 are you ready? All right JD Cronitz will be our next speaker and he will talk about using GIS to identify and characterize horizontal curvature. JD has worked as a consultant for more than 25 years serving clients in scientific, environmental, and transportation and manufacturing industries. For the past 15 years he's been primarily focused on GIS software applications for transportation, works closely with the Pennsylvania Department of Transportation, and he also teaches in Penn State's online geospatial program. Good afternoon. Today I have the pleasure of talking to you briefly about some work I did using GIS to identify horizontal curvature in roadways. So curves, as one might guess, are an important feature of roadways when it comes to highway safety. Surprisingly, many state DOTs, Departments of Transportation, don't have good inventories of their roadway curves. Often if they have curvature information it's embedded on engineering diagrams or other plans and really not in a format that is readily accessible for highway safety analysis. So in this project I basically focused on trying to extract that information and producing an inventory of Pennsylvania's horizontal curves from roadway centerline data, which is data that all state DOTs have. Now it's a priority of state DOTs to make sure that their roads are safe and to constantly try and improve safety. Generally what they're trying to do is they're trying to use a limited amount of safety dollars to apply to those roads that would best benefit, where they can make the biggest impact and get the biggest bang for the buck. The question is how do they identify their priorities? And there's really two different ways you can approach the problem. The first is by doing crash analysis. So states all have good data on their crashes and you can use the crash data to identify areas where there are high crash rates and in that manner identify your priority sections of roadway. That's a reactive approach and it may be erroneous because it could just be an anomaly. It might not really represent an area of roadway that inherently has safety issues. The other approach is to do a systemic analysis of the roadways where you're looking at the characteristics of the roadways and you're calculating expected crash rates on features of the roadway such as curvature features. Either way once you identify the priority sections of roadway you want to address and improve safety on you can implement any one of many many countermeasures or safety improvements. So here I've just shown a few and they're they're I could probably put together a hundred but for example centerline rumble strips, high friction surface treatments are just a couple. Okay so before I get into what what approach I used just a little bit on the geometry of a horizontal curve and this is kind of a complex figure but really all we're interested in are three parameters when it comes to horizontal curves. We're interested in the radius of the curve. We're interested in the length of the curve and we're interested in the central angle of the curve the number of degrees through which the curve turns. Those are the three parameters we're interested in and given any two of those parameters we can derive the third. So the approach that I used as I started with roadway centerline data for Pennsylvania and I basically took each road feature and deconstructed it into its ordered series of vertices which essentially what it represents and then for each pair of vertices I determined the straight line went through those vertices and determined the bearing angle the angle between that straight line and the positive x-axis the bearing angle and then I continued to do that for each pair of ordered vertices in sequence basically looking at how that bearing angle changed and anytime the change in bearing angle exceeded the certain threshold value I threw up a flag and said we're in a curve so that's how we identify the start of a curve continue in that process stepping through the vertices and when we get to the point that we drop below the threshold value we know the curve ended and by aggregating the change in bearing angle we can calculate the central angle of the curve we can calculate the length and from those two we can derive the radius again if we have more time to get a little bit into a little bit more detail on what the approach was but at the end of the day by using this this technique we can establish curve features that have attributes of radius, central angle, length, etc. Now to do this type of process manually would be extremely time consuming so I derived a or I created a program in Python implemented that as a custom toolbox in ArcGIS and named a curve detective and essentially automates that algorithm I just kind of walked you through so here's an example of the output of that tool you can see on superimposed or layering on top of the roadway network we have this new curve feature class that this tool created each curve in red labeled according to its central angle and radius I then went ahead and in process once I had established it worked okay I went ahead and processed all the state roadway in Pennsylvania so in Pennsylvania the state actually owns roughly 45,000 miles of roadway there's a lot more roadway in Pennsylvania that's local roads but the state actually owns and maintains about 45,000 miles of roadway in order process this roadway took the tool about two hours and it ultimately identified 170,000 or so horizontal curves then I went ahead and I I wanted to make sure that the output of the tool was legitimate that it was accurate and precise so I went ahead and found locations that had been surveyed in the field and presumably had good data on the curves and then compared those engineering diagrams which is where that data is embedded to the results of the curve detective without getting any into any great deal till I found that the the tool was very accurate and reasonably precise okay so at this point I had identified or created an inventory of horizontal curves in Pennsylvania and I wanted to basically see what what could I learn by using the crash data that we have in Pennsylvania and kind of combining the two just to see how crash rates for example differ on curves and I created a little tool in Microsoft access where I brought these two data sets together that allowed me to perform a bunch of analyses and I did conduct the number of analyses I'll just walk you through a couple briefly so in the first one I basically just looked at all the all the road sections in Pennsylvania that have a horizontal curve based on the output of the curve detective and I looked at the number of crashes of the crash rates on those sections of roadway and compared them to crash rates on straight sections of roadway and what I found is that on curves the crash rate is about 2.3 times higher than on straight sections of roadway I then went ahead and in a limited the crash I was looking at just the crashes that involve fatalities and when we just limit the fatal crashes we see that the crash rate is 2.8 times higher on curved sections of roadway as it is on straight sections of roadway so not only are crashes more frequent on curves they're generally more serious I also looked at I wanted to see the relationship between the central angle of the curve and the radius of the curve and the crash rate so I ran a series of analyses at various central angles various radii and what I saw was that there was little or no relationship between central angle and crash rate which was kind of intuitively surprising to me but there was a very strong relationship between radius and crash rate so as the radius of the curve got smaller especially as it got went below a thousand feet in radius the crash rate dramatically increased so on the vertical axis there we have crash rate on the horizontal axis we have radius and so you can see as the radius decreases the crash rate shoots up each data series here corresponds to a different central angle and you can see there's really no discernible relationship there so in conclusion this is a technique that can be implemented by implemented by any state DOT because state DOT's all have highway centerline roadway centerline data it's a technique that's very rapid it's cost effective and it can produce a highly accurate and precise inventory of a state's horizontal curves and looking at the Pennsylvania crash data in conjunction with these hard this horizontal curve data we saw that the crash rates on cars on curved sections of roadway are a lot higher than they are in straight sections of roadway and in addition for for crash the crashes that do occur on curves they tend to be much more serious or fatal more frequently than they are in straight sections of roadway and with that I will thank you for your time and you have any questions I'd be happy to take