This GitHub is a companion to [CUSP - TfL 1] Visualising the changes in London’s mobility pattern: rail travel 2016-2022, a disertation by Zachary Segal for an MSc in Data Science at King's College London. It is based on the NUMBAT dataset from TfL available at http://crowding.data.tfl.gov.uk/.

Graphs 1-3 contain the html files for the visualisations mentioned in the disertation.

data.zip contains the data excluding the NUMBAT dataset which is available from http://crowding.data.tfl.gov.uk/

datadict.pickle.zip contains the dictionary of dataframes used for analysis

Data.ipynb is a Python Jupyter Notebook for the data manipulation and preparation

Visualisation Toolkit.ipynb is a Python Jupyter Notebook to create visualisations

Visualisation Walk Through.ipynb is a Python Jupyter Notebook going over how to create different visualisation types with teh visualisation toolkit and datadict.pickle

Thesis contains a copy of the disertation

Abstract: Efficient operation and planning of public transit systems are crucial for urban sus- tainability. This study aims to analyze the impact of COVID-19, the expansion of the Transport for London (TfL) transit system, and changes in workplace norms on pas- senger demand and behaviour by creating advanced visualisations for the NUMBAT dataset published by TfL. This work also seeks to drive future research by augmenting the NUMBAT dataset with other data and publishing results. This work has found several insights: COVID-19 significantly altered travel behaviour and demand, with a persistent reduction in system load through 2022 compared to pre-pandemic levels. The Canary Wharf-Stratford connections on the Jubilee Line transformed from being pre- dominantly used for leisure to commuter traffic. The introduction of new lines, such as the Elizabeth Line, redistributed passenger volume but did not significantly change travel patterns. This study contributes to the ongoing discourse on the impact of exter- nal factors on public transit systems and offers a state-of-the-art visualization toolkit, available on GitHub [1], for further research and analysis, as well as data not available from other public sources. Future studies could expand on this work with new data from TfL showing accurate ride information or by creating a more user-friendly web application.