An Open Source Analysis Toolbox For Street Network Comparison
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Graser, Anita (QGIS team, AIT Austrian Institute of Technology)
Straub, Markus (Austrian Institute of Technology)
Dragaschnig, Melitta (Austrian Institute of Technology)
This paper presents a novel open source toolbox for street network comparison based on the Sextante geoprocessing framework for the open source Geographic Information System Quantum GIS (QGIS). In the spirit of open science, the tool- box enables researchers worldwide to assess the quality of street networks such as OpenStreetMap (OSM) by calculating key performance indicators commonly used in street network comparison studies. Additionally, we suggest two new perfor- mance indicators for turn restriction and one-way street comparisons specifically aimed at testing street network quality for routing. We demonstrate the use of this toolbox by comparing OSM and the official Austrian reference graph Graph Integration Platform (GIP) in the greater Vienna region.
 Well, thanks a lot. Hi, so my name is Anita Grasser. It's a pleasure being able to present here in front of you today. What I will be presenting is some work we did in the beginning of this year. So before Kyrgyz 2.0 was out. And it's about a comparison, obviously, between Open Streetmap and the official Austrian reference graph street network in Austria. And I'll be showing you the toolbox that we developed and the results of the comparison for the city of Vienna. If I talk we, that would be myself. Then my colleague Markus Straub, who is our Open Streetmap expert, and my colleague Melita Dragaschnick, who is our expert for the Austrian reference graph. To give you a short outline of what I've been talking about, there's a short introduction into the different concepts of street network modeling that those two street graphs follow. It's needed to understand a few of the results that I will be talking about later. Then I will show which comparison indicators have been used so far. There's quite a lot of studies on street network comparison, notably a lot in the UK here, but also in Germany and the US and France, for example. So they have already introduced some indicators, which I have implemented also in the toolbox. And I will be showing the results for Vienna. Then we introduce two new indicators, which are specific for vehicle routing. So we look at the quality of the street network for vehicle routing. It's important that the one-way street directions are correct and that they are modeled, and that you have the turn restrictions which apply for the vehicles in the street network. So we have developed indicators to measure the correctness of those. I will end with the case study for Vienna before the conclusions. I'm quite sure for most of you the motivation is not something I have to dwell on for a long time. Street networks are just a really, really important input data set for all kinds of analysis. And at AIT, where I work at the mobility department, we do, of course, a lot of analyzers with traffic data. And you almost always need a street network to do any kind of real work. So of course, in a research setting, it's great to have open street map, because it's global. You can collaborate with any entity all over the world on a project. You can just share the data. It's free. You don't have to mess around with strange licenses. Can I give my street network to my partner in university-wise set? But of course, there's always been concerns, especially with the vehicle routing part. How far advanced is open street map already? And can we use it for a project? I found this quote from 2012, Applying to Germany, which basically says that, well, yeah, no, it's not that great. Yet there are not that many turn restrictions in it, like in the commercial data sets. And it will still take many years. But we decided we'd just have a look at it ourselves. And we can go on from there. Short sneak peek, what was there in the end. If you know, quantum GIS, QGIS, and formerly sextant, now renamed to processing, you can create your own tools, which you put in the toolbox. And then you can chain them together to get analysis workflows. And what I will present thing is those tools that were necessary to do the comparison between the open street map and the Austrian graph. And there are all these tiny entries in the graph operations and graph comparisons. You can get them on GitHub. They are all out there. And try them out yourself if you're interested. So back to the street networks. Open street map, I'm sure you're all familiar with. Everyone can edit. Everyone can use. On the other hand, there is the Austrian street network graph. It's created by the authorities. And currently, there's really limited access to it. You cannot even go there and say, hi, I want to buy it. No, they don't have a business model yet. So you have to know someone who knows someone who can probably get you a copy of it. But that will hopefully change. Anyways, we got our hands on a copy. And that's what I will be talking about. Another thing I'm sure many of you are aware of that for these mobility and routing applications, you need a rootable network. And by default, open street map is not rootable. You cannot just take any export and run your Dijkstra routing algorithm on it. It will not work. You need to pre-process it. And that means on the lowest level that you have to split the edges at the intersections. Because in open street map, it's perfectly fine to have an intersection model just with two edges instead of four, which you would normally have, across the intersection like this one. By just having this one node in the middle shared between the two links, that says there is an intersection and you can turn