Matt Pain: Data Analyst

A data analyst collects, analyses, and interprets data to identify trends and patterns, ultimately providing insights that can be used to inform business decisions.

They use various tools and techniques, including statistical analysis, data visualization, and programming, to transform raw data into actionable information.

We asked CIHT Member, Matt Pain, about his career as a Data Analyst.

What attracted you to a career in data analysis, and how did you end up applying those skills in the highways and transport sector?

My interest in data and its applications stemmed from an interest in learning how to code with the Python programming language.

Most of the practical applications of the Python programming language revolved around manipulating and analysing data. From learning the intricacies of Python I developed a strong understanding of how to work with, analyse and present complex datasets.

One dataset I worked with was a Department for Transport data set on road traffic collisions. Using my newfound knowledge of Python I worked on a personal project to turn the dataset into a story. From there I developed a real interest in how data could be used in the real world to better our communities. At the time I was already working as a transport planner and had developed a basic understanding of transport modelling and its applications within the transport sector.

Combining my knowledge of data analytics and transport modelling I soon realised how effective the two could be at effective storytelling and influencing real world transport decision making.

Can you provide an example of when the data that you provided, directly influenced a transport policy or infrastructure decision?

My current role is focused on transport modelling where high-quality data, both input and output, are an essential part of the model development and application process. I have recently worked on a multi-modal junction improvement scheme with several partner organisations where my role was to develop a transport model to undertake options appraisal to deliver a junction improvement that delivered both an improved junction for pedestrians and cyclists but also improve bus journey times.

Data played an essential role in the production of a final option that is now due to start on site in late 2025. Data was collected at the junction to establish base line conditions such as journey times, pedestrian/cyclists’ movements, origins and destinations of users and movements of vehicles. All this data fed into the development of the model. Data outputs from the model were extracted to understand how each transport improvement option performed which included journey times (active travel, busses and private vehicles), average delay by mode, average speed, queue lengths which all had to be analysed and presented.

Ultimately this work led to the best-balanced infrastructure option to be taken forward into delivery.

In what ways does your work as a data analyst contribute to making transport safer?

My current role mostly involves modelling and appraising proposed highway infrastructure improvements with core objectives in improving active travel infrastructure. The identification of improvements starts with how we can make these junctions safer for more vulnerable users. Where safer can mean a range of possible improvements from controlled crossing facilities to reducing crossing wait times.

This involves having a comprehensive data picture of how the junction currently operates and most importantly how the junction is used. A wide range of input data sources are identified from road traffic collisions to location-based data such as the location of nearby schools.

What role do you feel data plays in supporting more sustainable transport projects?

Data plays several key roles in supporting sustainable transport projects from understanding the safety of current provision (as previously mentioned) to establishing what type of sustainable infrastructure provides the most benefits and where. In my experience of developing recent transport schemes and making the case for change there are a couple of key areas that data is playing a more pivotal role in evidence-led decision making.

Current examples are understanding how impactful segregated cycling infrastructure is on increasing active mode use and critically if they encourage modal shift from less sustainable transport modes. One way to establish this is through strong and well-designed transport monitoring and evaluation programmes, where the collection and analyses of data is an essential role to provide insight into developing new schemes of a similar scope.

What excites you most about the future of data in highways and transportation and where do you see the biggest opportunities for impact?

There are a lot of upcoming and exciting areas of data in the highway sector. However, there are a couple that pique my interest the most. One of which is the use of AI in data capture.

AI-enabled highway sensors count and classify vehicles, creating a valuable long-term time-series data set. Additionally, with the use of AI data capture, near-miss technology is taking consistent leaps forward to being a reliable data source to identify areas on the highway network that could cause a safety issue before anything tragic happens. Connected Vehicle data is also proving to be an increasingly useful data source from both a network performance perspective and road safety.

Akin to the AI near-miss technology CV data can identify areas of highway safety concern before anything serious happens. With the use of g-force data we can identify parts of the highway network where heavy braking and aggressive turning is present and take a pro-active approach to highway safety improvements.

In your experience, is the sector inclusive in terms of welcoming a wide range of voices and perspectives?

I think the transport industry generally is moving in the right direction. However, there is more work to be done to ensure as many voices as possible and perspectives are heard. I think this is particularly true for transport modelling. Within the organisation I work for we are increasingly developing active travel schemes with high levels of future uncertainty and current modelling and analytical practices are struggling to provide meaningful insight in this domain.

Fresh perspectives from the wider technical industry can bring different analytical approaches to the table. In particular from the data engineering, software engineering, statistics and economics space where key skills such as problem solving are forefront.

What do you think leaders need to be thinking about or doing today that perhaps wasn’t part of the equation for leaders five or ten years ago?

I think for leaders it is critical to be vision focused with strong analytical teams around them to guide on what evidence we should be using to make the case for improved transport infrastructure. These analytical teams need to be present in both public and private sector organisations to ensure a balanced approach to evidence-led decision making.

Additionally, leaders need to be aware of the limitations of modern analytical and modelling techniques and not see outputs as answers, but insights that compliment a wider set of objectives and priorities. Fundamentally, despite our best efforts, no future prediction can be correct, and leaders need to be cognisant of this key limitation.

Would skills or do you think are needed to work as a data analyst and what would you say to someone considering it as a career path?

The key skills I would suggest to anyone looking at a career in the analytical and transport modelling space is to be adaptable and problem solving minded.

Additionally, a willingness to learn and continuously improve is essential to keep up with this rapidly evolving sector. We are at the advent of AI and its applications within the transport sector. This is likely to rapidly change how we approach and undertake transport modelling and appraisal.

However, we must not become complacent and where AI can help, especially in more repetitive modelling/analytical tasks, the core fundamentals of transport modelling need to be understood to draw meaningful insight to drive effective change.