S1E1: Behavioral TDM

Apr 22, 2021

Andy Keeton

VP Global Strategy

Between the Lines S1E1: Behavioral TDM

with Joey Sherlock


Why will Behavioral TDM save the planet?

Join us for our first ever episode of Between the Lines, as we speak with Joey Sherlock, Applied Behavioral Researcher at the Centre for Applied Hindsight, about this new and exciting field.

And check out Joey's favorite commuting songs on our exclusive commuter playlists on Spotify.

  • Castle On The Hill - Ed Sheeran

  • Speeding Cars - Walking On Cars

  • Rome - Dermot Kennedy


Episode Transcript

- (Voiceover) Commutifi presents between the lines with Andy Keeton. Each week we explore the challenging issues transportation demands 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 playlists on Spotify. This is Between the lines with Andy Keeton.

-(Andy Keeton) Today, we are joined by Joey Sherlock. Joey is an applied behavioral researcher at the Center for Advanced Hindsight. He's currently on an extended sabbatical from central government in the U.K., where he is a principal behavioral scientist with HM Revenue & Customs. At the center, he leads the government team, which focuses on using behavioral science to bring innovation into civic society and as programs with funds totaling over two million dollars. Joey has run more than 50 randomized controlled trials with governments across the world and has a wealth of experience applying behavioral science and human-centered design to difficult behavioral challenges in a wide range of fields, including sustainability and transportation. Today, Joey is going to be talking to us about why Behavioral TDM is going to save the planet. Joey, glad to have you on. How are you doing today?

-(Joey Sherlock) Thank you. It's really, really great to be here.

-(Andy Keeton) I don't think a lot of our listeners probably even know the term Behavioral TDM. They might have an idea what it might mean, but if you could just give us your own definition, what is Behavioral TDM? What does that mean?

-(Joey Sherlock) Yeah, so I guess Behavioral TDM doesn't really exist. It's something that we are in many ways trying to start. So I guess the idea really sort of took hold with us probably about a year or two ago. So we've been working for a little while now, in particular with Jessica Roberts out of Alta Planning and with Professor Ashley Williams over at HBS, and as a range of other collaborators and stakeholders as well, working on a few different projects that have been trying to use behavioral economics, behavioral science, human-centered design and all of these things we'll talk about shortly. And apply them to the TDM context to try and basically develop cool ways, innovative ways to get people not to drive, and of course, to test those using RCTs. And so that therein is what I think behavioral TDM is, the application of behavioral science, human-centered design, and rigorous evaluation to TDM. And I really think it's a new idea. And you, I mean, maybe you could go as we could be as bold as to say it's a subfield within TDM or at least an approach within TDM. Whatever it is, however it will sort of play out in the coming years is to be defined. But it's cool and it's a lot of fun.

-(Andy Keeton) The main thing that is really intriguing to me about the Behavioral TDM is this idea of bringing in behavioral science, bringing in behavioral economics and human-centered design, and then really actually building these programs to get people out of their cars. And just it starts there. So can you just tell me a bit more? Let's just kind of dive into each of those kinds of subfields within the subfields, behavioral economics, and human-centered design. Let's start with behavioral economics and behavioral science. How does this kind of concept work in behavioral TDM? How does it get us to get people out of vehicles what you're talking about?

-(Joey Sherlock) Yeah! So I guess let's start with an understanding of our definition of behavioral science, behavioral economics. So what we're trying to do here is understand how humans think. And in particular, we're sort of making this important distinction between deliberative rational, cognitive thought as a very strong facet of decision-making, but something that is also very much flawed. And so to make a distinction between what is cognitive and deliberative, rational and what is unconscious. And really behavioral economics helps us understand these two separate ways of thinking, this dual system process, and importantly, understand where we are relying on unconscious processes or processes that aren't necessarily front of mind or front or on things that we're fully aware of and understand where those processes might lead us astray. So where we might be influenced by unconscious drivers or where the environment might be designed in such a way that it pushes us in a direction that is sort of less than ideal. So behavioral economics, behavioral science, in short, gives us a stronger understanding of how humans actually think and help us design environments to account for these unconscious processes.