and everything is fine. But to use normal routing algorithms, you will have to split the edges here and make four edges out of it, which share the node. Another aspect of street networks is the classification of the roads, which are important and which are less important. And in open street map, there is the highway tag with this names for different road classes. And in many commercial street networks, they use a concept called functional road class, FRC. Also in the Austrian street network, they use this. And it's usually from 0 to 8 plus they introduced some others, 11, 208, which are pedestrian ways or something like that, which is not relevant for the study that I will be presenting. So we put them side by side and decide which classes match up approximately. And that's the result of this matching process. I will use these groups later on because they are comparable to show you some results. Another important aspect in street network modeling of open street map and give where there's a big difference is in how the driving permissions are modeled. In open street map, you will have default driving permissions. So on a highway, it's usually not possible that you can cycle there. That would be forbidden implicitly. You don't have to specify it anywhere. And on the Austrian graph, which is called GIP, that's the abbreviation I use every vendor slides because the real name is way too long, they have a model that driving permissions using binary codes. So there's a bit for every mode of transport, like for walking, for cycling, for cars, for taxes. It has its own bit. And you will check for every direction of the road, is it zero or one? Can I go there? Can I not go there? Is it the one way? Then it will be one in the one direction and zero in the other direction. And you have to figure that out if you want to route on that graph. So that's two completely different ways of modeling the same thing. It's kind of similar with the turn restrictions by default in open street map. If there's an intersection, you can turn. That's the default behavior. And then you can have explicit restrictions, which say you cannot go from A to B, but you can do everything else. Or you have commands, which basically said here you can only turn from A to C, nowhere else. In GIP, it's again, everything is explicit. They have entries in a list of turning relations. And everything that's not in this list is forbidden. So you can easily go through that list. And I will show you later how we use that. So that was the basics of the street networks. And now that we know those, we can compare the two. And the first part will be the indicators which are used in the literature so far. One basic indicator concerns itself with positional accuracy. It's first been used in the UK study by Mr. Hackley. And it calculates the percentage of the low accuracy representation, so the network you want to test, with the distance in the high accuracy representation, within a specified distance. I will explain it using the image. That's probably easier. So you have the high accuracy representation. That's the finery line. And you specify certain buffer distance in GIS page. And you check from the network that you want to test how big is the percentage that falls within the buffer. And is therefore positionally accurate and how much is outside that buffer zone. So I've implemented that in a tool for extant processing for quantum GIS. And you can basically see the parts coming together here. You can read the models from the top down. There's always the reference graph that would be open street map in our case. The buffer size, which you can vary depending on how generous you are with your definition of positionally accurate. Then there's the second graph, which you compare to. And I introduced a concept of regions. So you can do in one go analyze different regions at once. It's just an area, a layer, which you put in. And then it does all the buffering. The two differences are the faster way to dissolve currently. I found that the solve operation takes just forever. But with the two differences, it runs much faster. So we dissolve all the buffers. And then we get the line length within the buffer and the total line length. And we can calculate the ratio. That's basically the result of this tool. A second indicator that is also well used. I've just put a few studies on there. There's even more. They compare simply the network length per region. So they make cells. And then they check how long is the open street back network in it. And how long is the other road network, which we think is better or simply different. So here's for nothing. And I found that in the Hackplace study from 2010. And you can see there are some regions where OSM is longer than the ordnance survey graph they used and other way around. Implemented in the toolbox, you have, again, two graphs that you put in. You have those regions, again. And you compare the total graph length and out it comes the difference between the two. And the resulting layer will then be those cells with an additional attribute of the road length difference. What's also been used in some studies is just to look at attribute completeness, which, of course, is another important aspect of the street networks. So they check how big is the share of streets, which have a name applied to them, or for how big the percentage the names are still missing. That's also quite easy. In this case, you only need one graph because it's not a direct comparison in this case. You input the classification field, and then the tool will check which streets there is a null in that field, and for which street there is some value in it, and calculate the length for these two groups and compare them. When it comes to the vehicle routing features, like