Why will Public Transit Data help save the planet? With Jonny Simkin.Read DocumentGet Document
Why will Public Transit Data help save the planet? With Jonny Simkin.
Why will Public Transit Data help save the planet? With Jonny Simkin.
On this week's episode of Between the Lines, we chat with Jonny Simkin. Jonny is the Co-founder and CEO of Swiftly, Inc., a mobility operating system that empowers mass transit agencies to provide more efficient, reliable, and seamless transportation. The platform weaves data analytics, intuitive visualizations, and real-time predictive technologies into applications that drive smarter decisions for agencies. Today, over 100 cities and 7,000 transit professionals use Swiftly to improve transportation for nearly 2 billion passenger trips per year.
Prior to founding Swiftly, Jonny was the Director of Product at Rafter Inc., where he helped 3 million students save over $700 million on college textbooks. Before Rafter, Jonny was the Co-founder and CEO of HubEdu, Inc. which was acquired by Rafter in 2012. Jonny holds a Bachelor of Science in Engineering with an Economics Concentration from Harvey Mudd College. In his spare time, he enjoys hockey, ping pong, tennis, and coffee.
And check out Jonny's favorite commuting song on our exclusive commuter playlists on Spotify!
-Here Comes The Sun, The Beatles
-(Intro): Commutifi presents Between the Lines with Andy Keeton. Each week we explore the challenging issues transportation demand management professionals face on their journey to transition commuters, from driving alone to more sustainable shared and active commuting habits. Be sure to subscribe to hear next week's episode, and check out our exclusive commuter playlist on Spotify. This is Between the Lines with Andy Keeton.
-(Andy): Hi, everyone and welcome aboard to this week's Between the Lines podcast. Today I am joined by Jonny Simkin. Jonny is the co-founder and CEO of Swiftly, a mobility operating system that empowers mass transit agencies to provide more efficient, reliable and seamless transportation. Today over a hundred cities and seven thousand transit professionals use Swiftly to improve transportation for nearly two billion passenger trips per year which is great. We're really excited to be talking obviously about transportation. And prior to founding Swiftly, Jonny's also been around the block a bit in the tech world, is the director of Product at Rafter Inc and the co-founder and CEO of Hub Edu Inc. And in his spare time he enjoys hockey, ping-pong, tennis and coffee, all great things I would say. Thanks for being on today, Jonny.
-(Jonny): Thanks for having me, Andy.
-(Andy): And today we're talking about, I guess it might be obvious to our listeners, we're talking about public transit, but specifically we're talking about public transit data and how it will help save the planet. So, maybe to frame things off, can you tell us what is Swiftly and how do you work with transit data?
-(Jonny): Great question. So, yeah. It's hopefully, we consider ourselves to be really the first big data platform for public transportation. We take a wide variety of datasets as inputs and really what we're trying to do is drive safe efficient and reliable transit. So we have three core product lines. The first is called transit time and that's all around generating more accurate ETAs and information for riders. So we power the real-time data that goes into apps like Transit or Google Maps. If you've ever opened any application you see the next bus arrives in five minutes. In the cities that we work in we power that data, and the reason why that's important is we can improve the accuracy by up to 30 percent. So it has a huge impact on the overall rider experience. And then we have a whole suite of back-end tools for the agency to analyze anything around a service performance or reliability, and then operational tools that actually drive more efficient and reliable service.
-(Andy): That's great. And actually a good segway as well to next week. Our guest is actually from transit as you mentioned. So it'll be a good, I think, tie-in because we're talking today about, you know, transit data from, you know, the operator perspective more, and then next week we'll be talking a little bit more about now, okay, great, you have all this data. How do we show it to people so, you know, everyone listening or watching tune in to both these? I think they'll tie in well. So I think this is really interesting. A 30 percent like increase in accuracy is pretty cool. So can we talk a little bit more about where exactly in the transport, you know, public transportation space data actually plays a role? Is this in planning transportation routes? You mentioned operations a little bit with rider experience. Like where really does this data play?
