Encoding Dynamic Traffic Flow Analysis into AI for Network-Wide Early Alarming of Traffic-Demand-Influencing Events and Their Impacts

University Of Florida, Gainesville FL

Investigators

Abstract

This project will integrate dynamic traffic flow analysis into artificial intelligence to provide early alarming of significant traffic-demand-influencing events (DIEs) and the associated traffic impacts. Urban traffic can deviate from normal states due to various scheduled and unscheduled DIEs, such as sports, commercial promotions, and festivals. These events often induce a surge in traffic demand, cause hours of congestion, and affect multiple traffic infrastructures. Early awareness of DIEs and their traffic impact will benefit many stakeholders, including travelers, government, and transportation-related service providers, in taking proactive actions to manage traffic congestion. This project aims to develop a network-wide online DIE monitoring system, which can automatically provide early alarming of the DIEs and forecast the resulting congestions. The research outcomes can be directly employed to mitigate traffic network congestions and become an essential component of future smart city technologies. The interdisciplinary studies will open a new line of research on seamlessly integrating transportation engineering, data science, and artificial intelligence-based technologies to develop new scientific knowledge and methodologies for traffic operations and control. The PIs will integrate research into pedagogical developments at their home departments, actively disseminate research results, and engage in K-12 outreach activities via the Gator Outreach program at the University of Florida. This project will develop hybrid approaches that integrate traffic flow theories, optimization algorithms, high-dimensional machine learning, and artificial intelligence for monitoring the events that cause significant traffic demand surges in a (sub)urban area based on real-time temporal-spatial traffic data. The research will produce the following transformative scientific technologies: (1) Data science-empowered online shockwave generating algorithms to accommodate data imputation, which quantitatively captures the impact of a DIE on traffic conditions on a network over time; (2) innovative encoding approaches to compile shockwave diagrams for feeding machine learning model better; (3) sparse principal component analysis-based feature engineering for selecting and fusing the most promising subset of features for DIE monitoring; (4) a novel feature acquisition location recommendation scheme to determine where new features should be additionally acquired from the traffic network to boost the DIE-monitoring AI maximally; and (5) a radical extension to the recurrent neural network (RNN) model and theory by incorporating explicit regularization and distributed computing-compatible architectures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.