Coffee with a Researcher 5: Integrating Micromobility with Transit and other Modes (Text Version)

This is a text version of the video for Coffee with a Researcher 5: Integrating Micromobility with Transit and other Modes presented on May 25, 2023.

SPEAKER 1: And now I'll pass things off to Lauren Reichelt.

LAUREN REICHELT: Can you hear me now? OK. Hi, everyone. Glad to see you all here for the fifth and final webinar in this EEMS Coffee with a Researcher Webinar Series. This has been one part of a larger effort to better connect the Department of Energy EEMS research with Clean Cities coalitions and Technology Integration where we're trying to open lines of communication between coalitions, their stakeholders and researchers working on EEMS efforts at DOE.

So we want this webinar to be conversational, and we want it to be an opportunity for our featured researcher, in this case, Andrew Duvall, to ask for input from coalitions and vise versa.

Next slide, please. Some other efforts that were undertaken to better connect Clean Cities with EEMS, so NREL is currently looking for coalition directors that plan to or interested in attending the virtual DOE Annual Merit Review from Monday, June 12th through Thursday, June 15th, to learn about the state of DOE Energy Efficient Mobility Systems research and to help us gather insights on the opportunities and challenges that coalitions face around EEMS work.

Coalitions can receive funding to attend those sessions, document your observations, and share them with us at NREL and the Clean Cities network. So reach out to me if you are interested in participating, or if you already plan to attend.

We are also nearly ready to launch a series of EEMS educational and outreach materials for coalition directors, staff, and stakeholders. So there will be an EEMS brochure that you can share with your stakeholders, a Clean Cities University course, actually a two-part course, information resources on how to start partnering on EEMS projects and with stakeholders in your area, and a model presentation about EEMS for you to be able to accurately share about EEMS with your stakeholders. All of these materials will be published for coalition use soon.

With that, I will hand things off to our featured researcher of the day, Andy Duvall. Dr. Andrew Duvall is a transportation behavior analyst at the National Renewable Energy Laboratory. He serves as principal investigator and project lead for U.S. Department of Energy research in micromobility, rural mobility, on-demand transit, mobility electrification, and emerging technologies and practices at airports.

Dr. Duvall's current work integrates human mobility needs, rightsize technologies, and shared mobility toward improving access, energy, and emissions outcomes for communities. He often collaborates with academic partners and serves as a mentor for postdoctoral and intern researchers. And he's a graduate of the University of Wyoming and the University of Colorado. And fun fact, he also served in the United States Peace Corps in Kyrgyzstan. Happy to have you here.

ANDREW DUVALL: Great. Well, Thanks. I'm very happy to be able to talk with this audience. Clean Cities in my experience play kind of a similar role as what I experienced in the Peace Corps, and that really there's a lot of effort to engage within a community and to work with stakeholders. And so I applaud those who are involved with this and have enjoyed my interaction previously.

What I'm going to talk about today is micromobility, and I'll go into what that means in a little bit. But I wanted to introduce the project by its official title, which is Mobility Integrated Transit and Infrastructure for Efficiency, which creates the fun acronym MITIE as in small but mighty. These are small vehicles, but the intent is that they have a more outsized impact on our transportation system than might be initially apparent.

I'd also like to note that although I lead this project, it's really very much a team of teams. My team here at NREL as well as integrated with other teams at Lawrence Berkeley National Lab and Argonne National Lab. And we have a variety of other partners outside the lab system. I'll touch on them as we go along, but this work is representative of a lot of people's effort.

The overall objectives of the MITIE project are several. I've highlighted the key pieces in this slide. Really, it's to get an understanding for the energy outcomes associated with micromobility at a top level. This is something that hasn't been pursued previous to this by the DOE. Most of the literature that exists is very local or regional in scale and dependent on a lot of things that we are trying to clear the hurdle in order to be able to get a view of what micromobility in a broader sense might mean for the national scale.

Also, its aim is to characterize the multimodal connection between micromobility with traditional forms of transit like bus and rail, as well as some emerging modes that do have some relationship with smaller vehicles.

We are also exploring mode choice and what that means to an individual, and how that might inform design and development of a system to integrate more access for micromobility options. And as well as how infrastructure and other elements come into play.