-(Andy Keeton) Interesting. OK, yeah, this is really interesting. So we're talking about kind of the idea of nudges and getting people to maybe not to unconsciously think about things that they may be working or choose, the kind of a rational economic choice. One of those I'm going to have to take a step back and comment on your sweater. I mean, this is amazing. So you've got a train here. You got a bus now. Are you trying to nudge me into thinking something here with your sweaters? Is this is an example of behavioral economics you are using right now on me?

-(Joey Sherlock) So it could be. This could be an example of priming where I'm looking to unconsciously prime you into a particular way of thinking. There are lots of examples of studies like this. Some of them have been sort of critiqued in the field but, you know, one classic example is, if we prime people with images of money, they are sometimes more selfish in subsequent tasks. So what I hypothetically might be trying to do here is prime you with images of a train to make you more inclined to get a train. Now, in reality, what I'm actually doing is we have a really strong collaboration and partnership with the Office of Extraordinary Innovation in L.A., which I think, by the way, is the most L.A. name you've come across.

-(Andy Keeton) Oh, absolutely yeah.

-(Joey Sherlock) And they have these sweaters and I thought, well, I'll give them some bits of a shout-out and wear the OEI sweater. So really, it's nothing to do with that.

-(Andy Keeton) But I like it, OK. Yeah. So this is really interesting OK. So we've talked, we've kind of touched on the idea here of behavioral economics. I want to learn a bit more about human-centered design, just a bit. So what can you define again, what is human-centered design? And then how can it be used in TDM?

-(Joey Sherlock) Yeah, so what we at least mean by this is we're looking to try and develop solutions and understand problems through the eyes of the user, through the eyes of the citizen, or the commuter. And so what we're trying to do when we apply human-centered design is take that user-focused lens and use it as a, you know, the tools that come along with it as a means of helping us take something from just an idea phase or just a problem statement all the way through the process of defining it, developing the idea, prototyping and iterating overtime to get to a point where we have something that seems to work in the environment that will change the behavior we're looking to try and change. So I guess in short, the way we apply human-centered design is really an approach or a mindset taking that user focus, but also then a collection of tools, a collection of methodologies that help us iterate and test and develop ideas and get all the way through to something that works.

-(Andy Keeton) Interesting, yeah. So, you know, I've definitely heard of the idea of human-centered design. I think this is like big in the tech world where, you know, I'm going to design a phone or something. I think Apple uses a lot of human-centered design in their work. This is a little different because I mean, you might be, maybe you're talking about how to design, I don't know, a rideshare system or something. But I think maybe this is more broad. It sounds like it's kind of a concept for a program. So it's a little bit different kind of in my mind. I'd like to kind of talk through here. How does this concept human-centered design and go back to behavioral economics, behavioral science, how does this actually work in the real world? Do you have an example of a project you've worked on, the kind of walk us through? So I can actually understand. I can put this in my mind and think about this for my programs coming up as well.

-(Joey Sherlock) Yeah, absolutely. So, so really think about this as an approach to innovation. You know, let's say that we are trying to innovate. We're trying to change the world. We're trying to save the planet, and we need to develop cool ways to do that. So in this context, we're trying to develop cool ways to get people out of their cars, to get people not to drive. And so we're innovators. So we're using the sort of the behavioral science, the behavioral economics piece of this is we're looking to like we said, understand unconscious drivers and develop and leverage the literature, the academic lecture that exists and develops tools and processes or come up with ideas at least that sort of account for these unconscious processes. So once we have those ideas, we're then using human-centered design in this iterative way to help us understand the context, develop the idea, iterate test and get to the point where we have a solution that is working in field. And so a nice example of this that we've been playing with, and one of the first things we did in the "behavioral TDM space" was to work on personalized routes. And so in this scenario, what we're doing is we're taking some basic assumptions of understanding about human behavior and in this case, commuting. And we're saying that lots of people probably intend to drive less, you know. Most people would agree with the idea that cycling is going to make you more healthy, that catching the bus is going to save you money or give you more productive time, that carpooling will give you more social time. So we sort of agree with this, but it's difficult for us to follow through. One of the reasons it's difficult for us is this intention behavior gap. So we intend to do lots of things and we struggle to follow through on our behavior. This is something that is well documented in the behavioral science literature. So we took that and we came up with an idea where we wanted to create personalized routes. But we would help people follow through on their given intentions by laying out, here's the way you can get to work, providing it in a timely way to help them commute by not driving. We, then, used a series of iterative methods to take that from literally us drawing things out on a piece of paper, barebones prototype one, all the way through an iterative approach where we created versions that with increasing sophistication, got user feedback, ran small quick pilots, got feedback from the mayor and high-level stakeholders, iterated tested, and improved iterated session. We've got to the point where we had something that we thought was working and then we and we haven't talked much about this yet. But then we launched a randomized control trial, which is where we're trying to wrap rigorous evaluation around an idea to see whether or not it works.

