Swiftly CEO Talks Recent Acquisition, Power of Tech in Transit

In late August, Swiftly announced the acquisition of Hopthru, adding the company’s ridership data and analytics solutions to the Swiftly platform.

The move gives agencies new, more powerful ways to access, clean, monitor, and analyze ridership data for National Transit Database (NTD) reporting, service planning, scheduling, and more.

METRO’s Executive Editor Alex Roman recently spoke to Swiftly CEO/Co-Founder Jonny Simkin about the Hopthru acquisition, the power of data, transit’s readiness to assess new technologies, and much more.

The Hopthru Acquisition

Can you talk a bit about Swiftly and what your solution does for public transit?

Sure, at Swiftly, what we've done is really focus on building what we call a transit data platform. We take large data sets, typically unstructured data, and help turn it into useful information for transit agencies.

The first product line we introduced to the market was all-around real-time passenger information. We wanted to leverage modern, cloud-based native computation to more accurately predict when the next bus or train will arrive.

Many agencies we work with have seen their prediction accuracy increase, sometimes as much as 50 percent. When we spoke at APTA TRANSform last fall, along with Vontas and Transit, we were discussing the latest addition to that product line, which was introducing real-time detours into that information.

The second thing we did is we evolved into supporting performance insights, which is figuring out how we leverage billions of data points we're gathering every month to help agencies understand their on-time performance, or to help them optimize schedules based on all the data that is coming off their fleet.

Historically, the information Swiftly has been focused on is AVL- or GPS-based data, so tracking vehicles through time and space, and then learning about past movements and predicting future movements.

What Hopthru allows us to do is now track a different data set, which is ridership. So, we can start to track where people are getting on and off transit and how that might impact planning, scheduling, and even operations to help agencies deliver more effective and reliable service for their cities.

So what does this acquisition mean for Swiftly?

This is our first acquisition. We looked at a lot of companies over the years, and I think one of the things that was really exciting about Hopthru was how aligned we are, both in terms of culture and how we care about the public transit industry.

We also loved the team we were working with, which started as a partnership last year. Then, on the product philosophy, we were both focused on modern cloud native software and applying sophisticated algorithms to make sense of traditionally messy data to make it useful for agencies.

How did this acquisition come about?

As I mentioned, we started off as partners. For some of our shared customers, we were both helping customers with ridership analytics, NTD reporting, or both. As we started to follow the partnership and bring on more agencies, it became clear we wanted to double down and actually have them formally join our team. And so that led to our acquisition discussions.

So, for the end user, what are some of the benefits they will see by adding Hopthru to the existing Swiftly solution?

I’d say there are several benefits. Ridership data is historically messy and hard to analyze, so if you ask agencies what their exact ridership is, or how many people get on and off at various stops, it can be really hard for agencies to answer. They often struggle to access accurate and complete ridership data.

Another challenge is to correctly associate ridership data to schedules. So for the 8 a.m. trip, how crowded was that versus the 8:30 a.m. trip? Much of that work is done in Microsoft Excel and legacy tools, so it's very laborious.

We hear from agencies that some of the current vendors have data accuracy issues. One of the things we're really excited about with Hopthru is they have solved a lot of the data accuracy issues through a product called Cleanse, which analyzes the data and makes sure that you can turn that raw data into useful information.

The second product they have is Analyze, which then allows agencies to visualize the data quickly and be able to come to an understanding of what's working across the network. So, it's both creating accurate data, but also making it accessible, specifically to a non-technical audience of people who are trying to manually clean data in Excel.

On the Impact of Data in Transit

As far as the data is concerned, I’m guessing there's still a learning curve for transit agencies. How are they able to process that information, and what does Swiftly do to make the processing of that information easier?

That’s a great question. The two main ways we see agencies process this data today is manually, which is time intensive and prone to error, therefore, it doesn’t work well. The second is through a vendor that might exist on the market. Many of these vendors’ solutions are 20- to 40-years-old, so they actually pre-date cloud computing.

What we tend to hear from many agencies we talk to is they are having data accuracy issues. The second thing we hear when they use a different vendor is they have challenges visualizing the data and making it easy to use for decision-making purposes.

Many systems will actually print pieces of paper that you have to read to be able to derive what’s happening with ridership.

As we were thinking about the future of the industry, one substantial change that’s happened over the last decade-plus is cloud computing.