Much of what we'll talk about today is focused on shared micromobility, but there is also a component of private ownership micromobility. However, from the standpoint of improving and optimizing shared micromobility operations, we've been looking into what those needs might be, and I'll go into those in a bit more detail in later slides.

And then looking at just the energy needs, energy demands, and other characteristics associated with micromobility vehicles that have not been well explored. One final element is what we term microfreight, which is essentially human powered possibly electric assisted smaller vehicles that can be used for delivery and other purposes. Anyway, that's just kind of a highlight of what I'll be talking about today, and we'll go into depth in most of these areas.

So just to have a common definition, micromobility is a recently emerged umbrella term to kind of capture these smaller vehicles that are human powered electric assisted. In the context of the work that we do on this project, we're including e-scooters and e-bikes, but also manual bikes, that's the initial large scope of micromobility included primarily shared manually powered bikes.

And additionally some emerging types of vehicles such as seated electric scooters or mopeds, an example might be in the lower right of the slide. The Revel scooters that can be rented in a number of different locations in the U.S. now. They are– function a little bit differently than either standard scooters or bikes, and so, they, kind of, merit their own approach.

We've also been considering access variants primarily the shared systems that I've noted in earlier slides. But also that, micromobility also includes the vehicles that a lot of people already own that might be in your garage or apartment. So using this as, kind of, our framework to talk this through, this is where this project is centered.

So the next couple of slides, if you'll indulge me a little bit, are to provide a little bit of context where micromobility has come from and where it is now.

Micromobility isn't exactly a new idea. It arguably is as old as the first bicycle that was developed. But really in the context of framing micromobility vehicles as readily accessible, easy to use, typically lower cost, and lower energy need vehicles for urban mobility. This is just an example of one of the micromobility devices that was developed in the early part of the previous century.

The Autoped scooter was patented in 1916. And for a brief period before cars, particularly the Model T became more affordable and accessible to a broader population. These became popular for much the same reason that e-scooters have been popular a century later. They were easy to store. They didn't cost a lot as compared to other options. They didn't require a lot of fuel, and they were generally viewed as being pretty good solutions for getting around in an urban setting.

This became part of the vernacular for a while. It was not unusual to see these types of vehicles appear in publications and books or be referenced in various settings. For example here, this would be a decade or so on from their introduction, but Amelia Earhart is highlighted as being on her motorized scooter.

And so this became part of the mobility fabric of primarily cities and urban areas for a few years and kind of faded out with the advent of the automobile, but then came back into some type of focus during the Depression and later into World War II.

So, power-assisted bicycles basically were what motorcycles started out as. The very earliest motorcycles were just bicycles with an electric– or a gasoline motor fitted to them. And they actually fairly quickly evolved between the early part of the 20th century, into the 20s and 30s, to being more or less what they are now, just more powerful, larger, more substantial vehicles.

But there was also a parallel evolution of these just simpler motor-assisted bicycles that did persist as the motorcycle developed, especially after World War II in Europe. These were some of the first vehicles that many countries produced, and helped people get back on the road. And in the U.S., they were viewed a little bit differently. The Whizzer, example here on the left, is more focused market-wise on younger people, and even kids.

On the right, this vehicle, the Solex or VeloSolex, as it was called, is a lot more prevalent in Europe, but did have some presence in the U.S. It actually became part of the popular culture at the time in various settings. There were a number of different celebrities that became aficionados of the VeloSolex and then later more powerful mopeds.

But this was like a period of time in about the 1960s, maybe up to the early 1970s where this power-assisted bicycle really seemed to have a connection with people for various types of use. And especially in the developing world, this is where some of the first motorized vehicles were put in application. They were inexpensive, easy to operate, easy to service.

So that's a little bit of the background of what we'll be talking about today. Where we are now, the current micromobility landscape does include functionally some of the same vehicles that I just highlighted. But they are shifted to use an electric drivetrain for the most part.

Most of these vehicles have been outselling EV car sales the last few years. Data on e-scooters is a little bit challenging to pinpoint. But data on e-bikes, I've been able to trace that they outsell EV cars by a substantial margin and have in the last couple of years.

