The most widely used use-case for Urban SDK customers is fielding public complaints. Our customers have found that over 60% of the speeding complaints they receive from concerned citizens do not necessitate traffic calming measures.

Below is an advanced training webinar on how you can effectively review and respond to public speeding complaints from your Urban SDK portal.

A majority of our customers here at Urban SDK use our platform to diagnose speeding complaints that we receive from the local community.

Now a lot of us have found that the complaints that we receive, and this has been communicated to us as well, don't quite measure up to a pattern that necessitates additional traffic calming measures. So what will often happen is you'll receive a complaint from a member of the community, you will take the time and resources to go into the field, deploy a speed study, whether that be someone to their physically monitor vehicles, could be Bluetooth sensors, could be anything. And the data ultimately doesn't show that there's a pattern of speeding, then this is a necessitate, excuse me, traffic calming measures, enforcement, physical measures, anything like that.

So that's the entire purpose of our webinar today, is to show you how to use the Urban SDK platform to quickly investigate or validate those complaints and to determine if you need to do more physical data collection on the roadway. By using what we're going to show you here today in Urban SDK, you should be able to respond to members of your community much quicker, and you can make sure that you're putting your resources towards roads that are actual problem areas. It's a question of resources, and it's a question of making sure those complaints are actually valid.

So without further ado, we're going to dive right into the platform. So when you load up the Urban SDK portal, we're going to see a screen, a splash screen kind of like the one that we're looking at right here. This is your Urban SDK account login.

So this is portal.urbansdk.com slash sign in. I already got my own account details preloaded. So go ahead and sign in.

And when you load up your Urban SDK account, the first thing you're going to notice is that we're in this little thing at the top that says personal workspace. So if you click the caret menu on the left, you'll notice the sidebar. At the very top, we have a star that says personal workspace.

Now, the personal workspace is an area of your Urban SDK portal where you can prepare maps that are just for yourself, basically. It's like a sandbox where you can test out how our features work. You can prepare maps, and they're not ones that are intended to be shared with other members of your organization or with the community or the public at large.

So if you do want to share maps with the community or the organization or the public at large, we're going to want to click that menu there. We're going to want to go down. Your organization's name will show up in this menu, obviously.

I'm just going to click Urban SDK. This is our own internal workspace. Now we switch to the Urban SDK workspace.

So the first thing that's going to come up is a list of recently built reports, maps, whatever else you've created in the workspace. So a lot of what we're seeing here is sample data that we've pulled for other customers. Now we're going to go to the sidebar again.

We're going to scroll down to where we have insights. And then most importantly, it should say Insights Beta. This is our Insights 2.0 product, and this is what we're going to use today. So we're going to click that. And we're immediately going to be prompted to select a geographic area. So for the purpose of our demonstration today, we're going to look at the city of Sarasota in Florida.

But what you'll do is that you're going to type your boundary, whether it's your city, MPO, you're going to type the name into this field. So I clicked city of Sarasota.

Give it a second to load. And we're going to be prompted to choose county or census tract. Now, there's a bit of a trade-off here.

If you know the exact area of the census tract that you want to look at for speed data, you can click census tract. And then if you do so, you'll immediately be prompted with a list of census tracts. You can select one or you can select multiple, and the data will only be loaded for those particular areas.

On the other hand, you can also just select county, and it will pull the entire county of data, in this case, Sarasota County. Of course, the trade-off is that this is a browser-based solution, Urban SDK Insights. So if we click county, we're going to take a little bit longer to load.

But for the purpose of our demonstration today, we're just going to load the whole county. Continue. Obviously, Sarasota County.

Continue again. And we're going to be building a traffic speed analysis today to investigate a speeding complaint in Sarasota County. So you're going to be prompted then to choose your months of data.

In this case, we already know that we're going to be picking May 2023 data. But for this particular location, we have data from June 2022 all the way to August of 2023. I'm going to pick May 2023.

And then immediately, you're going to notice something here. This is a quick review before we build the report and we dive into the map. What you're going to notice here is that there's a label that says supplemental months.

So we picked May 2023, and immediately, we're going to notice that March and April 2023 are also loaded as supplemental data. The point of this is that we're loading the two previous months of data before the month in question. So we can compare a three-month trend of speeds.

That just happens automatically when you pull a month of data in Urban SDK Insights. So click build report. Now, this may take a moment.

