Coffee with a Researcher 1: BEAM CORE – Agent-Based Model to Simulate Transportation Behaviors on a Regional Scale (Text Version)

This is a text version of the video for Coffee with a Researcher 1: BEAM CORE – Agent-Based Model to Simulate Transportation Behaviors on a Regional Scale presented on Jan. 26, 2023.

LAUREN REICHELT: Great. Thanks, Cass. Hi, everyone. Thanks for joining for the Coffee with the Researcher webinar series. This is part of a larger effort to better connect the Department of Energy EEMS Research with Clean Cities coalitions and technology integration. So we really want to open the lines of communication between Clean Cities coalitions, their stakeholders, and researchers that are working on EEMS efforts.

So we are undertaking a set of activities to build those bridges. And we've been convening in EEMS working group of Clean Cities directors to help us shape these efforts. So, a couple of things that you can be looking forward to will soon be publishing an EEMS Clean Cities University course, a brochure about EEMS for coalition staff and stakeholders.
A short document that outlines early steps that coalitions can take to build relationships with EEMS partners and start considering EEMS projects. And we'll also be releasing some template PowerPoint slides that can be used by coalitions. Next slide.

And then there's what we're all here for today, which is the Coffee with Researcher EEMS webinar series. So these sessions will highlight available EEMS tools and insights and models for a deployment audience of clean cities directors, staff, and stakeholders. We want these sessions to be very conversational.
Clean Cities coalitions and their stakeholders are in a really unique position to describe the local, and the regional mobility priorities, and challenges in their communities. And researchers can use those insights to enhance EEMS research.

If you haven't had a chance yet to register for upcoming webinars in the series, we encourage you to join us. It should be a really great lineup of diverse EEMS topics. Cass will share the registration link in the chat for you all.

Today's session will be featuring Dr. C. Anna Spurlock Dr. C. Anna Spurlock is a Deputy Department Head and Leader in the Sustainable Transportation Initiative at Lawrence Berkeley National Laboratory. Dr. Spurlock is PI of a number of large transportation modeling projects, including the work conducted by LBNL for SMART Mobility, which is funded by DOE Vehicle Technologies Office Energy Efficient Mobility Systems Program.

The geoeconomic multimodal systems model being developed by for the Federal Highway Administration Office of Transportation, and the benefits of infrastructure in large scale deployment air quality, which is funded by the US Department of Energy. Dr. Spurlock has expertise in decarbonization policy, equity, behavioral economics, and transportation behavior, and systems modeling.
Dr. Spurlock will be presenting today on BEAM CORE simulation model, and we'll share a bit on what the capabilities are, and how to engage, and leverage those capabilities. Before I hand things off to her though, I do want to get a quick idea of who's in the room. So we're going to do a really fast Zoom poll.
So if you can all respond to this question, what type of organization do you represent? So are you with a Clean Cities coalition? Are you with a local government, a local or regional planning organization, a utility, or a fleet, or are you with someone entirely different? Give you a minute to respond.

All right. Primarily Clean Cities coalitions. A few others which might be National Lab folks. All right. That is helpful for us to know, and also helpful for the presenter to know. So I appreciate you responding to that. And with that background, I will hand it off to Dr. C. Anna Spurlock.

C. ANNA SPURLOCK: Awesome. Thank you, Lauren. So I switched to my headphone mic just because I had some background noise. Can you hear me OK?

LAUREN REICHELT: Yeah. You sound great.

C. ANNA SPURLOCK: Great. OK. Let me share my screen. All right. Well, thank you so much for having me today. This is great.

I'm very excited to talk about this capability that we've been building for a long time, and talk about what it can do, and how it can help you. And I think there's a lot of exciting opportunities there. So basically, where this came from was it's funded as Lauren mentioned by the Department of Energy EEMS program, particularly the SMART Mobility Consortium, which is a consortium of national labs that was convened under the EEMS program to do work with a focus on transportation as a system of systems.

And this was a deviation. The whole EEMS program in some sense was a little bit of a new area for the vehicle technologies office to focus on Department of Energy, because historically, there was a lot of focus on the widgets or the technologies themselves like batteries, things like that.

So the idea of focusing on transportation as a system of systems and developing a comprehensive set of tools that really let us understand the interaction of a lot of different dynamic factors that contribute to outcomes in terms of the transportation system and also related systems was new and a big lift. So there was a first round of SMART Mobility work, where the concept of a SMART Mobility workflow was developed, which was the integration of transportation system models with other types of models to create this integrated system model.

