Cities have depended on each other since the origins of civilization. The original trade networks evolved to networks of cities that enabled the flow of knowledge embedded in physical resources and people.
Today, with the advent of AI, robots, and sensors, networks within and between cities increasingly rely on intelligent machines. This, in turn, has promoted a plethora of new connections between urban system components. Thus, to ensure sustainable urban development across different layers, it is imperative to understand the interactions between the physical, social, and digital networks.
The goals of this cluster are threefold. First, to use multiple perspectives in order to better understand the connections between different components of the urban systems. Second, to explore how such connections also affect the interactions across cities leading to different developmental stages, different challenges, and potentially different solutions. Third, to leverage the interactions between the different types of networks to propose innovative yet tangible solutions to global challenges.
Improving Multimodal Mobility in Urban Networks
Traffic congestion is a challenge across cities worldwide, with multiple transport modes competing for limited road space. At the same time, new technologies are triggering rapid changes in mobility. This project aims to develop novel modeling and optimization tools for designing, operating and controlling advanced mobility systems, especially during a transition period where conventional and automated vehicles coexist. The idea is to build solutions that improve the performance of the overall system while taking into account the interactions and trade-offs across the different modes.
Logistics and Supply Chain Optimization
This work aims to study challenges and opportunities in the broad field of supply chain management, logistics, healthcare management, and production planning, with particular focus on Abu Dhabi and the UAE. One of the health care management projects is the design of robust supply chains for blood components that are resilient to disruption due to disasters, a research that is particularly timely due to the Covid19 pandemic (during a pandemic, blood resources tend to dramatically decrease).
CityGraph: A Participatory Open Data Framework with Applications to Geolocalized Longitudinal Data in Smart Cities
This project aims to foster civic engagement in urban data gathering and analysis thanks to, among other things, the creation of a new platform for data sharing. Smart cities require data collection and exploitation (e.g., data mining, real-time anomaly detection) to increase their efficiencies. The generic issue in science, as well as for policymakers, is that the data producers, such as residents, may not be the end-users of the data. This leads to inefficiencies, gaps, and inconsistencies. This project proposes a general methodology to reconcile the objectives of data contributors and data users. In particular, it will apply this methodology to the problem of collecting geolocalized longitudinal data. The team will study two situations relevant to smart cities: first, high frequency (within a day) of intra-city mobility, and second, low frequency (months, years) of inter-city mobility to understand the general case of attractiveness and efficiency of a city over time. For that, the team will investigate how a smart city can foster the participation of its residents and tap into open data and public knowledge bases to measure critical aspects of its development. In the long term, the CityGraph project aims to create a platform for data sharing and contribution. This will provide a framework to collect new forms of data crucial to studying various societal questions.