Pictured above: Jeremy Foran works with contestants of the 2017 Hacktrain event in London.
Jeremy Foran has been with the BAI Canada team for over three years. In his role as a technology specialist, he focuses on using data to solve challenges for rail operators and their customers. We sat down with Jeremy to hear more about his day-to-day and what keeps him busy outside the office.
What’s the most exciting development happening in the industry right now? What does this mean for BAI Canada?
I think the most exciting thing currently is being able to apply technologies like machine learning to complex issues and seeing first-hand the positive results that they deliver.
A few years back we were challenged with accurately reporting how many people were on our public Wi-Fi networks at any given time, so we built a data management platform to solve it. By combining user experience monitoring with machine learning data, we’re able to detect when there is an anomaly in the number of people using our public Wi-Fi system, before it becomes an issue. For example, if it’s 9am and we see there has been a drop in use of the system by 0.5% we know there’s a problem. For BAI, this means we’re able to offer a better user experience not only on the Toronto subway but also on the New York subway.
What motivates and excites you about your job?
One of the biggest motivators for me is finding the people who benefit from being connected. Two obvious examples are Torontonians and New Yorkers who can stay in touch as they move through the underground rail networks. But it’s important to point out that rail operators also stand to benefit from understanding how commuters are engaging with the public Wi-Fi network – where they’re getting on and off the train and at what time.
These data-driven insights enable rail operators to predict how crowded the subway will be and adjust train schedules for special events. The data also feeds into an app that commuters can use to see how many trains will go by during peak hour before they’ll be able to board, based on congestion.
The scale at which we’re crunching masses of data is fascinating. Wherever we solve these challenges, we pick the solution up to take to rail operators in other cities, from Toronto to New York. This isn’t a topic I’d bring up on a date, but you can probably tell that personally, it really excites me.
What’s one of the challenges in your role?
One challenge is finding the correct balance between what we find interesting and what is of benefit to our customers. We need to understand what has the most potential to have the greatest impact on our business.
Once a year, the Toronto subway hires 30 people with clickers to manually count how many people enter and exit the subway (to ensure the station doesn’t exceed fire capacity regulations). Every 15 minutes they write the number of people on a piece of paper. But when you’re counting more than 10 people per second, this process is prone to human error and doesn’t account for blind spots in the data. Other reasons this is an inaccurate method is that foot traffic on a Thursday is very different to a Sunday. The weather also has an impact. This method is ineffective for multiple reasons and rail operators are struggling to find a more accurate way to tackle this challenge, so they can better plan.
At BAI, using a team of data scientists we have proven that Wi-Fi association is an accurate determinant of the total number of people coming into the station. This provides a very precise indication of congestion and people coming and going. With this data, we’re able to provide insights to the transit authority at any standard of time, day and month instead of once a year based on last year’s data. As a result, rail operators are more efficient in the way they plan the rail equipment and schedule, and commuters have a more seamless experience.
What are some things outside of work you like to do?
I love coding and I’m not the first person to tell you that it’s dominated by males like myself. One organisation that I am proud to be involved with is Ladies Learning Code. Ladies Learning Code offers a boot camp style course for women who want to learn coding, from children through to adults. As part of that, I mentor the adults. It’s a great way for me to keep up my passion for coding outside the office.
BAI Canada is a BAI Communications company.