turn restrictions, and one way is what I found was only this one comparison by the German study, which compared the number of turn restrictions. So you count them here, you count them there, you say when there is less, then that's worse than when there is more. But for the Austrian street network, that would not work. I already explained how the modeling approaches are. So in the Austrian street network, everything is explicit. They would explain, this is forbidden, this is forbidden, this is forbidden. While in OpenStreetMap, you could just say, at this intersection, you're only allowed to turn left. So there would be only one entry instead of possibly three or more, but it would have the same information. It would be equally correct. So just counting them does not work. You have to find a different solution. So we thought of some more routing based graph comparison approaches. There is a first preparation step, which is currently done with our in-house routing software, which extracts the one-way streets and the turn restrictions from both graphs. And which is also used to calculate the shortest paths. I will explain the concept in further detail on the next few slides. And the evaluation, which follows, is then again done in quantum GSM 6-tunnel. So the first idea for turn restrictions was we can take the list of turn restrictions in the Austrian street network graph, and we construct all those forbidden turns. The forbidden turns in this graph are the black arrows. So that's what you're not allowed to do. It always starts in the middle of one road link and ends in the middle of the next road link. So you have an easier time matching between different street network representations. Because if you put it just in an intersection, it will just not know where to put the starting point for the routing that we need to do. And the second, the colorful arrow that you see, that is the result of what happens when you route between the start and the end point of the forbidden geometry. In the case where the other road network has the same knowledge about the turning restriction, it finds a way around the obstacle. And if it doesn't know about the turn restriction, it will just go happily go, turn left, because it doesn't know that it's not allowed to do that. So we generate both those geometric representations, and then we compare them. We say, if they are too similar, it means that the turn restriction was ignored. It wasn't available in the data set. And too similar, we've decided to define using how stuff distance, which is implemented here in the model, which basically just says the maximum of the minimum distances between the two data sets. It's easiest to imagine on the graphic. I think it's a bit difficult to explain. Anyway, you get a maximum distance between the two, and we used a threshold of 15 meters. So if it's further than 15 meters apart, we say, that's different enough. It probably went a different route. It didn't ignore the turn restriction. So for the one-way streets, we used a similar approach. We again extracted the one-way geometries. In this case, just really short parts in the middle of the lines. Again, that's the forbidden geometry, so to say. And we route it again. And on the left side, you see it finds a way around the one way, because it knows about it. And on the right side, it doesn't know about the one way, and it just goes through. That's the model that does that. It just calculates export and that geometry columns, calculates the length of the lines, the line geometries. And we compare them, because the extracts are always just 10 meters short. And if the route resulting in the other graph is much longer, then we know that it found a way around it. And that's the one-way was modeled correctly in open street map. So now that we know all the things that we did in theory, that's the case study for Vienna. It's the bigger Vienna region, so there's a lot of smaller towns around as well. And we looked at it on December 2012 data. For the positional accuracy, that's something you can only do for selected roads. So we looked at, like in the British study, we looked at the highways in that region. And we varied the buffer size, so the definition of what is positionally accurate. And you can see that the positional accuracy of most of them is pretty good, even if you go to only 10 meters from the center line of the geometry. There's a few bigger deviations. Those are mostly only reaching into the analysis area for really, really short parts. And there it gets difficult to distinguish between all those ramps and how they are classified. And it just gets messy there. And I wouldn't give too much on those values. Then if you remember, I said network length. That's what everyone does. So we also did network length. What you can see here are the classes that I introduced before. So there's the highways and the trunk and the secondary and a lot of others. And the tracks, which they later told us that they didn't include into the export that we got from the Austrian Street Network. So everything that's unpaved just wasn't in the export we received. So we omitted the group E for all further analysis. Anyway, OpenStreetMap is still longer in general. All the classes show the same thing. It's a bit longer than the Austrian reference graph. And that's because the Austrian reference graph is more generalized. You can see here they would have just one edge where OpenStreetMap has two. It's just on a different level of generalization. So comparing the length is again something that shows you an indicator. But it doesn't tell you whether OpenStreetMap is better or worse than the comparison network. For attribute completeness, we checked names and speed values, so the max speed in OpenStreetMap. And you