and apply the results to other fields beyond transportation.