Depends on my own internet connection to see how fast it's going to load. Because as I said, we're pulling an entire county's worth of data. So of course, I'm hoping that's quick.

There we go. So we've pulled the entire county's worth of data. So I'm going to close the sidebar so we get a little bit better of a view here.

So a few things you're going to notice immediately. What we're looking at is, as I said, average speed data. And it's colored by default yellow, green, or red.

Now, yellow, green, or red is a mark of whether or not the speeds on the link, the average speed on the link in question is, for yellow, less than or equal to 10 miles per hour lower than the Urban SDK speed category of the link. Normal would be if there's value, if the average speed is close to that speed category. And then the red above is if we're greater than or equal to 10 miles per hour faster than the speed category.

Now, you're going to notice I've been saying the word speed category instead of posted speed limit. We'll handle that in a second, more on that in a second. But we're going to dive right into the speeding complaint that we've had.

So for this purpose of the demonstration, we're going to enter an address, and we're going to search by point of interest. We're going to search for that address so we can zoom directly in rather than having to pan and zoom over the map. So in this case, we're looking at Tuttle Elementary School in Sarasota, Florida, which is at 2863 8th Street.

I'm going to click that. And if we zoom in immediately, we're going to see this is the elementary school in question. If we want to see a slightly better view in the background, by default, we just load up this dark sort of gray map.

We can click interactions, and we can change the base map to the streets map. And give it a second to load. Again, we can see we're looking at the elementary school.

So now, if I want, I can hover over a link, and we can start taking a look at speed data. So I'm going to be taking a look at a speeding complaint on North Tuttle Avenue, which is this north-south street here that's colored green. So immediately, we see a green, which says that the average speeds on North Tuttle Avenue are close to the speed category.

So I'm going to hover over the link. We'll see immediately the tooltip shows up with some of the information here. It has the road's name, functional class, speed category, average speed, 85th and 95th percentile speeds.

So to go back for a second and talk about speed category, speed category is something that we here at Urban SDK have developed as essentially a model for predicted or estimated speed limits. What we've noticed and a problem that we've run into as we're developing this Insights product is there is no nationwide source of ground truth for speed limit data. Sometimes, these sources are, this data is just simply not available.

Sometimes, it's available in a patchwork. Sometimes, it's available, but it's not accurate. So for a nationwide scalable product within Urban SDK Insights, what we've done is we've developed a statistical model to estimate based on road functional class and other characteristics, what a reasonable speed limit would be.

Now, we understand that in some cases, this is not precisely what the actual speed limit would be. So we intend for the Urban SDK speed category to be accurate to within a plus or minus five mile per hour buffer of what the posted speed limit on the road would actually be in real life. Now, for some of you who are listening in on the call today, it's possible that we've spoken to you before and we've mentioned that if you have any geospatial data of your municipality or your MPO's speed limits, whether it's in a GIS file, a GeoJSON, anything like that, you can get in contact with our Urban SDK team and we can update your Insights tools so that we're using your actual speed limit data instead of the predicted speed category. In this case, we're using speed category and I actually have cross-referenced on Google Maps. This road is indeed 40 mile per hour.

So we nailed it with the speed category this time. So immediately we can see the average speed is just under three miles per hour slower than the speed limit speed category of 40 miles per hour. But the 85th percentile speed is four miles per hour above and the 95th percentile speed is almost eight miles per hour above the speed limit.

So 85th and 95th percentile are simply a way of saying 85th percentile is 85% of drivers on this link will travel at or below the speed. So this means that if the 85th percentile speed is four miles per hour above, 15% of all drivers on this link are traveling more than that four miles per hour above the speed limit. So we can see that although the average speed may be below the speed limit, the upper end of drivers on this link are indeed speeding.

And this is right past an elementary school. But we can dive a little bit more deeply into the speeding complaint as opposed to just looking at this data on its own. So we click the link.

We click the link and we're immediately get sort of shown this roadway segment analysis of North Tuttle Avenue on the right. This gives us all the data we were just looking at the tool tip and more.

We see the speed category, average speed, 95th and 85th percentile speeds. We also see average speeds in the AM and PM peak periods. And then if we scroll down, we're going to see this very important piece of data here.

Now, this is the average speed by period graph. For Monday through Sunday, it shows for the month in question, May 2023, what the average speeds were on that road link on a typical Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday. And it breaks those speeds down into seven typical time periods ranging from overnight, early morning through the peak hours, midday, early afternoon, PM peak hour, and then evening again.