can generally see that there the authoritative network is more complete. So it doesn't mean that it's correct still. And they have kind of weird names, like unnamed street. They would put in if some street doesn't have a name, whereas this OpenStreetMap probably wouldn't put anything. And in general, of course, the higher class roads are better covered. That's just to get an overview of the missing names, while the authoritative street network is pretty much completely named inside the city of Vienna. You will still get some unnamed streets in OpenStreetMap, especially those which stick out here, those areas are industrial zones, where it's probably difficult to find the street name information. The turn restrictions we also counted, so here are the counts, around 700 in OpenStreetMap and around 2,500 in GIP. So I already explained to you why that is not a really good comparison. When we apply our routing-based comparison, then we get these results. We see that in 95% of the cases, the one-way streets in OpenStreetMap and GIP would match. So if there is one in the Austrian reference graph, we find it represented in OpenStreetMap. And in 68% of turn restrictions, that's true as well. We also went out for a small region and checked on site, because now we only know the differences. We don't know who's right and who's wrong. It could be either way. So we checked in the ninth district of Vienna, which is rather small area. And there we found that OpenStreetMap errors, while they were more common, they were not all errors. They were in OpenStreetMap. They were also errors in the official reference network. So in this case, the official one had 20% to 30% failure rate of those differences. Of course, I also wanted to look at it how it looks in space. I'm a bit sorry. In the print version, you can then hopefully read the numbers. But there's a lot of one-way streets in Vienna. It always has three digits, and it's a one by one kilometer grid. The darker, the better the match. So you see the white cells around it, where the comparison shows that they don't agree, those two street networks. While in the city of Vienna, it's rather dark, and they agree well. Same for turn restrictions. There's generally fewer than one-ways. But also here, you see the further you go outside the city, the less they agree both street networks. This is already going further than what you will find in the paper that goes with this talk. But we continued over this simple feature comparison to how to actually routing results on these two network graphs compare. And we chose the center of Vienna, those 10 by 10 cells in the center. And we calculated routes in both graphs, so 10 per cell pair, making 99,000 routes, and with an average length of almost seven kilometers. And what you can see here is the distribution of language differences. So you would see if on the official Austrian network, it's 10 kilometers long, then you have this distribution of lengths in open street network. And in general, the open street map routes tend to be shorter. Not much. There's a mean difference of 17 meters. That could be because there's more shortcuts or because it lacks some restrictions. So you can just find shorter routes, which in practice are not allowed. But we need to look into more detail there. If you wanted to define how many routes are similar and to put different thresholds, how many would match. And if you're very strict with only 10 meter threshold allowed, it would be 16% of those in average seven kilometer long routes. And you could go up to 60% almost 400 meter threshold. I'm wrapping up. So we did a comparison of open street map and the Austrian reference graph for vehicle routing, a focused on vehicle routing. We found that the open street network in this analysis area was 17% longer than the official route network, because it's less generalized. But the attribute completeness, there's some catching up to do. The turn restrictions and one way street information match by 95% and 70% approximately. And you really get already a good match of the route length. So if your application is only dependent on the route length, it probably doesn't make much difference which street network you use. When you go down to the actual geometry of the routes, they might differ still, of course. So that's the toolbox. There's a link to GitHub where you can check it out. It's currently for 6.0, so with quantum GIS 1.8. But of course, I'm planning to port it to 2.0 processing. And it's an open source process for all the comparison parts. But for the preparation, there's still a bit lacking, because that was done with some in-house tools. So there could be some chance for calibration if someone would like to look into that. And then of course, we are also interested in looking at other modes of transport besides cars. So if anyone wants to talk about it later, you can find me around. Thank you. Thank you very much, Nita. Because it's calledllers forlessly that work. You know, there's a bit more control division in this, if you can say that there are two modes, for the people that have tried there. They have TOE, if you get the permission. Does this? Any other questions at all for Anita? Is there one about the host of distance? Host of distance, yeah. What does the block represent? Just describe it a little bit. The one was the, there's two geometries basically, and you compare between the geometries, there's the biggest minimum distance. So you get from all the points in geometry A to points in geometry B, and you check which is the closest one. And you do that for all combinations, and then for that list you take the maximum. Okay. Yeah. That's always helpful. Okay. Anybody else? Last one. Is there any? No? Okay, thank you very much, Anita. Okay, I can just lift it.