In the second round of SMART Mobility, which we are in the last year of right now, we rebranded that as BEAM CORE for our version of this SMART Mobility workflow. So what that is BEAM is an agent based transportation system model, which basically means it's very large, very detailed. It has a representation of all of the transportation modes in the system all of the networks, including the road network, the transit network, rail, et cetera. A lot of new and innovative modes like micromobility, ride hail micro transit, and the ability to simulate different configurations of those things.
And basically, resolves a set of constraint resources in a system based on the amount of demand coming in people trying to accomplish things on these different modes. And that's integrated with a set of models that capture land use, vehicle adoption by households, and the holding of different vehicles, and their technologies, energy modeling for the outcomes in the system, and greenhouse gas emissions.
We also have everything in parallel, which I'll talk about in a moment to model the whole Freight System and shipping. So a whole range of things that feeds in this integrated way, in this simulation environment that is what we call BEAM CORE.

So the use cases, and this is in no way meant to be exhaustive, but it's the way that I've found it's most easy to talk about a concrete waste that this type of a capability can be useful to a city or region. There's two main ways that I think about it. One is thinking of it as this digital twin is the one word that gets used.
So can be used for scenario analysis or A-B testing, to understand what to expect for alternative, potential policies, or implementation strategies for deployment of infrastructure, or certain types of policies in the system ahead of that deployment. Another is focusing on understanding what the implications of scaling something that you do have more knowledge about. So if you've done a more limited pilot.

And there's data that can inform the outcomes of that pilot. We can represent in our model what that looks like scaled to the whole region, or to a different component of the region and different sub region within the region, et cetera, different deployment strategies informed from a more limited pilot. So just to create some concrete pictures in your head, those are a couple of different ways that this type of a capability can be leveraged by a planner or decision-maker, or policy-maker, et cetera.

So the types of scenarios we can look at, again, this list isn't exhaustive. There's a lot of different things that can be explored within the simulation platform. But to highlight a few, so vehicle electrification charging citing, and a lot of things pertaining to vehicle electrification. We even have a partner project that's not part of BEAM CORE, but it's integrated BEAM itself with grid modeling, to understand grid impacts with electrification charging scenarios, et cetera.

Land use development policies. Policies to incentivize certain outcomes like technology adoption or to improve key target things like accessibility. Vehicle automation. Telecommuting polls, and fares, and pricing, like congestion or cordon pricing.

Like I said, a lot of things to do with shared mobility. It's a ride hail, or shared scooters, or e-bikes, or micro transit. And the way that they can be constrained or leverage for certain things.

Transit, system design, and also a lot of things pertaining to freight. Last mile delivery, innovations, things like that. So in the interest of keeping everyone engaged, we were going to pause at this slide for a moment, and do one more pull for you all to just get a sense as I've listed these types of topics.

What are the kinds of things that for you are actually a pertinent set of areas that you grapple with in your work, or that would be relevant to questions that you have? Just so we can get a sense of where people's interests lie.
LAUREN REICHELT: And I do want to mention I think we have it set up so you can select more than one action.

C. ANNA SPURLOCK: Yeah. So you can select all of them if you like. And while people are responding, I should have mentioned at the beginning, do feel free to interrupt me if you have questions as I go through this. And if someone has raised their hand, and I don't see it, feel free to just jump in and let me know.
OK. Well, that is pretty fascinating. So vehicle electrification is on everyone's minds. And I'm not surprised by this.

There's a lot of focus on this at a lot of different levels of policy, and government, and investment. So that bears out land use, and policies, and incentives to incentivize technology adoption, or mode adoption, or improve accessibility. OK. Interesting. OK. Cool. Thank you.

All right. So moving on, just to note as I said, that slide touched on a lot of the types of levers we can explore. When you change something in the system to see what happens, we can look at outcome metrics across the span of the different modules that we have.

So as I mentioned, we have modules pertaining to land use, to vehicle ownership, and technology adoption. So we have outcome metrics pertaining to each of those things in terms of residential and commercial property values, or other kind of things pertaining in the land use side.

