Using ATRAVEL to Calculate Cost, Travel Time, and GHGs for Various Mobility Options (Text Version)
This is a text version of the video for Using ATRAVEL to Calculate Cost, Travel Time, and GHGs for Various Mobility Options presented on March 2, 2022.
Sandra Loi: Okay. Well, welcome, everyone. Welcome to today's webinar. I am Sandra Loi, from the National Renewable Energy Laboratory. Our team at NREL provides technical assistance to the Department of Energy's Technology Integration Program and our network of more than 75 Clean Cities Coalitions located around the United States. Today you'll hear from Andrew Burnham, from Argonne National Laboratory, who will walk you through ATRAVEL, a new two-pronged tool for both individuals and transportation project planners designed to help users examine cost, travel time and greenhouse gas emissions for various mobility options.
The web-based ATRAVEL tool has a Trip Tool for individuals and a Metric Tool for transportation project planners. With the Trip Tool, users input location and recurring travel patterns to compare cost, travel time and environmental impact for vehicle ownership versus transit and Ride-Hail. The Metric Tool provides census tract level mapping and summary data for several transportation related parameters including vehicle cost, travel time and GHG emissions, as well as several demographic and air quality metrics. The Metric Tool serves as a valuable resource for equity-focused transportation planning.
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Now I'd like to introduce today's speaker. Andrew Burnham is a principal environmental scientist at Argonne National Laboratory. He develops tools and provides technical analysis regarding the environmental and economic impacts of alternative fuel in advanced vehicles for the US Department of Energy's Office of Energy Efficiency and Renewable Energy's Vehicle Technology's Office, Technology Integration Program.
In addition, he performs life cycle analysis to update the greenhouse gases, regulated emissions and energy use in transportation or GREET model. Andy, over to you.
Andrew Burnham: Thank you very much, Sandra. Welcome, everybody. I appreciate you taking time out of your day to hear me speak about the ATRAVEL tool. I will first do a presentation, give a little bit of introduction about the tool, how we developed it, where the data is coming from. Then go to a demo, kinda show the tool in its live form. Then open it up for questions.
I think, in general, you know, I don't know everybody who is on here, I know a lot of Clean Cities coordinators did sign up and the tool is geared for thinking about Clean Cities Coalitions and stakeholders, as well as a consumer education tool. So getting feedback from, you know, the users, the people who may be using this tool would be very helpful, so I'll try to leave some time for that discussion, and maybe at some times in the future when we have some other meetings where we can get together, I'd be appreciative of other face-to-face or, you know, again, just e-mail comments and questions and things like that.
With any of our tools and resources, we're developing them for you and we appreciate and need your feedback too, to make sure we're doing – we're getting it right or we're helping you and giving you information that's gonna be valuable. So I'm going to start with the presentation and Sandra can yell at me if I got anything wrong here, but hopefully everybody sees my slides, so –.
Sandra Loi: Looks good.
Andrew Burnham: There we go. So the presentation, I have to first thank Eric Pfister and Kendrit Tahiraj, they are the people who actually did the work, really, on developing this tool. They are the programmers and Eric is a student at University of Illinois; Kendrit works – or is at school at Wilbur Wright College here in Chicago, and I can't program so I have them do all the work. I do all the data and the analysis stuff but they really did, you know, a lion's share of the work and so I really have to thank them for that.
So, let's start. So, what is the ATRAVEL tool, why did we create it? The idea was to examine different travel modes for consumers over kind of key metrics that they may consider when either deciding to own a vehicle or make other kind of travel – you know, use other travel modes. So, the three metrics that we're focusing on are costs, travel time and greenhouse gas emissions.
If you're familiar with some of the other tools and work that I have produced for Technology Integration, Clean Cities, the AFLEET tool and specifically, but many other tools, we have predominantly focused on kind of environmental factors like greenhouse gam emissions here, as well as other emissions as well, and we have done cost, cost of ownership and so forth. We haven't done travel time, and when thinking about our personal travel, how long it takes us to get to places is really crucial, and one of the, you know, key advantages of oftentimes vehicle ownership is that you – in many instances, it is a very fast mode, right.
So those are – the idea is, you know, how do we compare, you know, our travel options along these three metrics and we do it at two different levels. One is, as it was introduced, as kind of a consumer level, thinking about our, you know, travel patterns, my specific travel patterns. I live in Chicago, pre-pandemic I would go to Argonne National Lab, where you work five days a week as my daily commute and, you know, I have other travel to the grocery store and to, you know, sometimes to a restaurant and here and there. So I have my own kind of travel patterns, and like what kind of modes and options are available to me will depend on where I live and where I go.
The other level to do analysis is kind of at this regional level. So, the second idea is really kind of summarizing travel data, travel metrics at different geographic resolutions, so maybe starting kind of at a census tract but potentially building up and seeing how travel patterns differ across the states. The Clean Cities Network is kind of a good example of that. You know, we have a wide range of coalitions representing, you know, most of the states in the Union and, you know, most of the population in the US. But there is, you know, people who live in rural, people live in, you know, smaller towns, bigger towns, you know, bigger cities and things like that, and all their options are gonna differ based on again where they live and where they travel.