 Well, thanks a lot.  Hi, so my name is Anita Grasser.  It's a pleasure being able to present here in front of you  today.  What I will be presenting is some work we did in the  beginning of this year.  So before Kyrgyz 2.0 was out.  And it's about a comparison, obviously, between Open  Streetmap and the official Austrian reference graph  street network in Austria.  And I'll be showing you the toolbox that we developed and  the results of the comparison for the city of Vienna.  If I talk we, that would be myself.  Then my colleague Markus Straub, who is our Open  Streetmap expert, and my colleague Melita Dragaschnick,  who is our expert for the Austrian reference graph.  To give you a short outline of what I've been talking about,  there's a short introduction into the different concepts of  street network modeling that those two street graphs  follow.  It's needed to understand a few of the results that I will  be talking about later.  Then I will show which comparison indicators have  been used so far.  There's quite a lot of studies on street network  comparison, notably a lot in the UK here, but also in  Germany and the US and France, for example.  So they have already introduced some indicators, which I  have implemented also in the toolbox.  And I will be showing the results for Vienna.  Then we introduce two new indicators, which are specific  for vehicle routing.  So we look at the quality of the street network for  vehicle routing.  It's important that the one-way street directions are  correct and that they are modeled, and that you have  the turn restrictions which apply for the vehicles in the  street network.  So we have developed indicators to measure the  correctness of those.  I will end with the case study for Vienna before the  conclusions.  I'm quite sure for most of you the motivation is not  something I have to dwell on for a long time.  Street networks are just a really, really important  input data set for all kinds of analysis.  And at AIT, where I work at the mobility department, we do,  of course, a lot of analyzers with traffic data.  And you almost always need a street network to do any  kind of real work.  So of course, in a research setting, it's great to have  open street map, because it's global.  You can collaborate with any entity all over the world on  a project.  You can just share the data.  It's free.  You don't have to mess around with strange licenses.  Can I give my street network to my partner in  university-wise set?  But of course, there's always been concerns, especially  with the vehicle routing part.  How far advanced is open street map already?  And can we use it for a project?  I found this quote from 2012, Applying to Germany, which  basically says that, well, yeah, no, it's not that great.  Yet there are not that many turn restrictions in it, like  in the commercial data sets.  And it will still take many years.  But we decided we'd just have a look at it ourselves.  And we can go on from there.  Short sneak peek, what was there in the end.  If you know, quantum GIS, QGIS, and formerly  sextant, now renamed to processing, you can create  your own tools, which you put in the toolbox.  And then you can chain them together to get  analysis workflows.  And what I will present thing is those tools that were  necessary to do the comparison between the open  street map and the Austrian graph.  And there are all these tiny entries in the graph  operations and graph comparisons.  You can get them on GitHub.  They are all out there.  And try them out yourself if you're interested.  So back to the street networks.  Open street map, I'm sure you're all familiar with.  Everyone can edit.  Everyone can use.  On the other hand, there is the Austrian street network  graph.  It's created by the authorities.  And currently, there's really limited access to it.  You cannot even go there and say, hi, I want to buy it.  No, they don't have a business model yet.  So you have to know someone who knows someone who can  probably get you a copy of it.  But that will hopefully change.  Anyways, we got our hands on a copy.  And that's what I will be talking about.  Another thing I'm sure many of you are aware of that for  these mobility and routing applications, you need a  rootable network.  And by default, open street map is not rootable.  You cannot just take any export and run your Dijkstra  routing algorithm on it.  It will not work.  You need to pre-process it.  And that means on the lowest level that you have to split  the edges at the intersections.  Because in open street map, it's perfectly fine to have an  intersection model just with two edges instead of four,  which you would normally have, across the intersection like  this one.  By just having this one node in the middle shared between the  two links, that says there is an intersection and you can  turn and everything is fine.  But to use normal routing algorithms, you will have to  split the edges here and make four edges out of it, which  share the node.  Another aspect of street networks is the  classification of the roads, which are important and which  are less important.  And in open street map, there is the highway tag with this  names for different road classes.  And in many commercial street networks, they use a concept  called functional road class, FRC.  Also in the Austrian street network, they use this.  And it's usually from 0 to 8 plus they introduced some others,  11, 208, which are pedestrian ways or something like that,  which is not relevant for the study that I will be  presenting.  So we put them side by side and decide which classes match  up approximately.  And that's the result of this matching process.  