So this allows us to look at how speeding trends change throughout the day. So while we were able to say that, yes, there's a slight speeding problem throughout the overall day as a whole, we can dive further in. And since we're looking at a road that's right next to a school, we can look at school hours, when school begins, when kids are being picked up and dropped off.

So immediately we're going to see a few trends here for this particular road. One is that speeds are higher on Saturday and Sundays. Intuitively, this should make sense to us as transportation practitioners.

There's an inverse relationship typically between speed and congestion. So lower traffic volumes on the weekend means less congestion, means higher speeds. This is just intuitive.

But there's also some more insights we can take a look at here. We can see that in the early morning and in the evening, speeds are higher than at other periods throughout the week or throughout the day. Now this could be a bit of a problem because this means that at times when there's less traffic on the road overnight, people are going to be traveling faster on this road link.

Again, that's just intuitive. But we're also noticing that during the midday, which is a time when kids might be being picked up and dropped off at school, we're seeing slightly elevated speeds as well compared to the a.m. peak or the early afternoon hours. Now this could present a speeding complaint or this could present a speeding concern rather, because while there's vulnerable road users, that is to say children, their parents picking them up and dropping off on the street, speeds are elevated.

So by taking a look at this data, broken down into the time periods, we can intuitively get a sense that there could very well be a legitimate speeding complaint here. So the speeding complaint that was received that we've simply verified with the urban SDK insights tool here could indeed be valid. And this is something that we might want to take a look at by deploying on the ground speed studies, whether that's simply sending someone out there, deploying sensors, something along those lines.

But this speeding complaint merits further investigation. Now there's a few other pieces of information we can take a look at here. If we scroll further down, we're going to see speed types by period.

Now this is essentially just a line graph that shows us very similar to what we just saw in the above graph. The speeding by period is average speed, and we've sort of broken that down into the seven time periods of the day in a line graph for average 85th and 95th percentiles of speed. And if we scroll further down, we can see speed type by month.

We're noticing essentially no change from March through May of 2023 here between those three different values. But at the same time, if there are elements of land use that change significantly, such as if we were looking at the summertime when school's not in session and congestion around the school might be lower, we might see a seasonal change. There's other communities in other areas of the country perhaps where there's a greater seasonal change related to snowfall or other extreme weather events that you might begin to see that temporal change as well.

So now that we've done our quick speeding analysis here, what we're going to do is we're going to save this report to our Urban SDK account so we can refer to it later, or we can share with other members of our organization. So we've already taken a look at the speeds here. We're going to go to the top left where we see this save report icon.

Oh, and by the way, before I save the report and exit out of it, if you notice any data problems such as speeds that don't seem realistic, that looks like there might be a problem with the roadway linear referencing system, or alternatively, if you notice any major issues with the speed categories, you can click the menu here. You can click report data issue. You can then click, you type out a commentary on the issue that you've noticed, send it to our development team, and we'll look into it and we'll rectify it for you.

So the top left, I'm going to click save report. I'm going to give us a name. So I'll say Tuttle Elementary School speeding complaint, and I'm going to click category, scroll down.

There's a lot of different test categories here. There's a lot of different categories of reports that we can save. This is just essentially a categorization to keep track of the reports that you've made for your own purposes, and this is a speeding complaint, so I'll click the speed button.

I'm going to click save, and we give it another second. This report will be saved to the workspace. Perfect.

That's been saved. So now, if I leave the Insights tool on Urban SDK, I go back to the home screen, the report should show up, and there it is in the top as a recent report from Insights 2.0, Tuttle Elementary School speeding complaint. If I go to the workspace tab on the left, see what we're seeing on the home tab is essentially just a group of recents.

We go to the workspace tab on the left here. It should show up as well, and again, there we have it. We click Insights.

We can filter down just to Insights reports as opposed to Studio reports, which is our mapping tool, dashboards, data from our data hub, which are other things that may show up in your workspace, and those will be some of those tools that I just mentioned will be the subject of future webinars in this webinar series. Today, we're focusing just on the Insights tool. So, I click it.

We can load it back up. Give it a second because, again, it's loading full county's worth of data, and we have the exact data that we were just looking at. So, I hope this has been a useful method for you to understand how to use the Urban SDK Insights tool to investigate speeding complaints in your community.

What we've done here is we've simply verified a complaint that came in and said there might actually be a legitimate reason to want to investigate speeding here.