Micromobility where it has been shaped in the last 10 years or so is really that shared access point. The ability to just pick up a bike on the street, or a scooter on the street fairly cheaply and easily and use it for unplanned trips or in conjunction with transit. We observed during the pandemic that there was a marked decrease in transit use. Also, there was a marked decrease in micromobility use– shared micromobility, but that has recovered a lot more quickly than transit has.

Thinking about all of this, through the context for shared micromobility, the latest estimate is somewhere around a quarter million vehicles in operation in shared micromobility systems in the U.S. as compared to privately owned bicycles. And again, I don't have data on privately owned scooters, but that is– there are about 400 times more privately owned bicycles than there are shared micromobility vehicles.

So just– I use this as a way to frame and interpret the subsequent slides that I'll go through that are primarily focused on shared micromobility. But the potential for access and use of privately owned micromobility vehicles is substantially larger. However, one of the limitations in understanding how those shared micromobility or the privately owned micromobility vehicles are used is that they don't generate data in quite the same way as the shared systems do.

That said, getting into the meat of the work that we've been doing in the MITIE project. We've really been examining how access to micromobility and shared micromobility in conjunction with transit can shift people's behavior when it comes to selecting a mode. And in an attempt to understand what that looks like from an energy and emissions standpoint, we put together a paper, it's published in Transportation Research Part D that you can look up.

But we found that between about 1% at the national level and about 2.6% at the city level, savings and transportation energy expenditure is possible through shared micromobility. To bookend that, we're working on exploring what that looks like if translated to the much larger fleet of privately owned micromobility vehicles. That said, the likelihood that someone is going to choose the bicycle in their garage is dependent on a lot of different factors, such as infrastructure that might make them feel more comfortable using a bike on the street, and mixed traffic, or whether there's air in the tires.

So there are a range of different factors that come into play when people consider what they– whether they might use a micromobility vehicle for a trip. One of the evaluative tools that we've used is the Mobility Energy Productivity metric. This is something that was developed by colleagues of mine here at NREL in looking at the quality of mobility afforded by various different modes.

One of the key findings we found in association with e-bikes specifically is that they can provide about as much as about 80% of the quality of mobility that a car can provide. That means roughly translated that it's possible to reach about 80% of the destinations that a car can reach in a similar time, but at a lower cost when comparing e-bikes and cars.

And so looking at the map that's on the right, this is central Denver, and especially in a dense urban area in central Denver, e-bikes are very competitive with cars, both in time, cost, and ability to access destinations. There may even be some advantages and easier location of parking spaces and things like that an e-bike might have over a car.

Connection with transit. We've seen that shared micromobility, multimodal trips that are conducted maybe as a first or final leg using micromobility to start or end, and using transit as the longer distance segment in the middle of a trip really enables people to choose to leave their car at home in a lot of different cases. So the connection between transit and micromobility, particularly shared micromobility, is fairly strong. Something that we are modeling with our partners at Lawrence Berkeley National Lab. They have developed something called the BEAM model. The acronym escapes me for the moment.

But really what it is is looking at the ability for agents within the model to move from point to point, and they can set different variables within this model. And we've found that when shared micromobility options were available at the beginning or end of a longer distance trip, that, that makes transit more appealing for serving that middle longer distance part of a multimodal trip.

So the collaborative joint use possibilities between micromobility and traditional transit is fairly strong. This is something that operators have identified as well where many micromobility systems have close associations, station to locations with transit stops, things like that. Dockless micromobility where there aren't set stations also tend to concentrate availability of their vehicles at transit locations.

So that interconnection micromobility serving as a feeder mode is something that most transit systems are encouraging. Just a time check here. We've been looking also at some of the patterns of use of shared micromobility systems. The graph here on the right shows some of the fluctuations over time of shared micromobility fleets in a number of different cities. How they're used temporarily has a lot to do with the prevailing temperatures.

And we're also, in talking with some of the operators found that the change from daylight savings time to standard time has an effect as well. Later in the year, when the months are colder, and it gets dark earlier, we have observed less use, generally, in shared micromobility systems.