For vehicle ownership, we have configuration of the household fleets, the makeup of those fleets, and also the makeup of those fleets with respect to adoption of new powertrains or vehicle technologies like electric vehicles over time. And then because everything pours into this BEAM model, we have a lot of detail on things that happen from what happens in the network.

So everything from VMT, PMT, VTI, mode splits, occupancy, of different modes, and different vehicles, rate of denied boardings because of capacity constraints, travel time, all kinds of different configurations of travel time, and specific things of interest their. Energy and efficiency metrics, greenhouse gas, and then we have a couple of accessibility metrics that are designed to be built off of outcomes from BEAM that one of which I'll talk about a little bit later.

And we've been doing a lot of work of late to really flesh out. Because this is an agent-based model, which basically means we have a representation of more or less everyone in the region. It really lends itself well to understanding distributional outcomes.

So we've been doing a lot of work in our post analysis of all the data that comes out of the simulation model to come up with useful and valuable ways to characterize equity and distributional outcomes in different, creative ways. So that's been a big focus that cross cuts these metrics.

So here's one of those inevitable model diagrams, which may or may not be useful for folks to see. But it is a representation of the different modules that are interconnected in this BEAM CORE framework. So I'm going to touch on each of these a little bit, and then we'll get into some brief examples.

So basically, we have all these modules that govern the characteristics of the vehicles and in terms of different new technologies projected forward, and market penetration scenarios for those. We have the land use and demographic simulation modeling for long-term scenarios. We have this technology adoption, vehicle transaction model. We use activity, which I'll talk about in a moment for our passenger demand modeling. And then that feeds into B, which is our transportation supply and system simulation.
And then we have the same parallel structure for everything on the freight side. And then of course, that feeds out a whole series of different metrics.

So going through some of these modules briefly to just give people a sense of what they are, and what we use for these different capabilities, we use UrbanSim for our land use model. This is something that is used by a number of MPOs and regions already. And basically, it includes different modules that capture things like household, and employment, locations, real estate supply, and real estate prices, and rents, and different things on the land use side.

For the demographic simulation, this is a capability we've built from scratch in the last two years. We've developed this model called DEMOS, which is innovative in that. Most of the time when you do multi-year simulations and a modeling framework like this, there are different snapshots of the population over time, but there's no direct relationship between the agents or the households in the population from year-to-year. It's just snapshots.

But what we've done is developed a dynamic, synthetic population simulation, so that all of the agents and households evolve through different lifecycle phases year-to-year. And there's different household formation, and dissolution events, and birth events, and all kinds of things. And so it makes it basically so that agent five in year one is the same person in year 10 as they were in year one. But just aged 10 years and moved through different stages of their life.

And why this is useful is because we can leverage it for this capability, which we've built, which is our vehicle transaction technology adoption model, which we've built to be sensitive to. Because we from a lot of research that vehicle holdings are very sensitive to transitions and lifecycle phases. And so we can leverage that simulation capability in this model to have the configurations of the household fleet and the decisions pertaining to whether in each year, the household's going to keep or dispose, or replace, or acquire a new vehicle, and what type of vehicle. Be sensitive to the stage of life and the things that are changing for that household.

As I mentioned, ActivitySim is used for the demand. Basically what ActivitySim is it's an open source capability that's developed by a consortium of MPOs, and it basically allows us to simulate the activity planning of all the agents. So what they're trying to accomplish in their simulation day, and at what times, and also the location choice of specifically where they're going to accomplish those goals and then mode choice.

And then as I mentioned, that all feeds into BEAM, which then resolves what happens in the transportation system given all of these activity plans, and mode choices of all the agents, and the constraints in the system that govern the set of resources that are available to folks in terms of the transportation networks, et cetera. And I already spoke a little bit about what BEAM entails, which is quite a lot of different capabilities.

And I'll mention, if you go to, we have created a fun animation that discusses what BEAM is in pretty high level terms that you're welcome to go check out. And relatively soon, we're going to be releasing a new animation that talks about BEAM CORE, which is the integration of BEAM with these other models that I'm talking about today as well.

And then as I mentioned, we have all the capabilities on the freight side. So the parallel stream of everything from the synthesis of the firms nationally, the supply chain logistics, and fleet configurations, the shipment sizes, and mode choice for all of the shipping that needs to happen from men to these firms, and to end users, online shopping demand, and the fleet operation planning, and then all of the operation of the vehicles from all of this outcomes in the road network in BEAM freight.