-(Andy Keeton) Interesting. OK, I like that, I really like this idea in my head is like, you know, spinning here. I've got so many ideas going through my head because this really applies to so many different programs or projects. This is why I really think, you know and why we have you on for one of our first episodes here. Behavioral TDM has such a broad kind of applicability here that I think anyone listening can think of a project they're working on where this applies. Now, certainly, if you're working on something, you know, if we're talking about something like carpooling. Carpooling is often a great solution, but it's not always a great solution. Behavioral TDM seems to be a great solution as a overarching solution to all of these different things that I really think that's interesting. But let's take a step back here and let's talk about what you were, just kind of getting to at the end of what you were saying about these randomized controlled trials and testing ideas, and finding the nudges that work, finding, figuring out kind of what is the best strategy moving forward. So, first, I want to ask you, why is this important? Why is this rigorous testing important? And then, what can we actually learn from trials and why are we doing this? What are we trying to get to?

-(Joey Sherlock) Yeah, so, look. If you have one thing that is going to change the world, that is going to save the planet, I think it is this rigorous evaluation piece. I think the ideas that we can bring in through behavioral from a behavioral lens and human-centered design and that process of innovation is really, really important. But it's nothing unless we can show whether or not these ideas work, how much they work, and then take them to different contexts and test them again. So what we're trying to do with the rigorous evaluation piece is really come from the starting place of we don't know. You know, we've developed something cool or we think it's cool. Some people have told us it's cool. Maybe we've run a small pilot and it seems to have worked, but we don't know whether it works. We don't know if it does work, how long it works for, whom it works for, if there are some population impacts, whether it has potential negative spillover effects in other directions. And so this idea of designing strong evaluation contexts, which are not always but are often randomized controlled trials and getting as close to behavioral measurement as we can, this is really, really, really important. And really what it is, is it's just bringing some strong analytical, methodological, scientific thinking, and using it to bolster some of the existing work that's being done. For us, this piece is often the hardest you know, it's reasonable to come up with lots of ideas. We can brainstorm, we could spend an afternoon and we'd have half a dozen strong ways we think we could get people not to drive. It's much, much harder to implement them and to test them rigorously in an environment. But I think it's tremendously important.

-(Andy Keeton) That's interesting. So I take it off of mobility for a second, I'm looking at these curtains behind you. I'm wondering what stage in the rigorous testing you are, and deciding the curtains on your windows here.

-(Joey Sherlock) So I should say that I've literally just moved house. I actually bought a house, one of those millennials that was forced to save during the pandemic and gave up chai lattes and spiked kombuchas and now able to buy a house, great. You are very much seeing the before picture. What will be the before, during, and after the series? So, this is baseline data. So we're collecting baseline data. We'll be prototyping intervention starting this weekend and I will be user testing those of my girlfriend. And then, I'll call you back in and we have the finished results and you can test yourself.

-(Andy Keeton) Oh, that's great. So this is your driving to work alone, forty-five minutes each way. Now we have to figure out how do we get to... How do we get you out of the car, just out of this old style of curtains here? I bet there's a better solution, I think you're there. OK, back to mobility. Can you just tell me a little bit more about what has worked, what hasn't worked is I mean, obviously when you're doing testing? Certainly, you're coming across things that you thought were going to work and didn't. Can you just tell me and you know, a concrete example here? What is an example of something that didn't work as well as you might have expected, you had to change things? What's an example of something that worked really well and then you amplified it? Just kind of once again, getting these concrete ideas in my head.