There's some variation with that. Some systems do actually close down in winter. But this is using some data that are generated through some participating shared micromobility systems and trying to get a better understanding for how to plan for systems, how to help operators, maybe identify the number of vehicles that they should have out in summer versus winter, and that sort of thing.

We found that higher large– or larger high-density systems appear to have higher use that's more rides per vehicle per day, and they're used for shorter and faster trips. And we've also found that they require less re-balancing. That's the repositioning of micromobility vehicles within the system where they are currently located and where they might need to be located.

E-bikes, also when they're present in these systems tend to be used for longer distances at higher speeds, and are more frequently used than pedal bikes. So it suggests that they're a little bit more popular, as you might imagine, if you have electric assist, and you might arrive at your destination a little less sweaty, maybe a little less tired, and just makes it for an easier experience. And so the advent of e-bikes and e-scooters and these systems suggest that they are an attractive alternative that can help people choose micromobility over other modes.

So understanding mode choice is a big part of the work that we do. Why people select a mode for a given trip is a key element of our research. And in order to do that, we've developed a mode choice model and something called a fundamental influencing factor model that can, independent of a specific mode, look at characteristics and help us understand which of those factors are more appealing to one demographic group or another. Such as the need for physical activity for scooters or bikes, does that appeal to one group more than another. Maybe there's an age associated factor there.

And so this has developed in part because initially in 2017, 2018 when e-scooters started to show up in a lot of different places, a lot of transportation planners and researchers were caught off guard. We didn't quite know how to think about these new vehicles and didn't quite know how people would use them or who would use them. And so, this development of this model is aimed at trying to understand that a little bit more, and also to anticipate potentially modes that have not been created yet or on the horizon or different practices or implementations of existing modes.

And so we can take these characteristics associated with the modes and use the model to help us understand who might use them, and for what types of trips. Just a note on the bottom right, a couple of charts here showing that the dew point, for example, is something that comes into play when people select a mode. Essentially, is it raining or is it not?

And e-micromobility, electric micromobility, is affected less by rain than non-motorized bikes and walking. So the selection of the mode appears that if you can get from point to point a little bit more quickly, even if it's an exposed on an e-bike or a scooter, it might be a little bit more appealing than traditional options.

Another facet that we've been looking at is access to shared micromobility in the context of a setting, and access to transit comes into play here as well. And what it tells us about what that access time is to reach a shared micromobility vehicle as to whether it appears to be equitable– equity of access for people who might choose to use one– a shared scooter or bike.

And what we found looking at this, as an example, this is Washington, D.C. We found that it's fairly equitable as far as access across a range of different neighborhoods within the city to be able to access a shared micromobility vehicle. The access time tends to be less than five minutes in most locations. There's an outlier here. This is Capitol Bikeshare, that is a station-based micromobility system. And by the way that it's designed and the outlay of its stations, takes a little bit more time to get to as compared to dockless systems. So this is a way to evaluate how ready the access is within different geographic settings within different neighborhoods.

As I mentioned before, there's a need to reposition micromobility vehicles, shared micromobility vehicles within a system from where they might happen to be to where they need to be. Sometimes stations empty out and all the vehicles are gone. Sometimes stations are completely full and there's no place to park a vehicle.

And so the systems have to reposition either bikes or scooters within that setting. This applies both for docked, station-based systems or dockless systems. Just the need to reorganize the vehicles within the service area. And this is the biggest energy input for any shared micromobility system is, these typically larger vehicles, trucks or vans or with trailers to move the vehicles around.

And so the challenge here is many systems don't keep track of the energy input required to make this happen. So we've been able to use the general bikes year feed specification data to assess which portion of trips are being taken without a passenger aboard. These are when the vehicles are moved from point to point, but not having been checked out by a user.

And we have identified those types of trips as that movement within the system. And looking at this there's a fairly wide range of trips within systems that are repositioning trips, ranging between about 7% and 19% of trips, and a similar amount of vehicle miles traveled.

Those larger denser systems like the New York City, city bike system appear to have less need for this redistribution. So most of what I've been talking about up to this point has been primarily focused on shared micromobility. And that's in part because most of the systems that operate, generate data every time someone checks out a bike or a scooter, and that data goes to a repository somewhere, and the data set is accessible, and we can request it and evaluate that data.

