Leveraging new technologies to shape innovative engineering solutions

June 28, 2024 by 1 Comment | Category Data, Data Science Accelerator, Digital Scotland

Guest blog by Georgia Boura, Graduate Civil Engineer, Transport Scotland.

As the national transport agency for Scotland, it’s essential for Transport Scotland to understand travel behaviour across the country. The traditional way to do this is through travel surveys, however collecting data in this way is time and cost intensive and therefore these cannot be carried out very frequently.

Datasets stemming from advanced technologies, such as ticketing systems and mobile networks offer the potential to better understand where travel originates and ends to create ‘origin-destination matrices’. Recently two sources of data were made available to us that we believed could be used to construct robust and cost-effective origin-destination matrices for the Scottish rail network without the need to undertake a survey. We wanted to explore this more fully, but we needed some additional technical understanding and support to understand the potential.

Our solution was the Data Science Accelerator, a programme that aims to increase the use of data science across the public sector in Scotland. As a Graduate Civil Engineer and data enthusiast, this offered a way to build my confidence in using data science to solve engineering problems, develop technical and project management skills and explore our business challenge.

The first dataset contained the movement trajectories of users of a large mobile phone network provider. This offered the potential to extract mobility patterns for a sample of rail travellers. The second came from ScotRail and contained the number of passengers boarding to and alighting from services on selected stations. My aim was to explore the potential of combining these datasets to construct origin-destination matrices for rail.

Since my past research experience in academia had already equipped me with a solid foundation in statistical programming, I selected to carry out this project using the R coding language. I was mentored by Vyron Christodoulou, Data Scientist at The Data Lab and our weekly meetings provided a great opportunity to discuss how I could improve my coding skills to implement in the project. Vyron provided valuable feedback on my scripts and nudged me towards the direction of optimising my code to avoid repetition and errors.

I learned to streamline parts of my code using functions, implement tests to assess whether the functions provide the expected output and use version control tools to document my code. Although these skills are not traditionally required in the Civil Engineering industry, they hold great potential for leveraging new technologies to shape innovative engineering solutions.

During the programme, I attended weekly cohort meetings, where the accelerator participants shared useful techniques and lessons learned from their projects, and attended presentations on wider topics, such as data ethics, code optimisation techniques and agile software development.

My participation in the Accelerator provided me with a unique opportunity to make progress on tackling a business need of Transport Scotland, while building my data analytics technical skills, along with a solid understanding of how these could be applied within the Civil Engineering profession. It’s also contributed to my progress towards attaining Chartered level of membership in the Institute of Civil Engineers.

Through these matrices, we’ll gain a more robust understanding of how many people travel by rail and where, and this information will be in turn used in our decision-making process for future policies and transport schemes.

The Data Science Accelerator is a fantastic development opportunity for analysts working in the public sector who want to develop their data science skills while also achieving a real business outcome for their organisation. Project applications and mentor opportunities for next year will open early in 2025 – contact to find out more.

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