So I kinda hit on that, you know, the location, travel patterns, how much did you travel, how much do you travel typically. What modes are available for you in your area? How many people do you travel with? So if we're doing kind of analysis on a kind of per person basis, you know, the occupancy of your car or the transit bus or even, you know, taking Ride-Hail or some type of shared rides, that will matter in kind of the per person, you know, benefits or costs, right.
So, I'm gonna start off with the first tool, which is consumer focused or consumer education called the Trip Tool, and so I'll walk through it here in this presentation format, give you the slides, but then I'll also show this in the demo as well. The idea of the tool is you enter your trips through a Google Maps interface. So we see this here, you know, kind of typing in as you may be familiar with, you know, what's your starting location, what's your destination.
We added features because we did get some good feedback from early testing with coordinators and other folks, that people would like to save and load the trips. All of these are saved on your local side, we don't – you know, we don't take any of this data so it's all in your cookies and you can clear your cookies if you want to get rid of it. But you're able to save if you want to, you have to click a button to say yes, I do want to save and then you can click load and bring back the trips that you did put in there.
After you do that, you can enter, you know, one trip, you can add two, three, four, you could, you know, go crazy and add a lot of trips. But in this example, I just, you know, chose one, leaving from basically where I live in Chicago, going to Argonne National Lab five times a week, a roundtrip. The next is entering your vehicle to start, kind of this is our baseline analysis, oftentimes. You know, we're often kind of comparing vehicle ownership to something else or, you know, gasoline vehicle ownership to something else. So this is our kind of starting point.
You can pick a vehicle that you do own, you could pick a vehicle you're interested in, you know, there are multiple options. We are expanding our vehicle list here. We have vehicles for the last like five, six years, so we have costs and cost data for them. We are working to expand that to more used vehicles, and I'll get into some more of the data analysis that we'll be able to expand upon. But so enter make, model, year and that's, you know, that's your big starting point. You don't have to do a ton, you know, it's consumer education, it's meant to be kind of a simple process.
After that, you can get this results – kind of overall results sheet, where you can compare the different modes using each for the trips that you entered. I also – let's see if I – I didn't mention – I think I glossed over it, that you can enter kind of additional mileage. So in this example, I just chose like, okay, this is my daily commute, but you can add additional mileage and say, yeah, you know, in general I drive another thousand or so miles and you could add that as kind of a slider. And we do have some automatic features that if you only pick like a very short trip from your starting location, the slider will kind of say, well, the people in your neighborhood typically travel 10,000 miles per year, and so it would it give you a little bit of a sense as you're adding trips to whether you're kind of near that kind of average or once you hit above then we kind of zero that out.
So, getting back to the results. You see this comparison of the three metrics that I was talking about, cost, travel time and emissions, and we compare it for each of the modes, vehicle ownership based on the vehicle you chose, transit, if there is transit in your area because you may be taking trips in areas that don't have transit and if Google Maps can't find any transit routes for you, it will kind of say NA and not available. Ride-Hail can be the same issue, do you have Ride Hail in your area, if you do, you would see this type of comparison. And, you know, oftentimes you may see kind of what you would think, that, you know, transit's gonna be the cheapest in general. Using Ride-Hail for all your travel would be kind of like having a chauffeur, basically, that's gonna be, you know, probably the most expensive, in many cases but not always, and we can talk about that.
Same thing for travel time, in general, you know, the expectation is transit's gonna take longer because you have walking time, you have waiting time, and the, you know, stops, of course, and so forth. The greenhouse gas emissions can be interesting. I'll talk about the data underneath but we do have data for every single transit agency that provides data on their fuels, their fuel efficiency, their passenger loads, so we can estimate on a per person basis what are the emissions of these different modes. And these may be not as always kind of maybe intuitive as you think and it really depends on some key factors. Of course, what vehicle you choose matters.
I mentioned before, if you see this as tons per person, so this is on kind of a vehicle person basis, person basis, excuse me. So if you are carpooling and you, you know, have two people in your car, you know, the emissions per person is half, you know, is for one – you know, the vehicle emissions are divided by two, right. Same thing, transit, you know, you're doing how many – what's the average transit number of people on, you know, the agency that you're using to get around. Ride-Hail is the same thing, that you may use Ride-Hail by yourself, you may be with a person, that will impact emissions, you know, potentially pretty significantly.
As you see this, you know, there are a lot of assumptions that go into these types of calculations, and as a consumer education tool, we wanted to provide these kind of levers to show how the results can change as you, you know, move the levers, basically. So, I can – I don't know if you see my mouse when I move things, but you know, the ownership, transit, Ride-Hail, off to the left-hand side, you can click on any of these and I'll show this is the demo, but you can adjust each of these assumptions and see how that changes the results. You know, we see some of the big ones that I mentioned, vehicle occupancy, the fuel economy which was based on the vehicle I chose, fuel prices, you know, of course are important, and we, right now, have kind of a static fuel price for your area, that's a challenge to think about this over kind of a year, future, you know, what's the price today. Obviously prices in the recent times have kind of gone up pretty significantly, so you can adjust those factors. Okay.