We all log into Gmail, Facebook, or Instagram, and part of the benefits of cloud computing is you can actually harness the power of much more sophisticated servers. You can develop much more sophisticated algorithms and quickly update and improve your software over time, just like on your iPhone or Android. It’s quite easy.

Frequently updating apps is a big part of what we're trying to bring to the industry. By having a modern, cloud-native platform, it is in many ways faster, more accurate, and easier to use.

It’s also built around innovation, where, over time, we can start to really push out updates, which allows us to grow beyond what staff can do, either manually or with many of these legacy systems.

As more data is available, what do you feel are the benefits for transit?

It’s a few things.

On the simplest level, NTD reporting is actually a big challenge for the industry. How do you report ridership to the NTD? That's what dictates transit agency funding. So at a base level, NTD reporting is a pretty big one. And, 2025 is actually a really big year, because that's the year tri-annual certification that NTD is going to require goes into effect.

So within the next year, if an agency uses automatic passenger counters for their NTD reporting, they have to get it recertified, and it's a cumbersome process. Most agencies struggle to do it. So that’s one thing coming up, which is funding for agencies that they desperately need.

The other issue is in large cities, we’ve seen examples where there are millions of dollars of potential inefficiencies that can be reinvested into the network by virtue of finding where and what segments need to be adjusted. By doing so, they may be able to stimulate more ridership, fewer greenhouse gas emissions, and/or more trips, which are all reasons why we want transit to exist.

So, finding millions of dollars of inefficiency, and then ultimately, providing a better service for riders are both really going to help with operational cost savings or reinvestments, as well as with funding coming in from the NTD.

Ultimately, this operational data could allow our transit agency partners to better serve their communities, which I think is what we're all here for.

When I've talked with agencies over the last decade, one of the biggest challenges the industry has faced is where they are doing well and where they are not. And that actually has an enormous impact on their ability to efficiently spend their budget to deliver the best service possible for cities.

I'm pretty excited that we'll be able to finally help with that, with the beneficiaries ultimately being millions of riders across the country.

On How Tech is Evolving the Public Transit Industry

Over the last 10 years or so, how much have you seen the industry become more willing to embrace technologies?

There has definitely been much change over the last several years, and I think there are still more opportunities for further change.

About 10 years ago, I was frequently explaining what a cloud native platform was or why data even mattered. Over time, what’s really changed is transit agency leadership has realized that what they can do at home as consumers is possible to bring to their systems.

We all have smartphones with tons of apps. The question is, how do we make operating transit look and feel like what it deserves using modern, cloud native tools?

That it has changed from paper and pencil to technology has been both welcome and important. It can help to find a lot of these inefficiencies that might be costing agencies millions of dollars a year. It can also help agencies deliver a better service for riders.

Dozens and dozens of case studies later, we’ve now proven to a lot of agencies we can make their lives much easier — we also hear riders saying the same thing. So it has really come full circle to supporting the whole ecosystem, and I think everyone sees now that change can be a good thing.

Swiftly, along with Transit app and Kansas’ Lawrence Transit, was recently the recipient of one of METRO’s Innovative Solutions Awards…can you talk a bit about how meaningful it is for small size agencies like Lawrence Transit to be able to utilize technologies that in the past may have only been available to the big boys?

Sure. Traditionally, and even still today, large transit agencies might have their own data science teams, which is great.

But when you get to these big agencies, you might also have thousands of employees, so while having their own data science team is great, that team is never going to be able to field all of the requests they're going to be getting from every team member across their organization. When you get down to smaller agencies, there’s no way they could hire a data scientist or anything like that.

At Swiftly today, almost half of the top 50 transit agencies in the country work with us, and we are empowering them with robust, sophisticated data-oriented tools, which help augment and support their own in-house data science teams.

But, as we continue to build out on a cloud native platform, all the small agencies we work with are also getting access to the same level of data science-oriented tools, which allow them to do things with the same level of sophistication as the big agencies.

The reason that is so huge is because funding is hard to get. Staff time is hard to get. But now with Swiftly, they are able to apply very sophisticated data science techniques and expose the information in really easy to digest ways that allow anyone to access it, whether they are technically savvy or not.

Access to data helps multiple departments within an agency. If it’s a big agency, it enables them to all see the same data so they can make sound, unified decisions. If it’s at a small agency, you no longer need the technical savvy to get the same types of insights as the big agencies.

What Swiftly, and technology in general, has done for public transit agencies is to liberate data and access to information so that agencies of all sizes can better serve their cities.