I will use these groups later on because they are comparable  to show you some results.  Another important aspect in street network modeling of  open street map and give where there's a big difference is  in how the driving permissions are modeled.  In open street map, you will have default driving  permissions.  So on a highway, it's usually not possible that you can  cycle there.  That would be forbidden implicitly.  You don't have to specify it anywhere.  And on the Austrian graph, which is called GIP, that's  the abbreviation I use every vendor slides because the real  name is way too long, they have a model that driving  permissions using binary codes.  So there's a bit for every mode of transport, like for  walking, for cycling, for cars, for taxes.  It has its own bit.  And you will check for every direction of the road, is it  zero or one?  Can I go there?  Can I not go there?  Is it the one way?  Then it will be one in the one direction and zero in the  other direction.  And you have to figure that out if you want to  route on that graph.  So that's two completely different ways of  modeling the same thing.  It's kind of similar with the turn restrictions by default  in open street map.  If there's an intersection, you can turn.  That's the default behavior.  And then you can have explicit restrictions, which say you  cannot go from A to B, but you can do everything else.  Or you have commands, which basically said here you can  only turn from A to C, nowhere else.  In GIP, it's again, everything is explicit.  They have entries in a list of turning relations.  And everything that's not in this list is forbidden.  So you can easily go through that list.  And I will show you later how we use that.  So that was the basics of the street networks.  And now that we know those, we can compare the two.  And the first part will be the indicators which are used in  the literature so far.  One basic indicator concerns itself with  positional accuracy.  It's first been used in the UK study by Mr. Hackley.  And it calculates the percentage of the low  accuracy representation, so the network you want to test,  with the distance in the high accuracy representation,  within a specified distance.  I will explain it using the image.  That's probably easier.  So you have the high accuracy representation.  That's the finery line.  And you specify certain buffer distance in GIS page.  And you check from the network that you want to test how  big is the percentage that falls within the buffer.  And is therefore positionally accurate and how much is  outside that buffer zone.  So I've implemented that in a tool for extant processing  for quantum GIS.  And you can basically see the parts coming together here.  You can read the models from the top down.  There's always the reference graph that would be open  street map in our case.  The buffer size, which you can vary depending on how  generous you are with your definition of  positionally accurate.  Then there's the second graph, which you compare to.  And I introduced a concept of regions.  So you can do in one go analyze different regions at once.  It's just an area, a layer, which you put in.  And then it does all the buffering.  The two differences are the faster way to dissolve  currently.  I found that the solve operation takes just forever.  But with the two differences, it runs much faster.  So we dissolve all the buffers.  And then we get the line length within the buffer and the  total line length.  And we can calculate the ratio.  That's basically the result of this tool.  A second indicator that is also well used.  I've just put a few studies on there.  There's even more.  They compare simply the network length per region.  So they make cells.  And then they check how long is the open street back network  in it.  And how long is the other road network, which we think is  better or simply different.  So here's for nothing.  And I found that in the Hackplace study from 2010.  And you can see there are some regions where OSM is longer  than the ordnance survey graph they used and other way around.  Implemented in the toolbox, you have, again, two graphs that  you put in.  You have those regions, again.  And you compare the total graph length and out it comes the  difference between the two.  And the resulting layer will then be those cells with an  additional attribute of the road length difference.  What's also been used in some studies is just to look at  attribute completeness, which, of course, is another  important aspect of the street networks.  So they check how big is the share of streets, which have a  name applied to them, or for how big the percentage the  names are still missing.  That's also quite easy.  In this case, you only need one graph because it's not a  direct comparison in this case.  You input the classification field, and then the tool will  check which streets there is a null in that field, and for  which street there is some value in it, and calculate the  length for these two groups and compare them.  When it comes to the vehicle routing features, like turn  restrictions, and one way is what I found was only this one  comparison by the German study, which compared the number of  turn restrictions.  So you count them here, you count them there, you say  when there is less, then that's worse than when there is  more.  But for the Austrian street network, that would not work.  I already explained how the modeling approaches are.  So in the Austrian street network, everything is  explicit.  They would explain, this is forbidden, this is forbidden,  this is forbidden.  