For additional consumer education, we have kind of – let's see again, if you see here, there is like these gray arrows on the left and right here, a little scrollbar indicator or scroll indicator here that you can move on from the overall results and click on, you know, these arrows, mouse over or, yeah, scroll over and get more details on the vehicle results, the transit results, the Ride-Hail results, and then a little bit – there is another page with our methodology. So it has some information, you know, kind of summarizing like, you know, the cost, the travel time, the GHG's, how they may differ in your specific situation or talk about alternative fuels for greenhouse gas emissions, how they differ based on different fuels, links to fueleconomy.gov which has always, you know, for on the vehicle side a lot of good information. Transit stuff has links to, you know, other transit resources. Ride-Hail to Ride-Hail, so on and so forth. So that's kind of the major parts of the Trip Tool.
And I'll just kind of highlight a bit of the data. So, as I mentioned, there is a lot of assumptions, a lot of data behind doing this type of calculation. So we, for vehicle costs, we'd need to get data for the three different modes, vehicles, transit and Ride-Hail. The vehicle data comes from a study that I was part of that was published last year, where we did a comprehensive total cost of ownership analysis of different vehicle types, so cars, trucks, and then we did heavy-duty as well. But like cars, passenger trucks, SUVs and so forth, and as well as different powertrains. So obviously for DOE, Clean Cities work oftentimes, you know, we're interested in how does gasoline compare to an electric vehicle versus hybrids versus fuel cells and so forth.
So we did an analysis and a chart that you see there is kind of a generic version of what, you know, what I kind of – was showing before, of like what are the costs based on some of these factors of vehicles, depreciation typically, financing, fuel, insurance, maintenance, repair, taxes and fees. The data currently in ATRAVEL is primarily national average cost data. The fuel prices do vary based on your locality, your local fuel price, but we haven't got to regional TCO, that's kind of a to-do thing, as a research topic for our team. So we're working on that. And so as we get more data, better data, we're gonna roll it into ATRAVEL.
Moving on to a couple of the other modes just to make sure I hit on them, is that for public transportation there is the American Public Transportation Association that has a public transportation fare database, so we used the fare database to estimate what it would be. So we have an algorithm that if you only do like a few trips, it's going to try to choose, you know, using a lower fare, like a single fare or a daily pass to, you know, do your costs, but if you travel a lot, maybe your transit agency has a monthly fare or an annual pass or something like that, it will try to pick the lowest cost option for you. Yeah. And then the Uber, for Ride-Hail, we use a fare estimator that has kind of the per mile, per time estimates for your travel that you enter.
For travel distance and travel mode. So kind of as I was showing, like Google Maps, we're using this interface. I think pretty much most people are familiar with that, that, you know, you're gonna get the distance of routes, you're gonna get the time of routes as well. And so this is one of the complications of this, is that we try to choose Google Maps based on, you know – we call data based on when you use the tool. We are working to try to get that as a feature and we're trying to see how easily we can do that. If you are familiar, you can say, well, I want to leave at 9:00 AM, I want to arrive at, you know, 10:00 AM at this location and things like that, and your travel times will differ because, of course, in many areas, you know, traffic is worse during rush hour and so forth. So, that's a feature that we'll continue to narrow on, but that's how we gathered like the data that you're entering via the Google Maps interface.
And then we have additional mileage information, kind of this regional information that we are leveraging from other data sources. So my colleague, Joann Zhou, developed this study, which you can see this chart here, which was looking at some of the things I'll talk about a little bit later, about how does transportation cost, specifically in this case fuel cost, differ based on where you live. But as part of that, they relied on a lot of census and modeling data to estimate, you know, how many miles do you travel based on where do you live, some other factors that I'll talk about in the next couple of slides. But this one is really about, you know, what is the typical travel in your area, and there is another source that we've leveraged in the past, the Local Area Transportation Characteristics for Households data, ooh, mouthful. So, yeah, and called LATCH, easier to day, but another kind of modeling type effort to see, like, you know, how do people travel based on where they live, and they're oftentimes using the National Household Travel Survey to get that data to do this information. But there is other census information that is collected as well.
For transit, an important data source that we used for a couple different things is the National Transit Database. They have information on vehicle speeds or transit agency speeds. So we know, say transit buses in Chicago, the Chicago Transit Authority, what their kind of average speed is that we can generate that. We can get some other data as well from there, as I mentioned, fuel economy, passenger load and so forth, but for travel time we can get speeds.