-(Joey Sherlock) Yeah, so I think, let's start and say lots of things don't work, and so and that's part of this is. we are testing to try and rule things out as much as rule things in. One paper, I'll cite. It's not actually one I was involved in, but Professor Ashley will insert a paper that was published, that showed a series of studies with a large university, with a large airport in London. I tested lots of different sorts of TDM style nudges. And I think it showed more or less flat lines across the piece, not a whole lot of impacts on any of them. And so the conclusion was this was, you know, we can run really strong evaluations and often they don't necessarily change behavior. It's really, really hard to change behavior. So I would encourage anyone to go and look up that study and read it. I think it was a 2019-2020 publication, then some of the things that do work. So the personalized route study that I was a series of studies, we talked more about those in other contexts. We think that that's working. Big caveat is we're relying on self-support data in their studies. So let's take that with a pinch of salt, I'm not convinced by it myself. We have a few other things that seem to be going reasonably well. We've been playing a lot with parking pricing and interventions around parking studies. We've been working as part of our Bloomberg mayor's challenge with the city of Durham and Vanderbilt University to run these studies. And we've got some interesting things that one thing I'll throw out, which is a quite a nudge-style intervention as we have been playing with different frames. So take a step back to that personalized route. For example, there are lots of reasons why someone might be motivated to not drive. So we have you know, you could be trying to get more exercise. You could be trying to save money. You could be trying to save time. You could be trying to reduce your CO2 emissions, your impact. And we're not really sure, particularly from those personalized route studies, which one of these is the most effective driver. We threw all of these into our intervention and just tested all of them in one go, we weren't able to isolate very well. So what we did over the summer is we ran a lab study where we worked with a really great master student named Owen Powell at the London School of Economics collaborated on this lab study where we isolated these different frames around a hypothetical experiment. And it turns out that people tell you in an online environment that money and the environment are the biggest drivers when we set up this online lab study to isolate those. We then took that into a field study with Vanderbilt University and we ran money and environment against each other. Oh, I should also say that the other thing that came out of that lab study was, if you really want to change behavior, also just put the thing you want people to do first. If you put driving as the first option, people are more likely to tell you they'll drive. If you put walking as the first option, people are more likely to tell you they'll walk. So we ran this field study, we took the results from the lab, brought, took them into the field. And so we tested control, then we tested just changing the order. You know what happens if we put the thing we want people to do rather than second. And then we had a money frame and an environment frame. And in this field study, which was trying to get people to sign up to a daily parking program with Vanderbilt University, we found that the ordering carries through. If you want people to do something, just change the order. We also found that while in the lab environment, it looked like the environment was the biggest frame or the strongest frame. In the field environment, actually, money seemed to be the stronger frame. And this is really curious. It's something we're exploring in more detail and I'll be following up on this and seeing whether it applies in different contexts. But it might be the case that people when they put in a self-support environment, they tell you that the environment is important because they want to feel like someone who is contributing to the environment. They want you perhaps, even though they don't know who you are, to think that you are a good person. But when the rubber hits the road, so to speak, a little bit more driven by money. So, yeah, we're following up on that module. But that's an example of a nudge. This is a small change to framing, which makes an incremental but significant impact on behavior.

-(Andy Keeton) Yeah, I like that example that you just walked us through because I think that took us through the whole process and everything we talked about. We start with the study. We start these ideas around coming from behavioral economics about often, something as simple as just framing and how you put the, you know, what you put first, what you put the second. That seems like it's a very simple idea. And then we take it to the field. We design something, a program here that leverages those ideas from the lab, from these understandings. And then we test it, we test it and we see what people actually are doing. And in this case, we find something that did really work well. And I hope everyone listening here, I mean, I'm going to shortly take just list driving last. I mean, this seems like the easiest solution. We're going to all we need to do is to list three options and the last one's driving. Obviously, that's not it, but I'm really excited about it, yeah. And then something that didn't work as well and as you expected, which was framing the idea around the environment, being kind of the keyframe there versus money and obviously, money coming out to be more important. I think that is a really good example of really kind of throwing it into my head a bit about how this works. We got a few more minutes, we like to keep this podcast nice and succinct so everyone can get back, take these ideas, go back to what you're doing and apply them to your everyday work. So I'd like to see I think that's I just segued myself into what I want to say, which is good. Do you have any ideas here about someone who's a TDM practitioner in the field, whether maybe they work for a company, they work for a university or a city? They're trying to come up with a way to reduce the number of people driving into work every day. They've got all these ideas. What is the strategy they should take to bring behavioral TDM into whatever kind of idea they're throwing around to try to get people out of the car? One of those is they're like a step-by-step process. There's something they should just be thinking about is that a resource they should be going through to follow up on this idea. What would you say to that?