So getting quickly to a couple of just examples, again, in the interest of time, I'm only going to touch on a couple of things that I think are pretty digestible and of interest. One is just looking at a case where we're looking at scenarios pertaining to some transit system expansions that recently have opened, or are shortly going to be open in the San Francisco Bay Area.

And so we're looking at this electrification of Caltrain, a new central subway line in San Francisco, and a new bus rapid transit line in the East Bay. And this is just a couple snippets of what you can see from this. Again, a lot more can be seen as well.

But we can see, for example what this means in terms of changes in ridership of those systems as a whole as a result of these new expansions. So the SF Muni Central Subway line is relatively modest change. That's a pretty dense area with a lot of transit already.

So the degree of change you can get is more modest. But for the other lines, they're going through a lot of areas, where there's more diversity of type of modes people are using, and there's more capacity to expand. And so you see this larger increase that resulted from these.

And we can see because we're looking at all of the individual agents in the baseline, and in the scenario case, we can see where they came from in the sense of what they would have used absent those new lines, or the new system design. So what we see is, again, in the SF Central Subway, where it's a pretty transit rich area, a lot of the riders of the new line came from other transit lines, or other transit modes. But there was still some gain of ridership from private vehicles, for example.
But in the other cases, you had a more sizable– while you still had some people coming from transit, you had a more sizable share coming from getting people out of private vehicles, for example.

Another brief example that I wanted to just touch on was I mentioned that we have developed some accessibility metrics that are able to be calculated off of the outcomes from BEAM. So one of them is the mobility, energy productivity metric, which is also funded by EEMS, and is run out of rail. And it's designed to actually be something that it's a location based metric that can be used whether in a simulation model context, or just with real-world data. So that's one.

The other, though, is a complement to that, which I'll talk about briefly today, which is that something we call the i-nexus suite of metrics. So what this suite of metrics is an agent trip level accessibility metric, which is only something you can glean from a simulation model like this. Because you need a lot of information about what the agents, who the agents are, and what they're doing to be able to do this.
And so basically, I'll introduce this briefly, and then just give you an example of some post analysis we've done on it for a case, where we varied the price of ride hail from 800% of the baseline down to 0%. And just to use that in an example of a case where we're taking a flexible backup option, and making it more or less affordable or accessible. And that's just a way to get at why it's interesting to look at something like equity in the context of this accessibility metric.

So this metric I mentioned, there's a suite of three that constitute this set. One is the potential INEXUS, which captures the well-being or utility from the set of potential mode choices or mode alternatives available to people regardless of which they pick. The realized INEXUS this is the well-being or utility from the mode actually chosen for the trip taken by each agent. And the social INEXUS is the realized INEXUS with the environmental externality associated with that mode and trip taken by the agent.

So this is just a couple of examples looking specifically at the potential INEXUS to talk about why this can be an interesting tool to think about equity. So there you can use it to highlight just the distributional differences in the system, in the baseline. So this is just showing that in our baseline, the potential INEXUS is 16% higher for the highest income decile of agents compared to the lowest, for example.
You can also use it to understand how the scenarios you're looking at disproportionately affect different subpopulations. So again, we're looking at this in terms of income. So it's showing that those at the lower income ranking, there's a disproportionate benefit in terms of this potential INEXUS of those greater reductions in ride hail price compared to the higher income groups, for example. So in particular, no cost ridehail resulted in 44% improvement in the median INEXUS for the lowest income decile compared to only 13% improvement for the highest income decile.

And then additionally, the potential INEXUS, in particular, is an interesting tool to be able to look at the couple of different types of benefits that emerge from a given study. So I've characterized these into three. And I know these figures are there's a lot going on here.

What you're seeing at the top is the distribution of potential INEXUS for people based on the mode they took for people who did not change their mode between the baseline and the scenario case, and then the lower set is for people who did change their mode. So what you get is, first, a free ride or direct benefit. This is people who did not change their mode when ridehail price changed. But then when ridehail price got cheaper, they just paid less. So it didn't change their behavior, but they got this direct benefit. Call that the free ride direct benefit.

The re-optimization direct benefit is people who because of the change in the scenario were prompted to change their behavior. And doing so resulted in an improvement for them. So these are people that switched modes presumably from something else to ridehail when the ridehail price got cheaper as we move down here, for example.