And then for greenhouse gas emissions, so this is kind of the stuff that I have been doing for the many years I've been at Argonne. We have already created kind of a dataset for AFLEET, if you're familiar, but if you're not, it's a tool to do comparisons of different vehicle types and vehicle powertrains, so gasoline, electric, natural gas, so forth. So we have that data, that's what is being used in ATRAVEL. That is – the data in AFLEET is really based on GREET and this is a schematic here of what GREET is. It's a life cycle analysis tool. It's looking at, in this example, you see the fuel cycle, so basically all the steps you need to like produce a fuel and then you use the fuel. So we're doing all the supply chain emissions as well as like the use emissions.
Some other key factors that we need are fueleconomy.gov 'cause we're looking at, in this case, real specific vehicles, you know, a Ford Explorer, what's its fuel economy. And then for the transit database, again, I mentioned we can get fuel economy and data from there. I didn't mention too much on the Ride-Hail portion. For travel, distances, you know, oftentimes it's gonna be the same as kind of your vehicle ownership route, so that oftentimes is assumed the same.
But like the greenhouse gas emissions for Ride-Hail is different because you have a driver who is driving around picking up people to go back and forth. So there is empty mileage, so there are times when the driver is driving around the city either to the place where they are going to pick that person up or just kind of circling around or to a different area. And so when looking at the greenhouse gas impacts, you do need to consider that, and so there is some data, there isn't a ton of data on that but we have some estimates of kind of that empty, it's oftentimes called deadhead mileage. We have a little consumer education to explain about that and why those, you know, greenhouse gas emissions can be higher.
The other challenge of trying to do this at a – you know, with good fidelity at different locations is, you know, the greenhouse gas emissions of the Ride-Hail Uber/Lyft that you're using will depend on which vehicle is picking you up, it's the same time. We have very limited information on that. Chicago provided some summary of that data, so we used that as kind of a baseline of like this is the representative vehicle types that may pick you up and we weight that based on kind of Chicago data. You know, as we get more data and get better information on a regional basis we can refine that and prove that but that's one of the factors that you can play with on the tool.
And so, moving to the next tool. So that was the Trip Tool, this is the Metric Tool. We started with the Trip Tool really as a consumer education idea and then we thought, well, what can we do to help Clean Cities coordinators and stakeholders. So the idea really was to provide you with summary data on some of these factors that we already had been looking at related to the Trip Tool. So we started with the Trip Tool, we're collecting a lot of this, you know, vehicle miles, travel and cost information at different, you know, regional fidelities. And so, like, well, let's start putting this together and maybe mapping it so that people can, you know, look at these factors in different locations.
So here is – I'll walk through the tool first. Here you select a location. You know, in this example I'm typing in Chicago, Illinois. Then you have to pick your geographic area, like where you're gonna focus. By default, everything goes to census tract. So if I would type in Chicago, Illinois, it would kind of – Google Maps would be like, okay, where is the real specific point that is like Chicago, Illinois in Google Maps. So it picks that area and it's a census tract. But you could type in a more detailed – like you could choose your home address or a business address and get like an actual, like very specific there.
But here you see the dropdown that you can adjust. So it starts with the census tract as the data but then you can go to ZIP Code, the municipality or city, the county that you're in. This is a core-based statistical area is kind of, basically a way to look at like you're region, I guess. You know, it can be a little bit more than a county but in this example, like Chicago, it's not just focusing on the county of Chicago but there are, you know, suburban counties that are part of Chicago, basically. You know, Argonne is in a suburb of Chicago but it really is kind of Chicagoland, I'll use colloquially, and that's what a core-based statistical area, it's really the counties around kind of a city of 10,000 or more. So, you know, it doesn't have to be a big city like Chicago, it can be a smaller city that kind of is a core area that has counties around it. Okay.
We also have definitions, 'cause that was one that like, yeah, maybe not everybody is gonna know what a core-based statistical area is. Clean Cities Coalition, so we have the boundaries of your coalitions. You can choose that and get some of this summary data which I'll talk about next. Next level is the US state, so you can pick the state you're in and get the summary data there.
After that, you choose the metric to map. By default, it kind of – it's set at household vehicle cost, but you can go down and change that, the map will change as you do that. So the key metrics that, you know, kind of similar idea of what we were talking about in the Trip Tool, vehicle costs, greenhouse gas emissions, travel time. But here we're doing it at a household level, we're not doing it at an individual, kind of I'm driving this vehicle. So in a household you may have zero, one, two, three, multiple vehicles being used, so that's some of the data that underlies that. So that's something that I want to make clear of what's happening in the Metric Tool.
We also have equity factors. HH is household. Household vehicle burden, this is something that I kind of developed basically leveraging what my colleague Joanne did for her study on fuel cost burden. So that study was like, okay, where does it cost the most to like drive your vehicle. So we took it a step further, fuel cost plus all these vehicle ownership costs. So we did the TCO study that – so these other vehicle ownership costs are depreciation, maintenance, repair, doing that on kind of an annual basis to kind of get estimate of your annual cost of that.