-(Joey Sherlock) Yeah, so I think I would... The first thought is, it's really, you know, not as hard as it is as it may seem what we're pushing for here is really simple. It's let's use behavioral thinking and human-centered design methods as an approach to innovation. And then where possible, let's try and evaluate things. And really it's not overcomplicated, that's what we're trying to do. Where to start? I would get stuck into some of the initial publications so that we've put a few cases studies out on our website. We published a working paper out, I think will soon be put into the Behavioral Science and Policy Journal, which is led by Professor Ashley Williams with myself on there and some of our other collaborators, Roberts and Team. We get stuck into that and that gives a really good overview. They're also quite a few conferences talks that we've... between us, we were given an act and TRB, so I would explore those. But I think more than anything, I would say just give it a go like that. That lesson from one of the studies before, just makes it easy. I think, for example, taking that into your work and saying how can we just make it easier for people to use alternative modes? How can we even add friction to the driving process? And then how can we think about measurement? So let's try and get good data and if possible, methodological design around the programs that we're running. So it would be my initial thoughts. And of course, I would also say, like, reach out if you have a cool program. This is a very new approach. We're sort of just doing our best, getting stuck in and so reach out we'd love to share it through.

-(Andy Keeton) That's great. Yeah, I mean, I know we're going to have a lot more conversations between the two of us because I'm really excited about this. And you know, first thing that I'm going to do is I'm going to use this idea not once again, not for mobility, but just applying it to everything in life, you know, trying to find a good place to record these podcasts. And I start here and I got some weird lighting from the side, I'm just going to keep iterating. So, as you are watching, you're going to see human-centered design, this iteration in progress [inaudible] going to be moving around every week for a little bit here. OK, so let's finish off here. This is a big concept, it's a big idea. For our listeners, for our viewers, in your own words, just briefly, can you kind of summarize all of this and tell us why will Behavioral TDM save the planet?

-(Joey Sherlock) Behavioral TDM will help us save the planet because it will get us the one small baby step at a time. Applying behavioral science and human-centered design as an approach to innovation will help us come up with cool things that hopefully work, have a good chance of working, and then testing those ideas using rigorous methods, strong evaluation usually relying on RCTs will help us show whether or not these ideas work. If they don't work, we'll kill them. If they do work, we'll look to test them again and hopefully scale them so that over time, piece by piece, baby steps by baby steps, we can develop, innovate, test, and change the world.

-(Andy Keeton) All right, Joey. As you know, we've got this playlist that we're building out with our favorite songs and we love to get our favorite songs from our guests. So tell me, Joey, what is your favorite song? What is your go-to song on your commute?

-(Joey Sherlock) OK, so it's a little embarrassing, but I'm a big Ed Sheeran fan. I think we were born a day apart.

-(Andy Keeton) No, you've got to be a fan man.

-(Joey Sherlock) Yeah, he's just a really cool guy. So Castle on the Hill is one of his songs, quite upbeat, which is counter to a lot of Ed Sheeran stuff and has a particular line in there about driving really fast down country lanes, which probably isn't appropriate for the theme of this podcast. But nevertheless, that's my first song to go to. Also breaking the theme of the podcast, I'm a big fan of an Irish band called Walking on Cars who is from County Kerry in Ireland, which is where I grew up, near where I grew up. And they have a song called Speeding Cars, also counter to the theme, but a great song. So they are my two picks.

-(Andy Keeton) I love it. Yeah, OK. We'll get them added in and everyone, go listen to these songs. Joey, thank you so much for joining us today between the lines. I'm really excited to see how this field continues to grow. And as you said, for our listeners, if you have any questions, Joey's available. We can reach out to him and we'll have his information on our website as well. So, once again, thanks for joining us, and thank you to everyone.

-(Voiceover) 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 playlist on Spotify.

Better commuting starts here.

Better commuting starts here.

Better commuting starts here.