Alan Turing Institute Intership: TRL Software Ltd
From Jan - May 2022, I undertook an internship alongside my full time PhD study. The blog post on this page
was written to summarize my experience.
From Chemical AI applications to AI for Road Traffic and back again!
In the spring of 2021, in partnership with the Alan Turing Institute and their industrial placement network,
I had the opportunity to take part in a 4-month placement at TRL Software Ltd. Here, as illustrated by
the company tag line, my work focused on “delivering transport science through software”.
Why did I want a placement?
Having read the above paragraphs, I’m sure questions will arise about how I went from working on chemical
problems to road transport. Sure, it is not an obvious transition, but nevertheless it happened and here’s why.
Casting my mind back to where my placement hunt began, it was clear to me that I wanted to do something
that would contribute significantly to my personal development. The chance to work in industry was the
perfect challenge. I could apply all I had learned over the previous 3 years, face challenges well
beyond those in chemical applications and experience how AI truly impacts the real world. Facing and
learning from these new challenges to become a more well-rounded machine learning practitioner was the
single most important factor in my decision to take on a placement.
Who are TRL Software and what do they do?
Transport Research Labs (TRL) Software are somewhat of a unique company. To quote their own webpage,
“independent from government, industry and academia, TRL helps organisations create global transport
systems that are safe, clean, affordable, liveable and efficient”. In my experience, what this really
means is that their primary purpose is to make a difference by effecting change in the problems they
face. From saving commuters from being stuck in traffic, to ensuring pedestrians have enough time to
safely cross the road, the application and impact of the software products TRL provide will be felt
by most of us each and every day.
Making AI less scary…
Before I explain my project, I want to take the time to try and make the AI buzzword a little less
intimidating. When we apply AI to problems, our goal is to learn from historical data, so that we
can make informed predictions going forward. To do this, we can choose from a variety of machine
learning models, all of which are algorithms designed to find patterns in data. In my project,
we used a machine learning (deep learning technically) model called a neural network. Neural networks
are mathematical models that take inspiration from our brain. As such we have to train them so that
they learn how to predict our target of interest accurately. To put it another way, imaging a child
attending school on their first day. If we ask this child to sit a maths test, then they will probably
do poorly. However, if we ask this child to learn from lots of experience, in the form of many maths
lessons, then they can do much better on that test. The neural network works the same way. We give
it lots of experience in the form of data, and it learns to predict a particular value from this.
My project at TRL
The project was a collaborative effort between me and Shreshth Tuli,
another PhD student from Imperial College London. During my time on placement, our project aimed to
identify incidents on a particular road traffic network in Greater Manchester. Incidents is a fairly
loose term, but here I will define it as anything that causes disruption to the road and makes
changes to the normal traffic flow. Currently, road traffic incidents are identified manually which
results in missed incidents and has the potential to reduce response time – if the operator doesn’t
see the problem, they cant start trying to solve it. The project focused on using a neural network
to identify these incidents, allowing for faster, more accurate detection, which in turn leads to
quicker response measures and overall less disruption to road users. As you can imagine road traffic
data can be very noisy, with things like rush hours, sporting events, bank holidays or even the
COVID lockdown all making it very hard to see what normal conditions should look like on a given day.
If you aren’t interested in the technical detail then you can skip the next paragraph…
Road networks present a perfect example of spacio-temporal data. A road network can be represented
as a mathematical graph, with nodes and edges representing the junctions and the roads connecting
them. Having reviewed the literature, most previous attempts focused heavily on either the spatial
(how the road layout effects traffic flow) or temporal (how day of the week / time of day effect flow)
aspect of the problem, with none fully accounting for both. Hence, we designed a novel neural network
architecture, which used graph attention and transformers to capture both aspects and intelligently
combined to help make the predictions. We used data from Transport for Greater Manchester and
open-source traffic data to prove that our network was state-of-the-art in this task. Having
completed the project, we have a few publication goals, but at the time of writing this blog post
I’m very excited that we have presented our work at the
IJCAI 2022 - Workshop
on AI for Time Series Analysis.
Final thoughts…
Having completed the placement, I can’t speak highly enough of it. Working with Shreshth and
Chris Kettell (our boss) was fantastic, and I would like to express my sincere gratitude to
them both for their support, enthusiasm and sharing their knowledge with me over the 4 months.
I must admit, managing a half-time placement and full-time PhD simultaneously did not come
without challenges, (or its share of late nights), but looking back, it was excellent for
developing my ability to manage big projects in parallel. Although yes, there were lots of
hours, it never felt like a burden as the project was so exciting that often I would forget it
was technically work. I must admit, I have been pleasantly surprised with how much of my new
experience can now carry back to my PhD project as my attention falls back to thesis writing
and concluding my PhD.