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Advanced Training - Pre/Post-Implementation Studies

How to use Urban SDK when studying the impact of traffic calming measures you have, or will soon, implement

Jonathan Bass avatar
Written by Jonathan Bass
Updated over a week ago

In this webinar we cover how you can use Urban SDK to quickly measure the effectiveness and impacts of traffic calming implementations. The use case in this advanced training comes from a residential community that implemented nine speed bumps to calm traffic following.

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Transcript of training webinar

Intro on Implementation Studies

In our previous webinars, we've covered speeding complaints, and we've covered traffic calming.

Today is going to be a bit of an evolution of those processes, because by this point within your traffic calming program, you've determined that there's a problem, and you've moved forward with building or implementing some kind of intervention on the transportation network. Today, Urban SDK's platform will give you a little bit more of an expanded reach into your analysis. In the past, you may have only studied the road or the segment in question that received the intervention.

But now you can quickly identify many roads within the area, many segments within the area, and understand how they were impacted by the traffic calming measure in question. And the same goes for time periods. You can do a month over month comparison, or a year over year comparison of the effectiveness of these traffic calming measures.

So what we're really trying to understand through this demonstration today is how to get an extra layer of reporting from Urban SDK that'll help you understand whether you've accomplished with your traffic calming program what you set out to achieve. It'll also help you understand if any new issues have cropped up. And it should give you a clear illustration through maps and data that you can use to advocate for future projects within your community.

So with enough preamble, let's dive right in today. So as within previous webinars, we're beginning here on the Urban SDK portal splash page. I've got my credentials already loaded in, so I'm going to sign in.

Logging Into Your Account

And if you've attended our previous webinars as well, you will remember if I click the pullout panel here on the side, there's a little note at the top next to the start that says personal workspace. So personal workspace is a sandbox environment if you'd like to play around with the features within the Urban SDK platform. And if you'd like to build out any reports, maps, or test any features that you don't want to share with the public or other members of your organization.

If you do want to start sharing data with other members of your organization, you're going to want to move into your shared workspace by clicking on the dropdown menu here. And then I'm just going to be moving into the Urban SDK workspace, which is our internal workspace, but it should show your organization's name up in the top left. So now that I'm in the organizational workspace, you can see a number of previous projects we've been working on and a number of previous studies that we've been doing.

So what we're first going to do today, we're going to examine a pre and post implementation of traffic homing measures in Jacksonville, Florida, which is a home to most of our team. Our firm here is headquartered in Jacksonville. And we're going to pull data from our insights platform.

Pulling Traffic Speed Insights

We're going to bring it into or export it to our data hub and then bring it into our studio mapping software in order to do a quick bit of analysis and to build out a map to examine how traffic speeds have changed as a result of the traffic homing measures implemented. So first things first, we're going to go into our insight platform, which has been the focus of our previous two webinars to get the data in the first place. So I'm going to click on the insights beta on the left and select my geographic area.

So this is Jacksonville, Florida, which is in Duvall County. So immediately, we can pick either the entire county or we can pick a specific census tract. Because I know the area in question, I have written down the census tract number.

So we're going to pick only a quick, a single census tract in this case so that we can get the data faster because rather than loading up the entire county's worth of data into our browser and then saving it and then loading it into the studio software, we're only going to be doing that for a single census tract. Now we will still have to wait for the census tracts to all populate and load. You can see them populating on the screen on the right.

And the fun fact, for those of you who don't know, Jacksonville is the largest by land area city in America. So naturally, it takes a little while for the census tracts to populate. But now that they have, you can see the map here on the right.

I'm just going to filter down to the number that I already know is the census tract that we need to go to, which is 105.201. I click it and if I zoom in, I can see that the census tract that I selected is highlighted on the right. So I'm going to click continue. We're going to be pulling traffic speeds today.

And then, so we're going to be comparing two months of data here. We're going to be comparing data from August 2022 to August 2023. Now what I've done for sake of time and ease of convenience within our webinar today is I've already pre-pulled these two sets of data and they're already stored in DataHub.