And then there's what I think is really interesting, which is the backup option indirect benefits. So this is a case where because the potential INEXUS captures the utility associated with your set of mode alternatives, even if you didn't pick an alternative for your specific mode choice for your trip, the fact that you had another option, a second choice that became more preferable, it's a little bit a keen to a metric of resilience.

So if you had something go wrong, it means that your alternative sets are better, and that is a potential improvement to what to your well-being in some respect. And so you can observe that with the potential INEXUS.

So for example, these are all people who took transit in both the baseline and whatever the scenario case we're looking at is. But you can see their potential INEXUS improves when the ridehail gets cheaper, even though they didn't take ridehail, because their backup option became better or more appealing.
One more thing to highlight is that we are developing this tool called BEAM CORE app, which is going to be an interactive tool to explore a series of sensitivity analysis. We're running with BEAM in our current implementations. So that you can just explore bit and get a sense of how different outcomes changed in the system in the cases that we happened to look at.

And as I say, there's a lot of data that pours out of these simulations. So trying to make it explorable in a user-friendly way is something we're doing with this tool.

So how to engage? We have a number of collaborations that take a lot of different formats. So we have folks we work with that are collaborators, or direct collaborators, or advisors on work that's Department of Energy funded.

I'm listing a few cases here. We also have funders who directly fund us for contracted work for their specific interests. So listing a few examples here.

And then, we also have just collaborated with folks who are using our tool themselves for their own projects, but that we can help support them to do that because all of our tools are open source. So those are some different ways that you can imagine engaging.
And I'll just mention that there are a number of different potential upcoming proposal opportunities, where partnering and collaborating is something that we are very much excited about and open. So if there are areas that are of interest to you, and you're interested in exploring whether there could be opportunities to partner on a proposal, or whatever it might be, please do feel free to reach out. This is definitely the mode we're in right now is application of this capability that we've spent a long time refining.

So this is the team that has poured a lot into this. This isn't even exhaustive. So just wanted to acknowledge everyone who has worked very hard on this.

So that's the content I have for you today. I'm highlighting here on this slide that similar, although different from this Clean Cities webinar series that Martin and Cass were talking about.

There's going to be a webinar series kicking off to highlight some of the findings and results from the SMART Mobility consortium round. This round of SMART Mobility work. So if there are folks that are also interested in registering for that webinar, you can find here where to do that.

And so we'll speak about some of the other results from the BEAM work. But there's going to be a lot of different projects that are presenting on a lot of different things in that webinar as well.

So that's what I have for you today. I'm curious if there are any questions from you all about either clarifications, or exploring different things that might be of interest to you about what we're capable of or anything else related.

And if there's just crickets, that's OK. I do have some questions to pose to you if you don't have questions for me. Anyone?

OK. Well, maybe in the interest of spurring a little bit more discussion, these are some things that I think questions that I've thought about that are really helpful for us to understand for potential stakeholders that would gain value from this tool. Just it helps us frame how to talk about it, and what to focus on in our work.

So the first question is, what are the most pressing challenges and questions that are proving most difficult to address given current capabilities that or models or things that you might have to hand that you are grappling with? So we did the little pull in the middle there, which showed a lot of people are grappling with questions or very interested in things pertaining to transportation electrification.

So if that's the area, I'm curious if anyone's willing to chime in with a little more detail on some of the things, in particular, that you all are grappling with in that domain or others. Anyone?
Maybe I should just call on someone. No. We'll do that.

LAUREN REICHELT: We then have a question. We have a question in the chat.
C. ANNA SPURLOCK: OK. Peggy is wondering, how do we break the car culture? Yes, that's a good question. I wish I had the answer to that.

I can tell you that what we can do with this tool is explore as I mentioned A-B testing of some of the different types of ways that one could tackle that. And C, based on our underlying models of mode choice and what people do, what types of policies are more effective for what types of sub-populations.

But this is a big issue. And I would say that breaking the car culture, in my opinion, requires investment in alternatives like public transit or other factors that gives people the ability to do what they're trying to accomplish.

I'm curious if anyone else has thoughts on that, or other questions for me, or other answers to this question?

LAUREN REICHELT: There is another question in the chat.


LAUREN REICHELT: Can BEAM CORE be used as a replacement for MPOs travel demand model?

C. ANNA SPURLOCK: I would say it could. We are in an earlier phase of the work in the sense that that isn't something that we're moving on in any cases. But if there were folks that were interested, because like so safe, for example.

