Air Quality Messaging: Measuring the Effects of Information Provision on Knowledge, Behavior and Policy Preferences
Fellow: Hilina Yigzaw Bayew
Supervisors: Professor Melina Platas
In the upcoming year, as a new CITIES Post-Graduation Research Fellow, Hilina Bayew will collaborate with Professors Melina Platas on an interdisciplinary research project focusing on monitoring indoor and outdoor air quality, followed by providing air quality messaging to assess its impacts on individuals’ knowledge levels, behavior, and policy preferences. Hilina’s research extends beyond the NYU Abu Dhabi campus into various parts of the city. The objective is to monitor air quality levels in classrooms at different high schools to regulate outdoor activity during periods of poor air quality (specifically when the Air Quality Index, AQI, exceeds 150). The project proposes an interdisciplinary approach, bridging pre-existing air quality modeling conducted by CITIES monitors with primary data that Hilina collects through her various devices to provide messaging and influence behavior and policy. Moreover, Hilina aims to map ‘A Day in the Life’ of various individuals to gain a better understanding of the diverse exposure levels to poor air quality. She hopes that this project will allow the stories of those in more disadvantaged, highly affected groups to be told. Additionally, educators will hopefully become more sensitive and aware of air quality conditions, enabling them to make more informed decisions regarding their students, which, in turn, empowers students to take better care of their health. By initiating research in this highly understudied field, Hilina aims to foster future collaborations with the Environmental Agency – Abu Dhabi and other government stakeholders to establish more comprehensive air quality guidelines moving forward.
Interpretable, Scalabl, and Generalizable Spatial-Temporal Graph Neural Networks for Traffic Flow Prediction
Fellow: Ryoji Kubo
Supervisors: Prof. Djellel Difallah & Prof. Monica Menendez
As a CITIES’ Post-graduation Research Fellow, Ryoji Kubo will collaborate with Professors Djellel Difallah and Monica Menendez on a research endeavor focusing on the advancement of traffic flow prediction through the lens of spatial-temporal graphs. The primary goal of this project is to develop cutting-edge methodologies that not only facilitate accurate traffic flow predictions but also prioritize interpretability, scalability, and generalizability. By leveraging state-of-the-art graph machine learning techniques, Ryoji aims to unravel the intricate dynamics of traffic patterns while enhancing the usability of such models for real-world applications. A pivotal aspect of the research involves addressing the challenges posed by varying large-scale datasets, thus ensuring the adaptability and scalability of the proposed methodologies across diverse contexts. As a culmination of this endeavor, Ryoji intends to deploy the developed frameworks to analyze traffic flow data in the UAE region, thereby offering invaluable insights into regional urban mobility dynamics. Through this initiative, Ryoji aspires to not only contribute to the advancement of traffic management systems but also lay the groundwork for future research endeavors in the domain of graph-based predictive analytics.