However, as I mentioned at the beginning, there are about 400 times more privately owned micromobility vehicles, and the insight into how those are used is very limited. Recently, we have developed a tool called OpenPath, I have a slide on it in a few slides from now, but it's an app-based tool that enables a system to collect background data on people using various modes of transportation.

And this is a tool that we've used to gain insight into how privately owned micromobility vehicles are used. The example here that I have a few slides on is the Colorado Energy Office developed a program starting in 2021 offering e-bikes at no cost to low income people in certain settings. This was initially started at a mini pilot scale with about a dozen users and increased to a much larger pilot scale with 200 or so users generating on the order of 100,000 plus trips.

And so we have a pretty substantial data set now that we have collected through the OpenPath platform that we can better understand how people are using micromobility vehicles. These are not shared, these private ownership micromobility vehicles, and what they're using them in place of.

The graph on the right shows the types of vehicles that are replaced by e-bike use. The biggest bar on the bottom is replacement of single occupancy vehicle car trips. So it's pretty substantial, the energy benefit. We ask each participant for each trip if you didn't have access to an e-bike, what mode would you have chosen instead? And so those that respond SOV car trip are more than any other single-replaced mode.

There is also a replacement of bus and other transit shared mobility options such as a car with others or a taxi of various types. There are also trips that are generated that would not have otherwise occurred. The no-travel option or the replacement of non-energy intensive modes such as walking or a manual bike. And so, even when we factor in those induced trips, or the replacement of non-motorized modes, the net energy benefit is considerably larger. So the green is good and the red is bad in that graph.

The graph on the left, looking at how people travel in the spectrum of different modes, they choose is part of this project as well. So we collect information on all trips, not just the trips that are conducted by e-bike. We found that about 30% or so of all trips, the e-bike is selected. And one people are also using other vehicles, including car drove alone and car with others and a spectrum of other things.

But the access to these e-bikes have meant that many of the trips that they are currently taking are replacing some other typically larger vehicle with an e-bike and much of the way that they're using the bikes is similar to how we observe them using cars. That is, point to point. The private micromobility trips are different from the shared micromobility trips in that sense. There are fewer private micromobility to transit multimodal trips that are taken that we've observed.

As far as purposes of the trips in this program, we've identified that a lot of them– this shows results for both the mini pilot as well as what's called all programs, which is the larger pilot that was operated at six different sites across Colorado. A lot of the trips have either work or home as a destination. And so the vehicles are being used for utilitarian purposes.

This is one of the early questions that we had and some of the criticisms that we anticipated was that, OK if you provide e-bikes to people, they're just going to take more recreational trips, and we've found that not to be the case. The spectrum of different trip purposes mirrors fairly closely what we see in things like the National Household Transportation Survey, other sources that suggest that for any individual, there are spectrum of different trips that they take and the trips taken by e-bike mirrors that fairly closely.

Even the old programs bartered here, it actually shows that comparatively the e-bikes are used more for work and home destinations as compared to including all other mode options. So we're interpreting this as access to the e-bikes have helped to satisfy the mobility needs of the people who are participating in the program.

Another aspect that we've been looking at is what distance of trips are people using e-bikes for within this program. And hopefully, this is not too difficult to absorb this graph, but looking at the trips that are between 0 and 1 mile, so fairly short trips. The preferred mode for those short very short trips is walking, and the purple there. E-bikes in the red shows up as well as car and shared curve or even those short distance trips.

However as we move between about the one and four-mile distance, the e-bikes are actually used more than other modes for those trip distances. Beyond about four or five miles, e-bikes fade away and more of the trips are conducted via car or shared car. But this suggests that the participants in this program are using e-bikes for a range of different types of distances of trips as well as purpose of trips that they are really finding a place to include e-bikes as a mode option for a wide range of trips.

A little bit more about this OpenPath tool. I'm happy to talk and introduce you to the people who have developed this tool on our end. We don't view this as just a tool to understand e-bikes use. It really is something that's very multimodal and intermodal. We can look at how people travel between modes, which is something that is very difficult to do using other data collection methods. But in any case, this is something that we've explored and refined using the Colorado Energy Office project as a testbed, and we are now scheduled to implement use of the OpenPath tool for the broader state level Colorado program as well as programs in California and other locations.