So the burden is those total vehicle costs for all the household vehicles in this area, the average, divided by the average household income in that area. So what, you know, kind of percentage of your vehicle costs, so say I spend $10,000.00 per year on vehicles for my household but my income is $100,000.00, so for household, then you would do 10,000 divided by 100,000, your vehicle burden would be about 10 percent, you know. So hopefully that makes sense.
We have some other equity-related metrics that we pulled from EJ screen, percentage of minority population in the census tract, low income population percentage in census tract, how many older population, over 64 in a census tract. We have air quality metrics still from EJ Screen. DPM is diesel particulate matter, this is a very specific factor really looking at diesel particulate matter, emissions coming from diesel. We have PM 2.5 which is fine particulate air pollution, which is really the key air pollutant that will damage health, so that's a really important factor. As well as ozone, which, you know, many are aware of, so ozone air quality as well.
We did add a new screening variable based on work, again, I should add Joann to my presentation 'cause we leveraged a lot of her work. She worked with Jim Kuiper, I don't know if you've seen the EZMT presentations that have been – he's done, but they were doing some mapping to support the DOE and DOT interim guidance definition for disadvantage communities. So we have an overlay based on that work so that you could, you know, overlay kind of some of these metrics that I just mentioned with other factors, like, you know, places where these – you know, where are the disadvantaged communities, where are high household vehicle costs. So you can do a little bit of screening with the first metric, and then you can choose another overlay that will basically, you know, remove kind of the other tracts that you're not interested. It's kind of focusing on I only want to look at the disadvantaged communities or I only want to look at areas that have a high percentage of minority population in my area. So that's how you can screen with that.
And then you can go to the data. So one, you'll see the map on the right, you know, right away, but then we have kind of analysis that we've done to do kind of a regional comparison of the metric at kind of the area you chose. So in this example, you know, Chicago, Illinois, what's the household vehicle burden on average in this city. So it's about a little more than 10 percent here, 11 percent. What's the state average, so, you know, how does it compare, state average, you know, closer to 14 percent. And then national average closer to 15 percent. So you can see how your region or, you know, again, the census tract, ZIP Code, compares to state and national average data.
And then, you know, within a geographic area there is variation, so Chicago is not the same, so is not homogenous. So you know, we have some variability and so we do a histogram basically, this distribution here, that shows like how many of the census tracts within the area that you choose are kind of, you know, these different bins. And so I should explain the bins. So we have, you know, kind of four quartiles, let's say, of data, you know, that's how we break out the data into these four quartiles. And, you know, basically the histogram says, you know, how many of the tracts within this area are the first quartile, second quartile, third quartile, fourth quartile. Hopefully that makes sense. And then we have a little bit of summary data below to kind of explain like, you know, what was being done there.
And then kind of the final thing that we have here on the Metric page is these household vehicle metrics. So we ended up focusing, 'cause that's where primarily our data is, looking at, you know, kind of summarizing this data, similarly kind of to a national average. Like where is your – you know, in this case, Chicago, how does it compare to national average. And so you can see, you know, for vehicle miles traveled, travel time, greenhouse gas emissions, for vehicles it's kind of on the lower end for that. And then vehicle ownership, you can kind of maybe see why maybe some of these, like it has in general, less vehicle ownership. In this example, like the fuel cost are lower than national average, also that ties into fuel economy, what the fuel economy in this area is, ownership costs. And then we do the vehicle burden so basically the calculation of the vehicle costs divided by the income gets you your vehicle burden. So hopefully that makes sense and happy to take questions as we go through.
I'll just go through the data. I talked a little bit about this already on the Trip Tool, but just to highlight some of the other things that we've done. So the household vehicle costs, again, leveraging our TCO study that we've already done. We do add another factor that – we have purchased Experian data, so we have the local registrations by ZIP Code and we know kind of what vehicle types. Are there a lot of cars in your area? Are there a lot of Crossovers, SUVs, full pickups and so forth? So in the calculations we weight the cost based on both the age of the vehicle in your census tract or your area as well as the type of the vehicle.
So, you know, we have to kind of generalize what the vehicle is, so, you know, we're seeing, you know, new vehicles, if your area has a lot of new vehicles the cost of ownership in that area is higher. If it has a lot of pickup trucks, those tend to be more expensive, that can also increase the cost, so forth. So, happy to take questions on that.
Fueleconomy.gov, that's for fuel prices. We leveraged Joanne's work on the fuel price. And then the household vehicles, which is relying on census data, the American Community Survey data as well as some VMT analysis that I talked about before. The household vehicle travel time – well, let me just hit on that chart. So I think the chart is interesting.
This is something I did in Excel, it's not exactly in the tool but we're thinking of like how to add additional visualization along these metrics. So as we can create more on that, this is something that I'd like to add. You know, what percentage of your area, you know, has car registrations, or what percentage of your area has light truck registrations. You see in this example, you know, kind of the Great Plains have the least amount of car ownership. You know, the highest car ownership is I think DC is in the 67, that's the max value, but 65 percent. But, you know, other, you know, you see California and Florida and some of the others having 50 percent car registrations. We obviously know or have heard that, you know, light trucks are being purchased more now today in sales, but this is kind of the average registrations in your area, not new sales, what's really there today. So that can help understand, you know, what kind of vehicles are there in your area.
