In transportation, knowledge is power

Transportation Planning

Nick Cohn – Senior Product Manager at Teralytics
Nick Cohn, Teralytics’ Senior Product Manager, is leading the development of Teralytics Streets, a software platform, currently in beta, that will allow transportation planners and road operators to tackle one of the pivotal challenges in road traffic management:

What does traffic look like street by street, day in day out? How do we minimise congestion and ensure smooth journeys from their origins to their destinations while removing carbon from our transportation systems?

Nick dedicated his career to improving decision making in transportation planning. He came to Teralytics after a number of years at TomTom, with a clear personal mission – to decode how people make travel decisions and apply this understanding towards making urban traffic management smarter.

What brings you to Teralytics?
Transportation planners’ job has so far been one of the toughest professions there are – to create the infrastructure and put in place services that cater to everyone, using no more than occasional glimpses into mobility behaviours and needs of a fraction of the population.

I’ve always sought to understand how people make decisions about where and how to travel. This knowledge is fundamental to our ability to ensure transportation services meet the needs of all, not just some, people; to reduce economic costs of delays, and allocate resources effectively.

Throughout my career, mobility data has always been this giant gap we’ve sought to bridge. Our behavioural models were based on conversations with maybe several hundred people we were thrilled to have been able to talk to. Data was always limited and therefore insufficient.

At TomTom, we found ways to turn the data collected from individual drivers into insights helpful to everyone on the road. We made inroads towards using real data, rather than solely relying on models, to improve decision making. This makes a huge difference in transportation planning where, without sufficient data, your models are bound to be wrong most of the time.

Insights from GPS offer an excellent understanding of traffic speeds, but less so about the various modes of travel, origins and destinations, or travel intent. I wanted to know how to arrive at total travel volumes in a way that’s both cost effective and remains up-to-date over time. You cannot rely on manual counts if you wish to account for everyone and make decisions effectively over time.

We’re working on Teralytics Streets to solve this long-standing challenge and we’re getting close to our goal.

What has it been like?
Solving one of the last big puzzles in transportation planning is of course challenging. We’re grateful to be able to closely collaborate with our customers to get it done. Embedding transparency into the process has been essential for winning over people’s trust.

It is tremendous to be able to help cities, particularly those with limited means, to manage traffic in ways that can have a tangible impact towards building more sustainable communities. I believe that Teralytics can get there due to its ability to deliver on three crucial elements – transparency, reliability and quality of output.

As you continue to grow Teralytics’ product portfolio, what are some broader goals the team has set out to achieve?
If we continue to focus on resolving solely what currently is, we’re bound to make outcomes ultimately worse both for our quality of life and the health of the planet. We must seek ways to change behaviours, rather than cater to them. Transportation planners must be able to understand people’s motivations and what constitutes meaningful incentives in order to drive behavioural change. Fuel waste and poor air quality are avoidable, but changing behaviours is difficult. The more we understand about mobility, the more likely we are to influence it.

I’m excited about the potential our technology has in identifying hidden demand, optimising transport systems and making choices with a long-lasting positive impact.

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