School of Computing, Engineering and Technology

Optimizing UK Urban Mobility: A Computational Approach to Sustainable Transport

This project will investigate optimization techniques using mathematical graph theory and computational methods to model transport networks across the UK. Combining urban planning data, business intelligence, and vehicle tracking with spatio-temporal modelling and metaheuristic optimization, it aims to enhance both urban mobility and commercial transport efficiency through evidence-based solutions

Award

MRes | MPhil | PhD | MSc by Research

Start Date

Usually February and October - at individual School's discretion

Research Opportunities

Summary

The project aims to develop a state-of-the-art framework for analysing transport networks, incorporating diverse real-world metrics. These include environmental factors (carbon emissions, EV charging infrastructure), urban planning considerations (public space accessibility, infrastructure integration, street patterns), and economic measures (operational costs, infrastructure ROI, congestion levels, travel times). This comprehensive approach supports both sustainability goals and evidence-based planning decisions.

Key technical areas include graph theory and spatio-temporal network modelling; pathfinding algorithms (such as Dijkstra and A*); and metaheuristic optimization methods such as genetic algorithms (GAs) and estimation of distribution algorithms (EDAs).

Methods combine core computational approaches (stochastic optimization, transport network modelling) with strategic data collection through vehicle tracking. These technical methods will be integrated with urban planning frameworks to evaluate and enhance urban mobility patterns. The research emphasizes practical applications that support sustainable city development while maintaining computational rigor.

This project promotes interdisciplinary collaboration, combining computational methods with urban planning principles and business analytics. The research outcomes will support evidence-based decision making for urban planners, transport authorities, and commercial fleet operators, while contributing to sustainable urban development and efficient business operations.

The ideal candidate will have a strong background in computer science and mathematics, with demonstrated ability to develop solutions to complex real-world problems. Proficiency in programming languages commonly used for data analysis and optimization is essential, such as Python, Java, or R. Knowledge of urban systems or business analytics would be advantageous.