Going to travel time. So, I did talk a little bit about like Joann's work about household VMT. We also can get trip speed, so we can estimate, you know, what travel speeds are in different locations. So this is real high level, at a state level, but we can get to more fine grained than this, but, you know, we see this – I like this just to look at it, just kind of seeing what, to me, you know, in general makes sense that like kind of more rural locations you'll have potentially higher speeds. I think there's some weird thing going on in Alabama that speed demons there, that one always – that one pops out as like very fast. But, you know, so we have that type of data to generalize kind of at a local area.
And then for greenhouse gas emissions, similar kind of data that we need. You know, AFLEET's doing the greenhouse gas emissions per mile calculations. We're using that Experian data to know how many cars, Crossovers, SUVs, like what vehicles actually – so we can get the fuel economy. So we can actually, you know, we can know in my ZIP Code of, you know, Chicago, what vehicles are there, what's the fuel economy of those vehicles in my ZIP Code to do these calculations.
And then finally the household vehicle burden, which I explained before but just to reiterate, is, you know, using the household vehicle cost, dividing by household income and we get the household income from Joann's study which relies on the American Community Survey, so census data, basically.
So, I'll just do quick future updates. We are interested in adding electric micro-mobility to the tool as an additional modes, e-scooters, e-bikes. We want to do additional mapping, I talked a little bit about that, some of the vehicle registration data that we have. As we get better regional total cost of ownership data, we want to be able to add that. Regional travel analysis, my colleagues, Joanne, Dave Gohlke, and myself are working on kind of the speed, you know, travel distance analysis, so that's something we're gonna roll in once we do that.
We want to improve the user selected visualizations. So, you know, right now it's kind of static, tiles on top of a map. We want to see how we can let the user customize kind of the visualizations a little bit. So this was our first pass, definitely want to get feedback. So, as, you know, you use the tool, both the Trip/Metric Tool, you know, whatever feedback you can provide is really gonna be helpful to improve the tool. So you can send it to ATRAVEL@ANL.gov.
I know, yeah, we're getting close to maybe the end. I want to do just a switch to a quick demo, just to show you how the tool works really quick. Again, Sandra can yell at me if not seeing it, but hopefully you see Chrome here. So the ATRAVEL –.
Sandra Loi: Not yet, Andy.
Andrew Burnham: Let me –.
Sandra Loi: Try again.
Andrew Burnham: Let me try one more time.
Sandra Loi: Okay.
Andrew Burnham: Any luck?
Sandra Loi: Perfect. Yes.
Andrew Burnham: There it is, okay.
Sandra Loi: Mm-hmm.
Andrew Burnham: Here, yeah, ATRAVEL is on the AFLEET homepage, so you can see it here, click through. The homepage gives you a little intro. We have some key concepts, as, again, trying to do some consumer education as we're thinking about different modes of travel. But we have an intro that explains like what I just did, so I won't get into it, and like how do you use the Trip Tool, how do you use the Metric Tool. You can click either here or here to get to the different tools.
We'll start at the Trip Tool. You know, the future I like, especially I'm doing testing and I like playing with the tool, is like, you know, hit the load saved trips. So I can load some trips that I had previously entered. I don't live in New York but I'm like, okay, I can dream I live near Central Park and I travel around to Wall Street and I go to, you know, here is four trips per week to Wall Street, that's where I work in my imaginary life. And then I go to Chelsea Market one time a week, and then I go to Times Square another time per week.
So you can enter and, you know, remove trips. So here you see kind of this ability here to, you know, remove a trip. You can kinda get some of this back and say, oh, I want to add a different place but I'm gonna start at this apartment here and I'm gonna add back going to Times Square. So you can see, you know, typical Google Maps interface. It goes, you know, how many times per week, you can use this dropdown to do this, so do I go once per day, once per week, so forth. Is it a roundtrip or not? Do I want to save these trip? So you have to click that to actually save it for next time.
Click the add trip button, it's added there, you see it here. Here you choose your vehicle type, with the information. You could add additional mileage if you are so inclined. You know, say travel another thousand miles. And results, voila. You get kind of what I showed you, the travel time, emissions there. So, ownership, transit, Ride-Hail, all the assumptions are baked into the calculations so we can adjust those here, and you can see how the maps will change as you, you know, potentially play with the data.
You can then scroll over to the vehicle results and get, you know, more information, a little bit of like pie charts and explainers, some links about, you know, saving money, reducing time, travel time, reducing your footprint. So that's for each of the pieces. And then we have, you know, resources pages with our trip methodology here and some other good information.