So there's a lot of interest in understanding how people might use e-bikes and informing programs to provide assistance to purchase them or in a variety of manners trying to understand how access to micromobility overall can improve mobility options, especially for people with the least choices currently.

Going back to some of the core elements of the MITIE work that we've been doing. Looking at the energy intensity of micromobility vehicles is something that has not been explored to much of a degree to this point. It's included in the MITIE effort because we identified that gap and understanding. This is equivalent to putting a car on a dyno, and understanding its fuel efficiency. Currently, there is no equivalent for an EPA miles per gallon rating for e-bikes or e-scooters. That's something that we're hoping to change, but this is kind of a first step and just putting some balance around what the energy needs are for these vehicles.

So this work was done in conjunction with our partners at Argonne National Laboratory. We developed a data collection tool and installed that on a representative vehicle both an e-scooter and an e-bike, and found that the e-scooter is about half as energy efficient as an e-bike. This is not factoring in human muscle power input. Both of these vehicles were tested using just the throttle input. So essentially costing.

But part of the reason that we believe that there's a difference in the performance of these vehicles is that the larger wheels on the bike present reduced rolling resistance. So from a physics standpoint, it improves the performance of the vehicle. Thinking about if people were to add muscle power into the 8.4 Watt hours per mile that we're estimating, the e-bike to need would be reduced, if there's muscle input.

That said it's also dependent on the weight of the rider, the weight of anything that they might be carrying, and the inclination of the road. So this is not to be meant to be hard numbers, but just a comparative energy intensity for these vehicles.

To put this a little bit in context, so 8.4 Watt hours per mile for an e-bike and 19 Watt hours per mile for an e-scooter, a typical EV car requires about 250 Watt hours per mile to operate. So it's still considerably more efficient as compared to those other vehicles.

LAUREN REICHELT: Hey, Andy. I wanted to make sure we had time to get to questions. So maybe one more slide, and then we can open it up for some discussion?

ANDREW DUVALL: Sure. Sure, OK. I guess, I'll just note, these are some of the cargo vehicles that I talked about at the beginning. Maybe for brevity, I might skip past this section.

I just wanted to note that the next big thing that's appearing for micromobility is incentives programs. Denver, Colorado has been one of the leaders in this effort. They're hugely popular the monthly allocation of rebates are usually claimed within about 20 minutes when they're offered at the beginning of each month.

And this is leading to a much more substantially supported $12 million statewide program that will start a little bit later this year. It is informing the development of similar programs elsewhere.

I think I'll skip this one as well, and maybe just pause here questions that I have for communities and maybe that can lead toward discussion and questions.

LAUREN REICHELT: Thanks, Andy. We had a lot of activity in the chat, so I want to be sure to call out some of those questions, first.


LAUREN REICHELT: Catherine Orleans asked if there's research on places that don't have these weather issues like the seasonality issues and whether there are other barriers that have been identified for mode shift specifically in the case of visitors versus residents as well, which as someone who was the former Hawaii Clean Cities Coalition director, I also find that very interesting.

ANDREW DUVALL: Sure, certainly. Well, there are some places that do have the benefit of not having a snowy season. We have actually been working in collaboration with a university in Puerto Rico who is very interested in understanding how micromobility vehicles might be integrated to solve some of their problems of mobility. One of the biggest barriers in those settings are that there isn't sufficient street infrastructure where people feel safe in mixed traffic with larger vehicles. So that would be a major barrier in places where there isn't much of a weather challenge.

Whether it goes in another direction as well, I've been working with interested organizations located in the south. And particularly, in the summer, it's hot and very humid, and that poses a challenge to people choosing micromobility over other options as well.

But really, I guess, to get back to the question, beyond weather, it's infrastructure, and it's policy that may support people choosing micromobility to ensure that they feel safe or have a safe environment to ride it.

LAUREN REICHELT: Great. Yeah. Safety is certainly top of mind for folks. The infrastructure piece, I think, really plays in there too, which is what can we do to help mitigate that issue through infrastructure. There is also a question about how organizations– how other organizations can access shared mobility usage data. I know Tom jumped in to share that some of the data that you shared is available online publicly, but do you have any other thoughts on where organizations can find additional micromobility data, whether it's from a third party or from a different national lab?