We have talked to a number of different MPOs in different regions that have a whole different range of sets of capabilities, or internal resources. Everything from, for example, San Francisco, or San Diego, or a couple come to mind, where they have pretty established sophisticated models that they use themselves, and in some cases, are developing their own agent based models.

So in their case, I'm guessing there wouldn't be an interest in replacing what they've invested in with BEAM itself. However, we are talking to or we have talked to some about whether some of the other modules we've developed like the Atlas model that gets at the vehicle transaction to households, or the freight modeling capabilities, where they're even these MPOs that have pretty solidly established capabilities don't have as well-established capability in those areas.

Everything that I showed you, all of those models interconnect in an automated way in our system. But they are all modular. So they could be pulled out and applied with a different set of models as their partner models. So just to note that.

When it comes to whether BEAM CORE could be used by MPOs, I mean, the short answer is absolutely. The long answer is we'd have to know more about what we'd be able to demonstrate for the potential user to make it able to be usable, and in what format for those agencies. Because the one drawback of a modeling system like this is it is very large and very computationally intensive.

And so we just have to would work out like the way that it would be most effectively usable in a planning context, but definitely a pathway. Any other questions? I'm not seeing the chat. So whoever can see that, do let me know if there are others that pop up.

LAUREN REICHELT: Oh, yeah. Go ahead. I just had a question. I have had a coalition tell me previously that they were implementing an e-bike pilot, and that either, I don't remember if it was the state government, or the city government, but they were wanting to see how that could be rolled out, and what impacts that might have if it was expanded beyond a pilot.

If there's a coalition, that's working on a pilot project, and there's interest in seeing whether the investment is worth scaling, how would you suggest they approach you? Or is there a certain partners they should be getting on board ahead of time? Or I guess what would be next steps if they wanted to work with you to see how that might play out?

C. ANNA SPURLOCK: Yeah. I mean, just reaching out and setting up a time to chat about it would be probably the best first step. Just because the more we know about the specifics of what it is that's being piloted, the easier we can communicate how that would translate into our simulation environment in terms of vehicles for getting that work done. I apologize for the pun.

Again, I think there's a lot of different models for how that could be done. Absolutely, we can do a direct contract to do some simulation work for that specific exercise. No problem. If it fits well within the priorities of some other type of funding vehicle, like I mentioned, that might be the type of thing that would be a good example of a good FOA project if the topic area of the pilot fit within. And for those that don't know, FOSA is I can't actually remember what the acronym stands for. But that's the Department of Energy's term for like a—

LAUREN REICHELT: Funding Opportunity Announcement.

C. ANNA SPURLOCK: Thank you. funding Opportunity Announcement. But specifically, FOE have to be primed by non-national labs. So it would be a case, where if someone was interested in priming a proposal, we would partner with you, and that could be some of the nature of the work, for example.
So yeah. But just reach out and just having an initial conversation about what's interesting, what's the context. I mean, like I mentioned, doing simulations to look at the implications for scaling up, or to suss out the right way to scale up is it perfect application of this set of capabilities.

LAUREN REICHELT: Great. Thank you.

C. ANNA SPURLOCK: So this is I think maybe a restatement of the same question I just asked a moment ago. I'm going to skip to the next one.
So as I've mentioned, one of the things that BEAM CORE does is because of the land use modeling and the Atlas like vehicle modeling. And some of these other modules, the demographic modeling, it's a uniquely powerful tool for long-term scenario analysis. So understanding longer-term implications for different types of interventions that you might want to explore.

And so given that, I am curious. People, there were a couple of you when you were expressing interest in the topic areas that mentioned land use, for example. And I'm curious with respect to some of these things that are on a little bit more of a longer-term time frame for how they evolve like land use.
What are the particular types of questions that are of particular relevance to you on those areas? Anyone willing to share in the chat or otherwise?

LAUREN REICHELT: All right. In the chat—

C. ANNA SPURLOCK: –Modal shift in specific investment projects. Yeah. So definitely, like I mentioned, that's something that certainly would be right squarely in the capabilities of this type of simulation modeling framework. In terms of cost and benefits, again, we have, like I mentioned, a whole range of different outcomes that can be configured to specifically capture the types of costs and types of benefits that are of particular interest to your case.