-(Jonny): So the answer is actually a bit of everywhere, and I think that was one of our big realizations when we started the company. Most of the transit agencies and operators in not just the US but the world typically rely on systems that were built 20 to 40 years ago. They're on prem. They don't necessarily have the capability of storing or analyzing large data sets, and in fact they were built before big data and cloud computation where even keywords that we talked about. And so each of these have a wide variety of disparate datasets today. A lot of analysis happens in Excel. A lot of it is paper and pencil. A lot of it is working with consultants. And one of the things that we found when we started the company is different departments within the agency have access to different datasets. They have access to different tools and sometimes they come to different conclusions when they're looking at the same thing. And the result is that you don't have a well unified vision of what's actually happening in the network, what might be causing inefficiencies or poor service reliability, and how do we improve it. And so when we think about being a data platform for transportation, we write solutions really built for scale so we can deploy them in hundreds of cities and impact billions of trips per year. But ones that are designed to drive consensus across the agency. So we have a whole suite of tools looking at historical data for planners to help them complete planning projects, typically 90 percent faster than they could otherwise. So completing them in only 10 percent of the time. We have tools for schedulers that look at real world distributions of travel times from every stop, from every trip 24/7 to create optimized schedules based on all of these distributions that we observe. We've seen that improve on time performance by five to seven percent, just that one module improve on-time performance by five to seven percent. And then we write algorithms to process data in real time for riders to create more accurate ETAs. And one of the new things we launched this year is actually combining operational changes with ETAs. So, for example, let's say there's a detour. You can now draw that as an operations team within Swiftly, draw the detour. We have an application that sits in front of the vehicle operator, the driver, so we'll route the driver around the detour and simultaneously update ETAs that go into every app, and cancel any stop that the bus is no longer or a train will no longer serve. So when you start to combine the data that every stakeholder might interface with, and you apply some more sophisticated algorithms on top of it, you can drive tremendous efficiency and really improve the rider experience. And that's one of the things we're heavily focused on.
-(Andy): This is really cool. So really the key here is you're unifying maybe some previously disparate dataset into one platform, but you're helping kind of everyone along the way. I thought that was an interesting idea, particularly the last one you mentioned. We're helping the driver navigate the streets around construction but we're also helping the driver understand that this is how it's changing my, you know, expected, you know, arrival time. So that's really cool. I wonder then, you know, who is it that is actually using the platform, right? Like who is it that says okay, we have a problem. We need something. Swiftly has the answer for us. And how do you then, you know, actually make it easier for these people to access their data that they probably already maybe had but didn't know how to access it?
-(Jonny): Yeah, yeah. So, I mean, I always say agencies are data rich and information poor. As you said, there's a lot of data from a lot of disparate systems that different people are using in different ways. When you try to take raw data, for example, let's just say GPS, right? You have a latitude and longitude of a bus, not particularly useful data but there's a lot of it, because you're hopefully getting a lot of GPS from your fleet. How do you then turn them into useful information to make smarter decisions? Well that's where you have to apply logic, algorithms, things of that sort on top of the data. So one of the things that we do is we not only ingest a lot of data from disparate sources and have it in one central place, but we turn it into useful information for different stakeholders. In a given agency, if it's a medium or large agency, we might have 200 to 500 different staff members logging into Swiftly from different departments for different types of problems or challenges they're trying to solve. So to the agency side we have a, it's a modern web-based portal where you can log in and access a wide variety of different pieces of information. You can look at the on-time performance of the network. You can look at maybe what stops are causing delays. You can run optimizations to build new schedules. You can create heat maps of the city to figure out what intersections or specific corridors of the road are causing delays. Now. Before Swiftly, you would hire typically a consultant to do a one to two year study to capture a lot of data, to analyze it and then try to figure out what routes are running slow, or what corridors are causing delays. We've now turned that into about 500 milliseconds. You press a button. Visualizes the network. You can see exactly what intersections cause the delay. That's just the planner. That's one of many planning, typical planning use cases. We touch scheduling. Customer service too. A large agency might get 5 000 calls per day from rider saying where's my bus? When is it gonna arrive? I left my wallet on this bus but I don't remember what was there. I was at this location at this time. So we have tools that help customer service teams answer those calls typically in about 10 to 20 percent of the amount of time it used to take them. And so what's interesting is we, yeah, we take a lot of data in but then we also we process it, we turn into useful information and we create really easy to use interfaces that anyone at the agency can access to be able to make their lives better every day. And then the end result is riders ultimately get a much better experience. On-time performance goes up. Service reliability goes up. And then of course we talked about ETA accuracy that we can send to transit or other apps. We tend to find with our algorithms that is a significant improvement, so that really the whole goal is to make public transit easy, reliable and hopefully preferred mode to get from a to b.
-(Andy): This is one of the reasons I love having this podcast and talking to people like you, because when we talk broadly about, you know, this whole TDM multimodal solution out there, PO transit is obviously a key player in it. You think okay, it's complex enough. We have to think about public transit, and bikes, and car share and rideshare, whatever it is that you want to throw into the mix. But then if you just go into one of those in public transit and, you know, listeners on here who are part of public transit agencies and our operators themselves understand the complexity. Other people might not get like how many different users did you say at a single agency might use?
-(Jonny): Oh, yeah. We have some agencies with more than 500 staff in Swiftly for a wide variety of use cases and applications.