ANDREW DUVALL: Well, I can point you to my colleagues at Lawrence Berkeley National Lab who have a repository for the general bike share feed specification data. That's open data. That's accessible through an API, but you kind of have to know how to navigate that type of system to be able to access the data.

So although it's publicly accessible, it's not immediately available to at least some people who might find it valuable. There's also NABSA, the North American Bikeshare and Scootershare Association that I might point you to. They have some resources, and they worked in conjunction with a group at University of California, Berkeley, that has some focus in this area.

There are typically some opportunities in different settings to talk to a shared micromobility operator individually and ask them questions. They might not just hand you a data set, but they might be able to answer questions based on data that you might have for a specific setting. But I'm happy to follow up with any questions related to data and point anyone in the right direction.

LAUREN REICHELT: Yeah. That's great. Maybe you can follow up directly with Julio after the webinar.


LAUREN REICHELT: I don't see any other questions in the chat right now. So if anyone else has questions, please feel free to jump in.

ANDREW DUVALL: I guess I could also start with a question that I might have for the audience. What am I missing? What could be of value for a community considering to support micromobility, whether it's shared micromobility, or just to support private ownership type micromobility, what can I or my team do to help answer questions that you might have?

And if it's in the chat, I can't see the chat at this point.

LAUREN REICHELT: No one's responded in the chat, yet. I would love for someone to come off mute. Oh, I see you, Barry.

BARRY: There's always me. It's a wonderful presentation. My question relates to insurance. Is insurance– I'm in New York state and insurance is a huge issue, both bike share and car share. So we've actually had one of our stakeholders car share shut down temporarily because the insurance is just not available to them. We're trying to get a law changed here. Is that an issue in other parts of the country?

ANDREW DUVALL: It is an issue in other parts of the country. And I guess to put it in context, I assisted in planning what became Denver BCycle, which was one of the first city scale bike share systems in the country. This was starting in 2008 or so when the idea started to formulate, and insurance came up immediately as a question.

And at that time, there were no insurance providers for shared micromobility systems. And what it took was collaboration with an insurer to describe what the program was and what the risks might look like. And from there, they developed a policy. My understanding is that has developed into more standardized industry approaches. I can't specifically say what it looks like for a shared micromobility operator, as far as insurance goes at this point. I know that there's been a consolidation in the industry. Many of the currently largest micromobility operators are Lyft or Uber or others that for which this isn't their core business element.

And so I think, they have approaches to insurance that are a little bit different than, say, a municipality that's interested in putting together a shared micromobility system. So I don't have a great question for that– or I don't have a great answer for that. I probably have more questions. But it's something that I think maybe the charter-level insurance for a community might be a way to start to address this, if you are thinking of developing a shared micromobility system on your own.

As far as insurance requirements for private micromobility, that's an entirely different set of questions. The insurance industry has adapted to the point where you can go out and get comprehensive coverage and liability insurance for your e-bike pretty readily. It's somewhere between about $100 and $200 a year from my probing into this. So from an individual level, that's an area in the insurance industry that seems to be emerging. So I don't know if that really gets to the point of your question, but happy to explore that as well.

BARRY: Well, that's good. Thank you so much.


LAUREN REICHELT: Thanks, Barry. Catherine did mention in response to your question, Andy, when you were talking about gaps, that she would love to see more data around visitors versus residents.


LAUREN REICHELT: Especially for tourism and destination management, they're looking at shifting visitors into more sustainable modes.

ANDREW DUVALL: And sorry I have this cough, aggravated by smoke, but in any case, yes, the differential between residents and visitors in a setting, it's a challenging question that probably has a lot to do with the context. So if we're thinking about, say, for example, a beach town or a mountain town where there's a large constant influx of visitors, and in many cases, those locations are kind of space constrained shifting people onto smaller vehicles has the advantage of removing some cars from traffic.