Extracting Insights Data

What I'm going to do is I'm going to walk you through how to pull one of these sets of data for August 2022. And then you would naturally, if you wanted to pull August 2023 as well, you would repeat the process for August 2023 and you'd end up with the two different files in your DataHub. Again, for me, they're already in the DataHub because I've pulled them in preparation for today's webinar, but you would pull them by just repeating this process.

So I'm going to pull August 2023, excuse me, 2022. You'll notice that it also pulls as a supplemental data set, the June and July 2022 data. If we wanted to compare straight within our insights tool, which is what we did in our previous webinar, we would certainly take note of those two months.

Today, we're just going to be focusing on a month, a single month, August 22 and another month, August 23. So I'll click Build Report. And as you can see, the report builds fairly quickly because if we zoom out, we're only getting data for the one census tract that I selected.

Implementation Study Use Case

So the area in question, I'll give you a little bit of background on what we're going to be examining today is this area right here. This street that sort of is curvilinear street here is named Rolling River Boulevard and the east-west street here that's sort of a gateway from Rolling River to the nearby arterial road is Rolling Street and Drive. Now, if you were to go into this area and take a look, whether you lived in the area or if you went on Google Street View, you'd see this is pretty quiet, safe, suburban America.

It looks like any neighborhood, any town in America. But nevertheless, in 2021, in November, a young man, 14 years old, was tragically struck and killed in this area by a vehicle while he was crossing Rolling River Boulevard at the Rolling Stream intersection from the east side to the west side. If you were to examine on Google Street View, the road diameter, the road, excuse me, the road characteristics critically, you'd notice that there are some dangerous by design elements of this roadway.

It has a wide divided median, which turns into dual carriageways, almost like a freeway. It has lanes that are excessively wide, almost 25 feet in width in each direction. And there's no crosswalk at this intersection.

These are factors that have been noted within media reports about this young man's tragic death as potential reasons why it was dangerous for him to cross the intersection. Nevertheless, that tragedy did take place in November, 2021. And then almost two years later in June, 2023, speed bumps were added to Rolling River Boulevard in this location to calm and slow traffic with the hope that an incident like this or a fatality or serious injuries would not happen again by slowing traffic through physical traffic calming intervention measures.

Comparing Data from Pre and Post-Implementation

So what we're going to do is we're going to compare data from August 22 before those speed bumps were implemented to August 23 after those speed bumps were implemented to get a good idea of their effectiveness. So immediately we're in the insights tool here. I can hover over Rolling River Boulevard and the tool tip will give me some information about the road.

It'll tell me that the speed category, which is for those of you who don't, haven't heard on our previous webinars, an estimated speed limits model that we've developed here at Urban SDK, which I've also verified on with the ground truth data is in fact the posted speed limit in this area. Speed category is 30 miles per hour. The average speed is almost at 30 miles per hour and the 85th and 95th percentile speeds are well in excess of 30 miles per hour.

So immediately we can see this is August, 2022 before the speed bumps were implemented. Speeding is a problem on this road. If we were simply investigating a speeding complaint, we could right here conclude speeding is a problem on this road.

The speeds are either at or in excess of the posted speed limit and certainly higher than you'd expect to be safe or you'd expect at all rather for a residential area with houses fronting onto the road. But since we're gonna be taking a look at data across different time periods, we're gonna go one step further than this. First, we're going to save the data set that we have here to our data hub, which is essentially a system of record or a storage space for any data sets within the pull from the Urban SDK platform that you wanna bring into our studio mapping software or that you want to download to be able to view offline.

Exporting Data into Data Hub

So if we go to the top left here and we click the three dot more options button, we click export to data hub. It pulls up this side menu here where we can immediately put some tags in. I'm just gonna put the location tag Duval.

I could put the time tag and I can put the more specific location tag of the census tract as well. And I can then choose to export average or 85th percentile speed or both. For a quick comparison, we're only going to export 85th percentile speed today.

