Primary supervisor: Mona JaberSecond supervisor: Greg Slabaugh
Project abstract: Digital twins are virtual replicas of a physical asset which, in this project, consists of urban mobility in a defined geographical area. Such virtual replica enables non-disruptive and accelerated optimisation toward goals that could include reducing car emissions, increasing the uptake of active travel, improving road safety, or others. Digital twins are empowered by streaming data from connected sensors (IoT devices). These data are interpreted by Artificial Intelligence models to replicate the target aspect of the physical asset; i.e. urban mobility of the given area. This project investigates continual learning models that update their representation to reflect the dynamically changing data that might be caused by a change in the physical asset (road network, related policies, nodes of transport, etc.).
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Primary supervisor: Mona JaberSecond supervisor: Jun Chen
Project abstract: In the dawn of autonomous vehicles and their co-existence with other multi-modes of transport that include human-driven and driver-assisted sharing the road space with adopters of active travel (e.g., pedestrians, cyclists, micro-mobility), the transportation sector needs to be upgraded to ensure road safety for all road users. This project leverages the digital twin paradigm to design anticipatory road safety solutions based on a physical testbed. This will entail mining Internet of Things data collected from the testbed in addition to model-based mobility and geospatial information to create a virtual replica of the transportation scene. The aim is to identify pertinent upgrades to the road network and related policies that will enhance the public safety of commuters.
Primary supervisor: Mona JaberSecond supervisor: Ahmed Sayed
Project abstract: A digital twin is a virtual replica of a physical asset that could be used to enable fast anticipatory solutions without disrupting the real world. This project leverages digital twins in the context of expediting an effective and efficient aid response in the advent of a disaster. To this end, the project will investigate a collaborative system that is driven by artificial intelligence models trained on constrained decentralised sensors with disrupted connectivity. Such scenarios will be simulated in the virtual replica and collaborative systems will be examined to obtain adequate responses in anticipation of disasters.