It also has the advantage that it makes it easier for people to spend money, interact with businesses. And there's, I think an appreciable difference in the experience when you are visiting a place on the back of a bike or a scooter as compared to traveling through it on a car. So from that standpoint, I can see where you're coming from, the approach I think, is to make sure that micromobility is as accessible and easy to understand, and the rates and costs associated with it are simple.

During some of the early years that I was working with advising bike share systems, we found that the variability of time for charges incurred for checking out a bike for a certain period was fairly confusing, especially for people who weren't accustomed to the area. And so, streamlining that access and cost element is probably a key way to get someone interested in using micromobility as a tourist or a visitor.

People who are residents of an area probably take a somewhat different approach, though they may be more focused on using shared micromobility for commuting or other regularly occurring trips, and so may have a bit different of a need. And so, I think the way that some systems have developed are kind of tiered costs, if you're able to pay for a week or a month at a time as opposed to a visitor who might be interested in just a day or a few days. Having those different options available help serve those different population groups, I think.

LAUREN REICHELT: So we have two other questions in the chat, so maybe a quick speed round with these last few minutes.


LAUREN REICHELT: So, Tanya, I missed her question earlier, they're looking at long-term rental, or rent-to-own programs that can make it easier for commuters to try e-bikes before buying, and then pay them off over time. Are there similar models that you know of out there, and do you have any suggestions around that any challenges? It sounds like they had tried a voucher program, but it didn't get approved.

ANDREW DUVALL: Well, the rent-to-own program in Colorado has been really successful, and in this pilot scale project. It's about to explode into a much larger scale project that we will also evaluate and hopefully have better answers for, but, yes rent to own, I think, is a good solution to provide mobility access especially to people who are interested in e-bikes, but the upfront cost might be a little beyond their reach.

LAUREN REICHELT: Wade, do you want to jump in?

WADE BRYANT: Yeah, please. Thank you. I'm Wade Bryant from General Motors Advanced Design. I lead the team that has been looking into sort of innovative mobility spaces for years that led to our [INAUDIBLE] e-bikes or maybe car sharing or [INAUDIBLE] delivery things. So we're always on the hunt, looking for new opportunities to develop new types of mobility.

This presentation has been phenomenal. And I definitely would like to follow up to learn a little bit more to see what other maybe gaps there might be that we could address. And I see that this call is probably chock full of experts.

So I just wanted to make sure that there was some ability to follow up and talk more.

ANDREW DUVALL: Sure. We'll definitely, Wade. I just put up this slide with my email on it. I'm happy to talk with you exploring new vehicles, new modes, new applications, is definitely along the lines of the team that I'm on. I'm on the mobility innovations and equity team here at NREL, and a lot of what we are doing is trying to anticipate what's on the near horizon. Things that may not exist yet, but we've identified needs for, so happy to follow up.

WADE BRYANT: Wonderful. Thank you.

LAUREN REICHELT: And we can finish it off with a very loaded question that we certainly cannot do justice in one minute. But Albert asked in the chat, do you have any research or insights into planning for public e-bike charging infrastructure?

ANDREW DUVALL: Yes. That is not something that I covered in this presentation, but is definitely on our radar. I can't adequately cover it in the time that we have left. But please do contact me. We are looking particularly at public access charging for e-bikes or other small vehicles in the context of workplace charging primarily in dense urban areas where people may not have the ability to haul their bike up to their apartment to charge it, or for any number of reasons apartments might not allow it.

So the opportunity for a very low cost, very low demand, on the grid capability to charge e-bikes is something that we are exploring.

LAUREN REICHELT: Yeah. And I think that was a great lead in. So if anyone on the call wants to have additional discussions with Andy, if they have questions, they want to be directed to research, insights, anything at all, his email's up on the screen. We very much encourage you to do so.

ANDREW DUVALL: We have a number of publications, I'm happy to point you to as well.

LAUREN REICHELT: Perfect. Anything else? Any last comments, Andy?

ANDREW DUVALL: No. I'm just looking forward to looking through the chat, and seeing what I missed. But really we are very delighted to be able to talk with anyone who has an interest in micromobility, and find out how our work can help inform your operations. I'm very interested in translating our research to practice.

LAUREN REICHELT: Thank you. And with that, we will close out this webinar, and this webinar series. So thank you everyone for attending.