And I could give this a name like August 2022. I'll just say traffic data rolling. And then if I were to click the export data button here on the bottom right, that data would then immediately save to our data hub.

Now, as I said earlier, we've already exported this data. So I'm not going to re-export it here, but I'm going to go over to data hub and show you where that data is stored. I've done this for August 2022 and for August 2023.

So I'm gonna close that on the side. And if I go over here to the data hub, which is the tab on the side here that looks like a cloud, I can just click leave now. We can see immediately these are the two data sets that I've saved.

I've called them webinar data for today's webinar. But these are the traffic speed analyses in question. I've pulled 85th percentile speeds for 22, August 22 and August 23, just for that geography, that census tract in question.

They're stored here in data hub as CSVs with geo-referencing. So if you want to download them, you can click here on the data set, scroll down to the bottom right and click the download button. What will happen then is it'll download to your local machine.

And then you can load that data set into QGIS, ArcGIS, whatever GIS mapping software you want in your local machine. But one of the most, in my opinion, one of the most cool and flexible aspects of the Urban SDK platform is the studio, which is our GIS mapping software right here within the browser. So that's where I'm gonna go.

Importing Data from Data Hub to Studio

I'm gonna load up these two different data sets and we're gonna compare how speeds have changed from August 22 to August 23. So I click studio on the left side here. So as you can immediately see, this is kind of an interface that if you're familiar with a lot of GIS software should look fairly familiar to you.

There's a layers table of contents on the left. We're gonna have to add data from Data Hub, obviously. There's the ability to click filter data.

There's the ability to change interactions, which is just what factors will show up on the tool tip. And then there's the ability to change your base map. So we have the typical dark Urban SDK background here, but we also have a satellite view.

We also have an FHWA streets view. We have a lot of different views that you can use as the background for maps within Studio. Now we are gonna go a little bit more into the detail of the different aspects and things you can do with Studio in future webinars.

But today we're gonna use sort of the basics and import data and do our time pre and post implementation comparison. So within this five blue squares menu here, we'll click add data. If we were going to take data from our local machine, I would click the import file here and then I click continue.

It would open up a dialogue that would allow me to upload a geo-referenced CSV, a shape file, a zip shape file, a geo JSON file. We're pretty flexible as to the formats you can upload, but today we're of course getting data from Data Hub. So I'll click import from Data Hub and click continue.

So these are all the data sets that we just saw that were stored within our Data Hub. I'm gonna search just for webinar and I'm gonna click the two different data sets here. The CT I just used as the suffix for the census track data.

Click these two and give it a second. And we can see immediately that two data sets are loading into the platform. We'll just give it a second to load.

We've got green check marks, which is a good thing to see. Click continue. This is our data import wizard here.

Data import sort of allows you to quickly check the attribute table of data that you're going to be importing to Studio before you actually import it. It gives you a dataset quality pie chart here on the bottom right. Tells me there's 97.3 valid data, which would mean that there's an appropriate value in every single column for every single row. And then there's this 2.7%, almost 2.7% of missing data, which would indicate that there is a piece of data in that row that can be imported and can be drawn, but is missing a value somewhere within one of the columns. But this is perfectly fine for our purposes today. So click import or continue rather and then import.

Now the import process can sometimes take a moment. Again, we're pulling data from one aspect of our Urban SDK platform to another, but it should hopefully not take too long. Hopefully also that means that my internet is fine today.

If this unresponsive page dialogue comes up, we'll just click wait. I tested this out before our webinar today, so hopefully we won't be met with any technical difficulties. Okay, so immediately we can see we've got one of the two datasets loaded.

It's still trying to load the other dataset. And I'll just take another moment. Something that's interesting while waiting is that traffic combing obviously is a tiered process.

And by the way, we can see the second dataset has loaded. When I say traffic combing is a tiered process, I mean there's different levels of interventions. Sometimes the speed limit itself might be dropped with no physical interventions, or there might simply be an enforcement campaign that's set up.