I'll go to the Metric Tool and just walk you real quick, just so we leave questions. Same thing here. You know, as I showed, enter your location. We see it defaulting to the census tract specifically here, but I could choose, you know, a more specific location, not just Chicago. I can pick Argonne National Laboratory and what is the census tract, you know, that it's in. I can choose the different metric that I'm interested. I want to look at household vehicle burden, and then I might choose the overlay and this is going to adjust the map.
We can, you know, scroll out a little bit to see a little bit better as we do this and say, I want to look at places that have let's say disadvantaged communities. So here the map kind of removes the areas that are not disadvantaged communities. So the overlay is really just showing, you know, where are the disadvantaged communities in my area. So Argonne, you see the outline here. You know, no disadvantaged communities in this area, but I could change, you know, instead of census tract, I could go to the county of DuPage. There are, you know, disadvantaged communities in this county.
Here we see kind of the summary data. You know, it will change as you change the geographic resolution. We can go to the Chicago area Clean Cities location. You'll see the map, you know, adjust based on the metric you have here. The distribution of the data. You can also kind of, you know, click around the tool as well. So if you, you know, get to maybe ZIP Codes, you can like click over here and like, oh, I'm just gonna like play around, well, what about this ZIP Code, I'm interested in this one, I'm gonna click over here. So it's just kind of – has that capability to, you know, kind of click over based on which resolution you're picking. You know, same thing, you know, I want to pick the area that I'm in or the county I'm in, you know, click over here, click over the county and so forth.
So that's kind of – yeah, here's the summary tools. I guess we can – as I click over, you'll see, you know, the data adjust based on which county I'm in and this data. So. And then we have a methodology page here, explains a lot of the things that I just explained here, where the data is from, you know, how do we do these calculations, a little bit of glossary. So, that's ATRAVEL. I do have a few questions but I think since we're maybe running, you know, running late I didn't save maybe as much as time as I wanted, let's answer any questions that are in the chat. Yeah.
Sandra Loi: Okay. Sounds good, and thank you, Andy. Thank you for that presentation and going through the tool. Lots of great information in there, thank you for sharing that with us. There are just a couple of questions here. I think when you were talking about the Trip Tool, so the first section, this question came in. For the vehicle selection, can you add older model vehicles?
Andrew Burnham: So, it isn't just new at this point but it is limited to basically I think 2014 to basically model year 2021. We're working with someone to help us add additional data on that, so to kind of get to more used vehicles. So that's something that we are hoping to get into this first release but it's gonna have to be a future release where we'll get a little bit beyond that. And I appreciate – you know, I appreciate some feedback too, 'cause we were gonna cut it off at like about 15-year-old vehicle, I think 20 to 15-year-old vehicles. But if people have preferences, you know, how far back they want to look for age of vehicle that's helpful but 15 was kind of the start point where we're gonna do that, so yeah.
Sandra Loi: Okay, great, thank you. And then I think you had started diving into the Metrics Tool, this question came in. Can you have multiple overlays for one basic metric map?
Andrew Burnham: Right now we have just one – you know, we have one metric and then one overlay. So talking about like our data visualization and being able to improve upon that. So I think, yeah, that type of feedback is like, you know, what would be helpful on the data visualization side. There is a second piece, we had planned a PDF kind of summary sheet to kind of summarize whatever area you chose. We didn't get that into this version but that's for a future release that we're gonna kind of summarize a lot of the key metrics, have it in basically a one-pager PDF that you could print out or, you know, e-mail to say like, okay, here is, you know, the – this is what ATRAVEL says about area code 60616 or where – 90210 or something like that. You can choose, you know, your – or the city of Chicago or the Clean Cities Coalition. It has like – we have that capability, it's near ready but it will be pushed in the near future, so yeah.
Sandra Loi: Okay, great, thank you. And I did put in the links to ATRAVEL first to the main homepage and then to the ATRAVEL tool directly. And then that ATRAVEL@ANL.gov e-mail where you can send more feedback. And I think you can probably likely send it to Andy directly or if you want to send it to myself, I'll pass it along. And Peggy from Vermont did let us know, she'll send us some comments about mapping in rural areas, so thank you, Peggy.
And yeah, and I think as people start using it and starting – you know, start feeling it out, figuring out, oh, it would be nice to have this or why doesn't it have this or can it also do this, you know, please send those along. I think that's what Andrew is looking for today. So there is no other chat questions, so Andy, if you want to dive into these discussion questions, and then since it is a small group, if you all just want to unmute yourselves and join the conversation, I think we could do it that way. Don't be shy. Or if you want to raise your hand, I can look at that and we could do it that way.
Andrew Burnham: Yeah. I have just three kind of general questions kind of for everybody who is here. You know, and this is maybe too broad of a, you know, topic to really hit, but I think the idea was, you know, Trip Tool was focused on consumer education of personal transportation, and so if you have additional feedback, you know, what we have there, you know, what are the areas that you work on? You know, do you – you know, are you really focusing on just electrification, are you looking at different mode options, you know, what factors do you care about? And so that – I think getting that info, as we just talked about, like what are you interested in, what would be helpful, you know, around this idea of, you know, consumer education. You know, what could the tool do to help you? That would be helpful. I don't know if anybody has any thoughts there. Yeah, I'll stop talking for a second.