-That's crazy. So, yeah. And this points to the value of data. 500 people can access data now in a way that helps them do their job at a single agency. That's pretty cool. I'm really impressed by this whole story. It's really interesting. So this is good. We framed the idea. Data is important. If you didn't believe it now you should believe it. It's important to the whole entire operation of the whole system. So what does this actually do? You mentioned like there's a lot of improvements with how people, how, you know, transits operate or how people interact with it. But how does this then help kind of the key number which is ridership? I'm thinking like how do we actually, does this help get more people on transit and move them more efficiently?
-(Jonny): Great question. Well, I'll share maybe a few stats from pre-pandemic. The pandemic obviously blew up in many different ways in different cities. But going back to 2019, before the pandemic started, there were actually only seven agencies in the US that were experiencing increased ridership. Four of them happen to be partners.
-(Andy): Yeah. That's pretty cool.
-(Jonny): I can't necessarily, it's hard to know exactly how they were growing ridership. We're one small piece of many things that these agencies are doing, but over half the agencies with increasing ridership in the US were Swiftly customers. And what we find is that they tend to adopt Swiftly for a wide variety of different applications. And as you start to increase the reliability and efficiency of the network, as well as the passenger information quality, those things have compounding effects. So I'll walk you through a few examples. In one of the cities that we're working with they're using our runtime suggestions which is looking at those distributions of how long does it actually take to travel from stop to stop . Sometimes agencies, you know, ride the bus with a clipboard and they'll write down how long it takes and they build schedules off that. There's a better way, and you can actually, we can optimize the exact network because we're tracking it 24/7 every few seconds from every vehicle. So just by optimizing those schedule times we've seen improvements in on-time performance by five to seven percent. We launched a case that, we released a case study about a month ago in Washington DC. For example, in the US, they saw on-time performance improvements of over six percent just by using optimized runtimes. In addition to that you think about different planning use cases, designing better streets, thinking about where you should be placing bus stops, is it on the near side or far side of an intersection? All of those small tweaks can have huge impacts to on-time performance. You think about ETA accuracy. If we're able to improve the quality of the ETAs for riders, that's going to make it so that they can trust and rely on the information they're getting, and they're more likely to take transit. And then even Onboard app, which is our Android application or iOS application that can just be placed right in front of the driver. Drivers have always had this information in front of them. It's just never really been usable. So we did a few studies where we placed next to a legacy, you know, fifteen 20 year-old, they call them MDTS, where you could it was giving the driver feedback. And then we placed our just simple iOS, Android application in front of the same driver. And we saw that on-time performance improved by 5 to 7percent again. And we saw a reduction in early departures by 35 to 55 percent. And that's the worst scenario for the rider where the vehicle leaves early because they are on time. It just left and you're waiting.
-(Andy): You're seeing it going and you're like chasing it down. Yeah.
-(Jonny): Exactly. So now you think about improving the operational reliability from the operator side, the driver side. You think about more accurate schedules, streets that are being designed around facilitating smooth transit. And then ETA is for riders that reflect what's really happening in real time. So if you're doing detours, or canceling trips or adding trips, that's all factored into those ETAs. Those things have a huge implication. And I think that's one of the ways that we've been really successful as far as improving ridership, the rider experience, and ultimately increasing ridership.
-(Andy): And I think we've talked about this in previous episodes, and we'll likely talk about it again in next week's episode, we're talking about transit, is the importance of kind of on-time arrivals for buses is, I mean, it's just like people something like overestimate the wait time when they're waiting at a stop by like two or three times. And so if you expect oh, the bus is going to be here at, you know, 8.30 and then it's 8.40, it feels to you like it was 20 minutes late, when it was 10 minutes late. It'd be nice to just be able to know a) that it's going to be there at 8.40 but b) that it just actually gets there closer to 8.30. So I love that like there's multiple different things in the data space that can help get you closer to let's just get there at 8.30 every time. That's really interesting, and it all comes from the data that already exists. It's already out there. It's just using it better, is what it sounds like you're saying.
-(Jonny): It does exist. It's normally from very different systems that don't talk to one another. And so it's non-trivial just to gather these multiple datasets. But oftentimes it does exist. And then sometimes we create our own data too when we need.
-(Jonny): Especially with the application that sits in front of the driver that ensures good GPS as well. But no, what you said is so true and it's really interesting for me because I actually did not know anything about transportation going back about eight years ago. I sold my car. I moved to San Francisco. Started thinking about the transit. I'm like oh, why did I keep missing the bus? Like why is this app giving me wrong data? And then to your point around like oh, when you start to peel away layers of the onion, these are really complex networks with really challenging problems to solve. And so it's been, yeah, it's been quite a journey. But so much fun. And I think we're just getting started.