While in OpenStreetMap, you could just say, at this  intersection, you're only allowed to turn left.  So there would be only one entry instead of possibly  three or more, but it would have the same information.  It would be equally correct.  So just counting them does not work.  You have to find a different solution.  So we thought of some more routing based graph comparison  approaches.  There is a first preparation step, which is currently done  with our in-house routing software, which extracts the  one-way streets and the turn restrictions from both graphs.  And which is also used to calculate the shortest paths.  I will explain the concept in further detail on the next few  slides.  And the evaluation, which follows, is then again done in  quantum GSM 6-tunnel.  So the first idea for turn restrictions was we can take  the list of turn restrictions in the Austrian street network  graph, and we construct all those forbidden turns.  The forbidden turns in this graph are the black arrows.  So that's what you're not allowed to do.  It always starts in the middle of one road link and ends in  the middle of the next road link.  So you have an easier time matching between different  street network representations.  Because if you put it just in an intersection, it will just  not know where to put the starting point for the  routing that we need to do.  And the second, the colorful arrow that you see, that is  the result of what happens when you route between the start  and the end point of the forbidden geometry.  In the case where the other road network has the same  knowledge about the turning restriction, it finds a way  around the obstacle.  And if it doesn't know about the turn restriction, it will  just go happily go, turn left, because it doesn't know that  it's not allowed to do that.  So we generate both those geometric representations, and  then we compare them.  We say, if they are too similar, it means that the turn  restriction was ignored.  It wasn't available in the data set.  And too similar, we've decided to define using how stuff  distance, which is implemented here in the model, which  basically just says the maximum of the minimum  distances between the two data sets.  It's easiest to imagine on the graphic.  I think it's a bit difficult to explain.  Anyway, you get a maximum distance between the two, and  we used a threshold of 15 meters.  So if it's further than 15 meters apart, we say, that's  different enough.  It probably went a different route.  It didn't ignore the turn restriction.  So for the one-way streets, we used a similar approach.  We again extracted the one-way geometries.  In this case, just really short parts in the middle of the  lines.  Again, that's the forbidden geometry, so to say.  And we route it again.  And on the left side, you see it finds a way around the one  way, because it knows about it.  And on the right side, it doesn't know about the one way,  and it just goes through.  That's the model that does that.  It just calculates export and that geometry columns,  calculates the length of the lines, the line geometries.  And we compare them, because the extracts are always just 10  meters short.  And if the route resulting in the other graph is much  longer, then we know that it found a way around it.  And that's the one-way was modeled correctly in open  street map.  So now that we know all the things that we did in theory,  that's the case study for Vienna.  It's the bigger Vienna region, so there's a lot of smaller  towns around as well.  And we looked at it on December 2012 data.  For the positional accuracy, that's something you can only  do for selected roads.  So we looked at, like in the British study, we looked at  the highways in that region.  And we varied the buffer size, so the definition of what is  positionally accurate.  And you can see that the positional accuracy of most of  them is pretty good, even if you go to only 10 meters from  the center line of the geometry.  There's a few bigger deviations.  Those are mostly only reaching into the analysis area for  really, really short parts.  And there it gets difficult to distinguish between all those  ramps and how they are classified.  And it just gets messy there.  And I wouldn't give too much on those values.  Then if you remember, I said network length.  That's what everyone does.  So we also did network length.  What you can see here are the classes that I introduced  before.  So there's the highways and the trunk and the secondary and a  lot of others.  And the tracks, which they later told us that they didn't  include into the export that we got from the Austrian  Street Network.  So everything that's unpaved just wasn't in the export we  received.  So we omitted the group E for all further analysis.  Anyway, OpenStreetMap is still longer in general.  All the classes show the same thing.  It's a bit longer than the Austrian reference graph.  And that's because the Austrian reference graph is  more generalized.  You can see here they would have just one edge where  OpenStreetMap has two.  It's just on a different level of generalization.  So comparing the length is again something that shows  you an indicator.  But it doesn't tell you whether OpenStreetMap is better  or worse than the comparison network.  For attribute completeness, we checked names and speed  values, so the max speed in OpenStreetMap.  And you can generally see that there the authoritative  network is more complete.  So it doesn't mean that it's correct still.  And they have kind of weird names, like unnamed street.  They would put in if some street doesn't have a name,  whereas this OpenStreetMap probably wouldn't put anything.  