Layering a Studio Map for Analysis

These measures are often well-intended by traffic engineering practitioners, but they don't always necessarily have the effect that we might want. Even awareness campaigns might not have the effect that we might want for traffic speeds to actually be slowed and lowered on our roadways. As I mentioned, the tragedy in question that we were looking at happened in November 2021.

And yet the following year, about nine months later in August 2022, despite local knowledge of the fact that a young man was killed on their roadways, there was still speeding present in the community until speed bumps were implemented. Personally, as a traffic engineering practitioner, I find this interesting. This is a great example of how physical interventions are, rather there is no substitute for physical interventions when it comes to slowing traffic on a roadway.

The best and most surefire way to slow traffic and prevent traffic safety problems is to slow traffic by influencing physical interventions. So our two datasets have been loaded here. They've drawn right on top of each other because they're the same line streams.

And if I zoom into the area we were looking at here, Rolling River and Rolling Stream, I'm going to actually quickly delete one of the layers here. So the two datasets are loaded. You can see them up here.

These are the two CSVs we were looking at. The layer is just like you would have in GIS, is just a way of drawing that data on the map. What I'm going to do is I'm going to delete one of these two, and then I'm going to put in all my symbology options here on the left to draw the map the way I want it to be drawn.

And then I'm going to copy it and just change the data source so we can look at our 22 and our 23 data in similar ways. So if I click the dropdown menu, we're going to see this is polygon data. This is the data source is August, 2023.

And it's automatically picked up the geometry field within CSV. I'm going to update our stroke width to be a little bit thicker. And then I'm going to click three dropdown menu dots here so that I can change our stroke color.

What I want to do is I want to take a look at our stroke color based on 85th percentile speed. I'm just going to pick Monday. Our data by default has all seven days of the week averaged over the month in question.

And then we have a number of different color palettes you can look at here. I know the color palette I want. I want a green to red color palette.

So I'm going to reverse. As you see, all that simply did is flip the direction of the color ramps. I'm going to go to a 10-step color ramp.

And then I'm going to pick this green to red right here. So now we have sort of a heat map, if you will, of traffic speed data within the area in question. I highlight the data set.

I can see in the tooltip in miles per hour, those average speeds across the different days of the week. As we saw here, the 85th percentile speed Monday, 29 miles per hour. It's approaching the...

This is 2023 data. This is approaching but not exceeding the speed limit of 30 miles per hour. So we were previously trying to compare 22 to 23 data.

We're already seeing reduction from what we previously saw. But now what I'm going to do is I'm going to copy this layer. Give it a second.

Duplicate the layer. And I'm going to call this August 22 data. Then what I'm going to do is I'm going to change the data source from 2022 to 20...

Or excuse me, 2023 to 2022. So now what we have is two different data sets. They're overlaid directly on top of each other.

And if I toggle this little eye icon, which hides the layer, we can actually flip back and forth between the two different data sets. The redder a link is here, the higher the speed. So what we can immediately see when we have 2022 data visible is that the road in question, Rolling River, is of a more red color palette than when we click it off and we see 2023.

Diagnosing the Report

So immediately we're seeing it looks like there has been a reduction in speed as a result of the implementation of speed bumps on this road, which is exactly what we wanted to see. If we highlight the two with our tooltip, again, I can see August 2022 data, 85th percentile speed on Mondays is about 36.6 miles per hour. But if I hide the 2022 data and I highlight 2023 data, 28.3 miles per hour. So we've seen about an eight mile per hour reduction in the 85th percentile speed as a result of the introduction of speed limits, which again is great. That's exactly what we want to see. We can also layer these two layers directly on top of each other.

What I've done is I simply changed the drawing order since the 2023 data is on top, it draws on top of the 2022 data. And if I want, I can actually change the stroke width of the 2022 data to be much wider than 2023 data so it shows underneath. And we can compare the two automatically.

So I'm going to change it to stroke width four. So very chunky data now for our 2022 data. But understanding that the chunkier, larger, thicker line width is the 2022 data.

