A Resilience-based, Mobility-driven, Decision Support System for Multi-Modal Transport Networks
The project aims to produce a decision-making framework, that assesses the impact of inherent connections in multi-modal transport networks (e.g. road, rail, air, ferry) during failure/disruption propagation and recovery/restoration process.
Start date1 September 2021
£15,285 per year in 2020-21 plus a Research Training and Support budget to cover costs such as conferences, workshops and equipment.
With the increased frequency and magnitude of extreme environmental events in Scotland in the last decade, one of the proposed priority areas for the Second Scottish Climate Change Adaptation Programme is to provide climate change resilient transport systems (Figure 1). A key factor in achieving this goal is to develop a management system that operates across transport modes, appreciates the inherent interdependencies between different modes, implements mobility patterns, identifies primary vulnerable zones and prioritises investments and supports resilience capacity building accordingly. Currently, transport asset management systems work in isolation, addressing single-mode transportation networks without considering connections and mobility patterns/shifts between different modes. This approach neglects cascading failures due to inherent connections between modes of transport and hence underestimates the amplified negative consequences due to these failures, under extreme weather events. A more scientific approach to quantify these interdependencies could offer opportunities in shared intervention measures where mode shifts could be implemented in emergency systems. The project aims to produce a decision support framework, that assesses failure propagation due to inherent interdependencies in multi-modal transport networks. This project builds on lessons learnt from a feasibility study entitled: Resilience and Vulnerability-based Decision Support System (RV-DSS) considering Infrastructure interdependencies. The project will start by developing a geospatial database of a multi-modal transport system, integrating geometric, topological, semantic and operational data from different modes of transport. You will then build a novel inference system that can identify and classify the spatial and temporal correlations in different modes of transport. The output from this model will be then used as an input in a multi-agent network system to be tested on a wide range of climate change-induced hazardous scenarios. Given your interest and progress, you will have the flexibility in adjusting and focusing on different aspects of producing the multi-modal decision support framework.
This project is suitable for students with an engineering-related, mathematical, environmental or transport degree (either undergraduate or postgraduate) or professional experience in engineering.
The studentship is available for UK, EU and overseas students.
IELTS requirements: 6.5 or above (or equivalent) with 6.0 in each individual category (not for native speakers).
How to apply
All applications to SCENARIO are made via the University of Reading, whether the projects you are interested in are based at Reading, Surrey, Centre for Ecology and Hydrology, British Geological Survey or Institute of Zoology.
Choose the PhD projects that interest you most (maximum of 4) and rank your choices in order of interest. Your application is only sent to supervisors for projects where you express an interest, so listing more increases your chances of success. If in doubt, choose 4. There will be limited possibilities to express interest for other projects later in the Admissions process.
Each project description indicates the name and institution of the lead supervisor and has a reference number. You are welcome and encouraged to email the lead supervisors of projects to ask them any questions you may have or to discuss the project.
Main interview day: 10 February 2021
It is likely that our interview day will be an online event, but that decision will be made nearer the time based on governmental Coronavirus guidelines.