Data Science at LIDA - Transport & Travel

The 1st instalment of our data science mini-series showcasing current research projects here at Leeds Institute for Data Analytics (LIDA). This weeks features 10 minute lightning talks from three data scientists about their research on Transport & Travel.

Chair - Francesca Pontin, Senior Research Data Scientist, Consumer Data Research Centre

TALK 1 - Incorporating Geospatial Climate Data into Statistical Models of Transport Infrastructure and Operations
Toluwani Osabiya

Summary - This project integrates geospatial climate data into transportation models, exploring the impact of climate change on rail incidents like delays and cancellations. The research examines French rail and weather data from the SNCF Reseau and CQC Efficiency Network and English weather and rail data from the CEDA Catalogue and National Rail Data Portal, respectively. By enhancing transportation models with climate insights, the study aims to guide adaptive strategies, ensuring resilience and sustainability in the face of climate change. This initiative underscores the importance of data-driven decision-making for the future of transportation systems.

Biography - Pharmacy graduate turned data scientist with a Master's from the UK. Excited to join LIDA and learn from experts, aiming to apply data science to healthcare, business, and sustainability challenges. Passionate Arsenal FC supporter, avid reader, and video game enthusiast.

TALK 2 - Using non-survey data sources and innovative methods for international travel and tourism & statistics
Aditi Sudhakar

Summary - This project, in partnership with the Office for National Statistics (ONS), aims to transform UK International Travel and Tourism (T&T) statistics by replacing traditional survey data with non-survey sources. The current reliance on the International Passenger Survey (IPS) incurs high costs and granularity limitations. Shifting to non-survey sources, such as AirBnB and Mastercard data, promises higher quality, more accurate, and granular data. This transition improves decision-making and also potentially results in substantial cost savings for the publicly funded ONS. By utilising machine learning and statistical methods, the project seeks to enhance the accuracy, depth, and cost-effectiveness of T&T statistics production and support evidence-based policy-making.

Biography - Starting as a pre-med student, I rerouted my career path towards computer science engineering, with the ambition of blending my love for mathematics with my passion for medicine. This brought me to Leeds for the MSc programme in Data Science and Analytics and subsequently to the Leeds Institute for Data Analytics (LIDA).

TALK 3 - Breaking barriers to active travel: Modelling the Impact of Weather and Daylight to Reduce Inequality
Lydia Wharton

Summary - In partnership with Bradford City Council, this project aims to understand how weather and daylight affect active travel modes, such as walking and cycling, in Bradford. The project
examines Strava data along with demographic and environmental datasets to uncover demographic inequalities in active travel and assess the impact of infrastructure changes. Preliminary findings indicate higher levels of active travel in affluent areas, varied correlations between rain and cycling, and notable bimodal trends linked to morning and afternoon commutes. The project will output a visual dashboard for stakeholders, highlighting the influence of weather, demographics, and infrastructure on active travel, thereby aiming to reduce travel inequalities and inform improvements in regional infrastructure.

Biography - Lydia is a data scientist at LIDA, under the DSDP. She earned a BSc in Environmental Science from Sheffield Hallam University and later an MSc in AI and Data Analytics from the University of Bradford. Prior to joining LIDA, Lydia gained experience as a research assistant, which played a pivotal role in her career journey.