If not, we can – I'll click through just to see if there is any other things, you know, that we're looking for is, you know, same idea, kinda on the Metric Tool. You know, are there other metrics, data, mapping that would be helpful to yourself as a coordinator or any stakeholders who are on the call or others? So I had mentioned – I can maybe flip back to like some of the things that we're interested in doing, 'cause I know I was rushing. But, you know, we're focusing on kind of personal transportation. Maybe at some point, you know, as we're getting more data we could even have fleet transportation data in here, that kind of takes a little bit away from what ATRAVEL is doing, but that was a brainstorming thing of like, you know, some of the kind of truck travel or, you know, in your area, things like that that may be of interest as something.
And then the last one, yeah, fleet, yeah, that was my last question. Are other fleet transportation metrics. So basically, you know, the ATRAVEL is meant to be a, you know, personal transportation tool but maybe thinking down the line is there fleet related metrics that we could still use this platform to help with. So, I know, yeah, I ran – I didn't leave a lot of time –.
Sandra Loi: Yeah, it's okay.
Andrew Burnham: So if anybody does have a comment or question, it'd be appreciated. If not, you know, thank you for your time too, yeah.
Sandra Loi: So, Jacob has his hand raised, so Jacob, the floor is yours.
Jacob Beeman: Thank you. So I'm drafting up some questions to send to the ATRAVEL e-mail, but I guess while we're on. So, like, for the greenhouse gas emissions calculator, this is average annual greenhouse gas emissions per household, right?
Andrew Burnham: The Metric Tool does household, and then the Trip Tool will pick a specific vehicle and compare that travel.
Jacob Beeman: Okay, right. So, yeah, I'm thinking the Metric Tool, right now. So I know – like we're planning – or, you know, trying to look into ways to do a regional greenhouse gas emissions inventory, and you know, I see this as a – you know, if we could get like a roll up of total greenhouse gas emissions per census tract or, you know, and then all of the – kind of the geographies above that, I think that would be great, you know, so we could just kinda get a higher level, you know, county by county comparison. I imagine that could just be done in the background data, you know, multiplying these numbers by the number of households, is that –?
Andrew Burnham: Yeah. So, you know, I don't know, maybe my flipping of screens isn't working as well, but so yeah, you can kind of do it somewhat an individual basis. But if there are data requests and even that, thinking of like, hey, it would be helpful if we could maybe export some of this data. Some of the things are in a database and some we may not be able to export, but in general, like we're trying to – I'm just trying to be cautious or whatever. But in general, all this data is publicly available and, you know, we could set limits of what's available or not. So if there is some feature to export information that would be valuable, we could look at that. I think that's kind of –. And if, in the meantime, it's like, hey, we could use some of this data but, you know, it's – we'd like it for kind of a broad swath of area or something like that, we could talk about how to share that data.
Jacob Beeman: Okay. Yeah, exactly. That's – yeah, I'm always a fan of being able to export into Excel and that way we can kind of manipulate it. But yeah, that's perfect, thank you.
Andrew Burnham: Yeah, thank you, that's a great comment, thank you.
Sandra Loi: Any other questions or comments? Feel free to jump in, raise your hand. It can be kind of informal. Okay, I'm not seeing anything else. Andy, was there anything else you wanted to close out with?
Andrew Burnham: No. I – yeah, I think, you know, ATRAVEL@ANL.gov is the place to send e-mails. We've done a lot of testing of different areas but, you know, I think one of the things that we've noticed with like Google Maps is that Google Maps kind of relying on Google Maps can cause a few issues of – it can be a little finicky about some things not showing up, like transit routes not showing up based on the time of day and things like that. So we're trying to clean up some of that to make it less – you know, we're trying to clean any bugs that we have and, you know, Eric and Kendrit really did a lot of work to do that. But as you are kind of doing analysis in your specific area, and this is more on the Trip Tool, and you're doing different routes, if you see things that, hey, this is weird, you know, if you can just shoot a screenshot to ATRAVEL@ANL.gov, you know, with the place that you – you know, the route you chose or whatever, and the weird thing you saw, that would be helpful.
But yeah, you know, those are the probably, you know, key things of, you know, any feedback on what's not working on the tool, what you do like or what you'd like to see, and you know, hopefully we'll have more opportunities, either through webinars or, you know, future opportunities to kind of talk through, you know, what kind of data, what kind of information is gonna be helpful for you.
Sandra Loi: Okay, great. Well, thank you again. Thank you, Andrew, for all of that information and for the overview of the tool. And again, send him your feedback. I know he's gonna be looking for that. And if you have any other questions feel free to reach out, otherwise, but anyway, I just want to thank everyone for joining us today and as I mentioned at the beginning, we are recording this and will be posting it on the Clean Cities site within the next seven business days-ish. So thank you again and have a great day. Thank you.