And the thinner line in the center is 2023, we can again see we've gone from sort of orange to a light green here. We've gone from a light green to a dark green, which indicates that Rolling River Boulevard, as expected, did see a decrease in speeds as a result of the implementation of traffic calming measures. Now, an interesting piece of information we're also kind of noticing here is that although we see a decrease in speeds on Rolling River Boulevard, on the east-west, we have Rolling String Drive.

We see actually a slight increase in speeds. The 2022 was about 18.5 miles per hour as the 85th percentile on Mondays. And we see about 22 miles per hour in the 2023 data.

Now, this could simply be an artifact of a couple of particularly speedy vehicles in 2023. This is the kind of moment where I would take a look and say, OK, maybe this could merit an in-the-field investigation. But our tool has immediately given us a good diagnostic of what's changed as a result of the implementation of traffic calming measures.

So that's essentially the long and short of a pre- and post-implementation study with Urban SDK. What I'm going to quickly run through again, just for your knowledge, is how to load data into Studio. We've covered Insights a few times in our previous webinar series, which are available to re-watch online.

But just in summary, I'm going to delete the map we have here. I'm going to go back to Studio. And then to load data that we've previously saved to Data Hub from Insights, I click the Add Data button, Import, search for the dataset I want.

And then once these load, I would then click through the Continue menu at the bottom right. So since that's going to take some time, this is just a reminder of the steps that are necessary. And actually, that didn't take too long at all.

While that's working in the background, I just wanted to say thank you everyone for watching, and thank you for attending our webinar series. If there's any questions, Ashley, feel free to send them over and we can discuss.


Can I share this map with the members of the public?

Yes, you absolutely can. So since it's loading in the background, I'll have to show you. I'll have to show you in a second once it's finished loading.

But the Save button you can see here, once the map is loaded and we can click the Save button, that will not only save the map to your workspace, but it will actually then give you an option to what's called Publish the map. So if you publish the map, it gives you a publicly accessible link that you can then share with any member of the public. In fact, here, I'll show you really quickly.

I'll give it a quick name here. It's called Webinar 3 for ease of convenience. And what do we want to call this?

Let's just call this Speed Analysis. And once it's saved, there should be an option to publish, which will give you, sorry, let's just close that, which will give you a link that you can share with members of the public, anyone else within your organization, or anybody who doesn't have an Urban SDK subscription. Yeah, publish report right there.

Simply distribute this link as you wish, this link right here. And this is the ability to look at the map without changing or updating any of the data. So if you want to even provide this to members of the public at like a public information session, that's the way to do it.

What is the source of your speed data?

Yeah, of course. The source of speed data is sourced from HERE Technologies. We take the raw speed data that comes from HERE that ultimately is derived from connected vehicle data.

And we do our own QA/QC on the process. We establish our own gap fill methodology. And we aggregate it across the different time periods and make it available in a very standardized spec that is easy for our tool to pull it and present it to you in a way that gives you the best insights.

So ultimately, this is ground truth data from connected vehicles that's provided to us through HERE Technologies.

Where we can access recordings of trainings?

So as long as you guys are signed up for the webinar, which it seems you guys are since you're all here, a recording will be sent over to everyone once we're done buffering and editing out any dead air.

And then we also will have it in our Help Center. So on the bottom right here, excuse me. Hang on a second.

Gotta get out of my way of the studio. So on the bottom right here, we can immediately see the Help Center here. We can search for any help articles that we need in the Help Center here.

Or if you don't understand a place to go within our platform or have a question about our services, you can click Send Us a Message. Everyone within our firm, all the way from our intern up to our CEO, will receive the message. So whoever's best place to answer it, we'll be able to get back to you then.

We also send out a monthly newsletter. And we choose one of the two webinars that we do each month to feature in that newsletter as well.

So there's recording links there also. So lots of different places the recording will be. But you guys will all receive one directly to your inbox as soon as it's ready.

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