-(Andy): Yeah. Public transit is probably, to jump in somewhere in transportation you jumped into a complex one. But this sounds great. So can we talk a little bit then about the environmental benefits of this? I know like, before we got on we were talking a bit, you were throwing out some numbers that, you know, we can see from people taking more transit. Do you want to like just tell us what could the environmental benefits be that we see from increased ridership? Hopefully it's coming from more data usage. What does that look like?
-(Jonny): Yeah. Well, I can at least speak for some stats in the US where I tend to follow in a little bit more closely. But in the US the transportation sector is about a third of greenhouse gas emissions, just under 30 percent. And one of the largest and, by the way, transportation in general is the largest single contributor to greenhouse gas emissions over any other sector too. So it's really important that when you look within the transportation sector, moving people is one of the largest contributors within that. So if we think about how to drive environmental impact, a big piece of that is not having people in cars that are using gasoline to get from a to b, and ideally combining multiple people in a single vehicle for a trip so that you're sharing the environmental impact on that trip, and also reducing congestion along the way which is a huge other contributor to greenhouse gas emissions. So general stat, in the US at least, public transit saves about 37 million metric tons of CO2. And to put that in perspective that's for about the electricity consumed by 4.9 million households. So that's every household in Washington DC, New York City, Atlanta, Denver and LA combined. So it's pretty significant.
-(Andy): Yeah. No, that's an amazing stat. I don't know how I haven't heard that before because that's like, that was well put together. And if we're talking, you know, we talk about data and we say you're increasing, you know, on-time arrival by six percent here and eight percent here, and making it easier to use it and hopefully that, even if it only meant one percent more usage, we're seeing huge environmental benefits from that. That's really cool. Okay. So this is your time to shine, all right? We're on to the final question. And you just hit on a couple of these things. But like we do every episode, let's kind of summarize this. What's that key point for that key takeaway for our listeners? Why will public transit data help save the planet?
-(Jonny): Well, I think it's a great question. I think it goes very much to what I just talked about. Transportation is almost a third of greenhouse gas emissions in the country. Public transit is one of the most efficient, reliable ways to reduce that impact, because we're getting people out of their single occupancy vehicles, shifting them actually to more electric modes as transit fleets are electrified as well. And if you were to look at the largest contributors getting people out of their passenger cars into shared mobility that with electric fleets, that's where you're gonna have a huge environmental impact. One other thing I'm gonna add too is the environment is obviously essential. So is the economy. Every dollar invested in public transit it has about three dollars of local economic returns. So if we're thinking about recovering from the pandemic, the environment is a huge aspect of that, and so is rebuilding our local economies. And so I think public transit is just essential to thinking about the future.
-(Andy): This is another thing I like about this podcast, is almost every time we're talking about the environmental benefits there's always like the caveat. Well, it also helps with something, you know, economic. I hadn't heard that stat either about actually how, like that's really cool, that we can help, you know, rebuild, you know, the economy build back better as the Biden administration likes to say, yes. Via public transportation. That's really, it's really cool. Well Johnny, this has been a great conversation. I have one more question for you, so don't go anywhere. But to all of our listeners and to our viewers, thanks again for joining us. Make sure that you like, subscribe, give us a follow, give us a rating, you know, whatever you can do it helps. If you haven't yet checked out the video, we post all of these podcasts as videos. Just a little different flair, you know, look like you're doing something at work because you are, you're learning new things. Check us out. You can find the video. You can also subscribe to our email list where we send out some additional information each week at betweenthelines.io. All right, Johnny. Our final question, as our more loyal listeners might already know, we have been building a playlist on Spotify with music for everyone's commutes to work that they can listen to. And we're filling that with our guest favorite songs. What would you like to add?
-(Jonny): I would love to add Here Comes the Sun by The Beatles. It's very sentimental. It always makes me happy. And it's what my wife walked down the aisle to, so it brings a nice sunlight to my day every day.
-(Andy): I love it. And it's, I mean, it's a great song. You can't have a good playlist without that song. I feel like a good start to the day, certainly.
-(Jonny): Yeah, yeah.
-(Andy): All right, Johnny. Thanks again for being on. And everyone listening and watching, thanks for being here.
-(Jonny): Thank you so much for having me. This was a blast.
-(Andy): All right, we'll see you all next week.
-(Outro): Thanks for joining us on this week's episode of Between the Lines with Andy Keeton. Be sure to subscribe to hear next week's episode, and check out our exclusive Commuter playlists on Spotify.
What will happen when employees start returning to the office? Follow these 3 steps today to optimize their commutes.
Commuting for the Hybrid Workplace, a Movmi guest blog by our Data & Product Strategist, Andy Keeton TDM-CP.