So there's a lot of flexibility there, and a lot of things that can be tailored to the particular needs that you might be thinking about. Anyone else care to share this question?

LAUREN REICHELT: Or even coalitions that can speak to what are some of the key questions that their local partners are thinking about when they're thinking about mobility investments.

C. ANNA SPURLOCK: All right. Maybe the next one. Let's see. So yeah. So on this question of metrics, this is something that's near and dear to my heart because I do a lot of work in a lot of different areas.
And one of the areas that I've been doing a lot of work in lately is in specifically things that pertain to Justice40. So for those that aren't familiar, Justice40 is a Biden administration executive order that basically calls for 40% of benefits from certain key federal clean energy and related investments going to disadvantaged communities.

And so I've been very deep in the weeds in a lot of different ways in different settings on what we mean by benefits, and how you structure metrics to really target the things that matter, and ask the questions in the ways that get the outcomes that you're actually interested in understanding how they stack up. So I'm just curious. In terms of metrics, particularly with respect to equity, or but others as well.

In your particular domains, is there any particular metrics that either you have to be able to see in a particular exercise to understand the impacts of something, or would want to be able to see but can't with current capabilities?

Greenhouse gas emission reductions with marginalized communities. Yes. So that's a good example. And I'll just say that, at least, in our case, right, we can attribute greenhouse gas emissions to very specific character agents pretty much. So we can slice and dice that based on a range of different ways of characterizing different sub-populations or communities of interest.

Another thing I'll mention is while we don't have it within the context of the BEAM CORE project, something we've been pushing on for a long time is actually marrying up the BEAM model with some reduced form models to look at air quality impacts, which I think is going to even more directly impactful to marginalized communities.

So because it's more of a local emission, and so we have quite a bit of expertise on air quality modeling in our team. And so we're working right now in a small project to build in the capability to look at air quality impacts and health outcomes as a result of that as well.

How do we accurately measure reductions greenhouse gas, or VMT while trying to increase access to goods and services for marginalized communities? In many cases, it may mean more VMT if we can provide access to services health care, et cetera. Yes, absolutely.

I mean, I think this is a thing that's really I think fascinating because as someone that cares a lot about environmental outcomes, but also cares a lot about equity. This isn't the first time, and will not be the last that those two goals may not always be exactly aligned, and that there have to be trade offs made.

But I think that the idea is that you can explore some very nuanced sets of outcomes in a simulation model like this that lets you dig under the hood on some of the natures of the constraints that have to be addressed to improve accessibility, and how those reducing those constraints interplay with something like greenhouse gas emissions, or like VMT that's resulting in those emissions, for example.

Yeah. I mean, I think that's a very rich area that's critically important. Because you don't want to limit accessibility, but we also want to do so in a smart way, right?

Any other thoughts on metrics? Well, those are all the question prompts I had prepared. Anyone else have any questions on just the– oops. Hold on. What happened? Come back. Come back to here just so you have that.

Any other questions, or any other thoughts on this topic? In the last few minutes.

So I guess maybe the last piece of information I can convey, or I can think of now is, right now, we have completely calibrated implementations of BEAM in the San Francisco Bay Area. Sorry. BEAM CORE in the San Francisco Bay Area, and in Austin, Texas.

We have implementations of different subcomponents of this system. BEAM, in particular, in New York City, and that's surrounding region. We had an implementation in Detroit for a time. But that once it would take a bit to bring things back up to par on that we haven't worked on that for a while.

And we're working right now on expanding to Seattle. However, we can expand anywhere. We try to rely almost exclusively, or make it, so that we can stand a model up with publicly available data.

The more data we do have from specific local agencies like if there are MPO surveys, or things like that, that are a richer set of information very specific to your region that will just make things better. But so just know that we have a number of implementations, but we would have the capacity to expand to your specific case if there was interest in a collaboration, or a project leveraging these tools. All right.

LAUREN REICHELT: OK. Well, thank you so much, Anna. That was fantastic.

C. ANNA SPURLOCK: Yeah. Thanks for having me.

LAUREN REICHELT: Of course. This was recorded. So we'll get that posted up on the Clean Cities toolbox. So if anyone wants to reference back, or for the folks who weren't able to attend today, it'll be available there. And again, encourage all of you to register for the remaining sessions in the series. So thanks, everyone.

C. ANNA SPURLOCK: And do reach out.