Transport with Data Science

Gain a thorough understanding of key principles underlying transportation systems, including their operation, context, and relationship with the built environment.

This foundational module provides the foundation for the topic that will be covered throughout the MSc course.

Develop fundamental skills in statistical modelling from both theoretical and applied perspectives.

This module covers essential theory, methodology, and computational techniques for constructing statistical models to represent real-world scenarios, supported by practical training in R and RStudio.

Understand traffic engineering fundamentals, including macroscopic fundamental diagrams, shockwaves, queueing theory, and traffic microsimulation.

Practical applications focus on solving real-world traffic problems, including signal control, congestion management, speed limits, parking management, and safety standards.

Gain comprehensive expertise in machine learning tailored to civil engineering applications.

Key areas include supervised learning (regression, classification), unsupervised learning (clustering), large language models, and reinforcement learning.

Hands-on training in Python using NumPy, scikit-learn, and PyTorch will enhance your practical skills. It will help you put theory into practice across a set of case studies in the civil engineering sector.

Build a rigorous mathematical understanding of travel demand and transport supply models.

This module covers aggregate and disaggregate modelling techniques, including discrete choice and stated preference methods, equipping you to evaluate transport policies through market analysis, cost-benefit assessment, and welfare maximisation using specialised software.

Examine how transportation affects energy use, climate change, air quality, and noise pollution.

Specific transport modes, such as active travel, road, aviation, and shipping, are discussed alongside critical innovations that promote sustainability

Discover detailed insights into rail and mass transit operations, planning, infrastructure, and policy, informed by international case studies.

This module addresses public transport principles, specific service requirements, economic drivers, and the historical and future contributions of mass transit to sustainable urban development.

Develop practical experience constructing, validating, and deploying machine learning models and algorithms in Python.

This module covers key areas including reinforcement learning, computer vision, developer tools, version control, cloud computing, visual analytics, data modelling, database management, and advanced data manipulation and visualisation techniques.

Gain a thorough understanding of freight transport operations and the mathematical modelling techniques associated with efficient supply chain management and distribution strategies.

Topics encompass analytics, decision models, optimisation tools, and specialised logistics applications (healthcare, humanitarian, disaster response).

Building upon the knowledge covered by the programme throughout the year, this module allows you to consolidate your knowledge and understanding through practical application in the context of a transport design project.

This module, delivered over 4 weeks full-time, provides you with an opportunity to work in teams on a significant piece of transport engineering design. Industrial partnership on the projects enables you to gain real-world insight into the design process. Emphasis is placed on effective team working, creativity, design understanding and practicality, the appropriate use of technical knowledge, and the ability to present effectively in written, poster and oral presentation forms.

You will work with clients to develop the briefs into projects, exploring design proposals in an iterative manner, building towards the effective presentation of a comprehensive feasibility and outline design study.

Master key methods and concepts in transport planning and policy through the study of the four-stage travel demand model: trip generation, trip distribution, mode choice, and traffic assignment.

You will apply these models practically, enhancing your skills in data handling, estimation, and interpretation while addressing contemporary policy issues and advanced modelling techniques.

Investigate the complexities of safety and security in transportation systems, examining interactions among technology, procedures, and human factors within organisations.

This module integrates quantitative analyses of crash data and system vulnerabilities with qualitative evaluations of legislative impacts on transport safety.

Explore the air transportation system comprehensively, from aircraft operations and key technologies to essential human factors ensuring flight safety.

You will study the influence of international aviation regulations and governance, as well as the safe integration of unmanned aerial systems (UAS) into urban airspace.

Explore intelligent and autonomous transportation technologies driven by vehicle connectivity, automation and artificial intelligence.

Learn how these innovations influence transport planning, infrastructure use, and decision-making, and gain practical skills in designing and operating intelligent transport systems with AI-driven data analysis.