And in general, of course, the higher class roads are  better covered.  That's just to get an overview of the missing names, while  the authoritative street network is pretty much  completely named inside the city of Vienna.  You will still get some unnamed streets in OpenStreetMap,  especially those which stick out here, those areas are  industrial zones, where it's probably difficult to find the  street name information.  The turn restrictions we also counted, so here are the  counts, around 700 in OpenStreetMap and around 2,500 in  GIP.  So I already explained to you why that is not a really good  comparison.  When we apply our routing-based comparison, then we get  these results.  We see that in 95% of the cases, the one-way streets in  OpenStreetMap and GIP would match.  So if there is one in the Austrian reference graph, we  find it represented in OpenStreetMap.  And in 68% of turn restrictions, that's true as well.  We also went out for a small region and checked on site,  because now we only know the differences.  We don't know who's right and who's wrong.  It could be either way.  So we checked in the ninth district of Vienna, which is  rather small area.  And there we found that OpenStreetMap errors, while  they were more common, they were not all errors.  They were in OpenStreetMap.  They were also errors in the official reference network.  So in this case, the official one had 20% to 30% failure  rate of those differences.  Of course, I also wanted to look at it how it looks in  space.  I'm a bit sorry.  In the print version, you can then hopefully read the  numbers.  But there's a lot of one-way streets in Vienna.  It always has three digits, and it's a one by one kilometer  grid.  The darker, the better the match.  So you see the white cells around it, where the comparison  shows that they don't agree, those two street networks.  While in the city of Vienna, it's rather dark, and they  agree well.  Same for turn restrictions.  There's generally fewer than one-ways.  But also here, you see the further you go outside the  city, the less they agree both street networks.  This is already going further than what you will find in  the paper that goes with this talk.  But we continued over this simple feature comparison to  how to actually routing results on these two network  graphs compare.  And we chose the center of Vienna, those 10 by 10 cells  in the center.  And we calculated routes in both graphs, so 10 per cell  pair, making 99,000 routes, and with an average length of  almost seven kilometers.  And what you can see here is the distribution of  language differences.  So you would see if on the official Austrian network,  it's 10 kilometers long, then you have this distribution of  lengths in open street network.  And in general, the open street map routes tend to be  shorter.  Not much.  There's a mean difference of 17 meters.  That could be because there's more shortcuts or because it  lacks some restrictions.  So you can just find shorter routes, which in practice are  not allowed.  But we need to look into more detail there.  If you wanted to define how many routes are similar and to  put different thresholds, how many would match.  And if you're very strict with only 10 meter threshold  allowed, it would be 16% of those in average seven  kilometer long routes.  And you could go up to 60% almost 400 meter threshold.  I'm wrapping up.  So we did a comparison of open street map and the Austrian  reference graph for vehicle routing, a focused on vehicle  routing.  We found that the open street network in this analysis area  was 17% longer than the official route network, because  it's less generalized.  But the attribute completeness, there's some catching up to  do.  The turn restrictions and one way street information match by  95% and 70% approximately.  And you really get already a good match of the route length.  So if your application is only dependent on the route length,  it probably doesn't make much difference which street  network you use.  When you go down to the actual geometry of the routes, they  might differ still, of course.  So that's the toolbox.  There's a link to GitHub where you can check it out.  It's currently for 6.0, so with quantum GIS 1.8.  But of course, I'm planning to port it to 2.0 processing.  And it's an open source process for all the comparison  parts.  But for the preparation, there's still a bit lacking,  because that was done with some in-house tools.  So there could be some chance for calibration if someone  would like to look into that.  And then of course, we are also interested in looking at  other modes of transport besides cars.  So if anyone wants to talk about it later, you can find me  around.  Thank you.  Thank you very much, Nita.  Because it's calledllers forlessly that work.  You know, there's a bit more control division in this,  if you can say that there are two modes, for the  people that have tried there.  They have TOE, if you get the permission.  Does this?  Any other questions at all for Anita?  Is there one about the host of distance?  Host of distance, yeah.  What does the block represent?  Just describe it a little bit.  The one was the, there's two geometries basically,  and you compare between the geometries,  there's the biggest minimum distance.  So you get from all the points in geometry A  to points in geometry B, and you check which is the closest one.  And you do that for all combinations,  and then for that list you take the maximum.  Okay.  Yeah.  That's always helpful.  Okay.  Anybody else?  Last one.  Is there any?  No?  Okay, thank you very much, Anita.  Okay, I can just lift it.
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