Research Idea: Evaluation of Traffic Lane Detection with OpenStreetMap GPS Data

I am soon leaving University and thus the time for pure research will soon be over. Unfortunately I still have some ideas for possible research. I’ve tried getting them out of my head as this has not yet worked out, I’ll try to write them down – maybe somewone finds them interesting enough for a Bachelor-/Masterthesis or something like that …


OpenStreetMap creates and provides free geographic data such as street maps to anyone who wants them. The project was started because most maps you think of as free actually have legal or technical restrictions on their use, holding back people from using them in creative, productive, or unexpected ways. The OpenStreetMap approach is comparable to Wikipedia where everyone can contribute content. In openStreetMap, registered users can edit the map directly by using different editors or indirectly by providing ground truth data in terms of GPS tracks following pathes or roads. A recent study shows, that the difference between OpenStreetMap’s street network coverage for car navigation in Germany and a comparable proprietary dataset was only 9% in June 2011.

In 2010, Yihua Chen and John Krumm have published a paper at ACM GIS about “Probabilistic Modeling of Traffic Lanes from GPS Traces“. Chen and Krum apply Gaussian micture Models (GMM) on a data set of 55 shuttle vehicles driving between the Microsoft corporate buildings in the Seattle area. The vehicles were tracked for an average of 12.7 days resulting in about 20 million GPS points. By applying their algorithm to this data, they were able to infer lane structures from the given GPS tracks.

Adding and validating lane attributes completely manually is a rather tedious task for humans – especially in cases of data sets like OpenStreetMap. Therefore it should be evaluated if the proposed algorithm could be applied to OpenStreetMap data in order to infer and/or validate lane attributes on existing data in an automatic or semiautomatic way.

Continue reading Research Idea: Evaluation of Traffic Lane Detection with OpenStreetMap GPS Data

SRTM Plugin for OpenStreetMap

One of the features of our TrafficMining Project at the LMU was to use additional attributes in routing queries. Standart routing queries usually just use time and distance for calculating the fastest/shortest routes. In order to have an additional attribute we decided to use evelation data as this might be an issue if you also want to take fuel cost into account or if you’re planning a bike tour (instead of crossing a hill, you might want to consider a longer tour that avoids crossing the hill).

The problem just was that data nodes from OpenStreetMap are defined mostly by id, latitude and longitude, which is totally enough for painting 2D maps and standard routing queries. As the elevation is not added to OpenStreetMap data directly (and it is also not intended to be added to the OSM data base), a component was needed that parses both Nasa SRTM data as well as OSM data files in order to combine the data.

In the first version, we parsed the SRTM data directly and addied it to the nodes of the OSM-Graph directly. During one refactoring, we decided to integtrate Osmosis into the project in order to be able to read PBF files (another OpenStreetMap file format). During this integration we decided to separate the SRTM parsing into a separate module so that other people can make use of it as well. The plugin was open sourced some time ago at google code as the “osmosis-srtm-plugin” under an LGPL licence.

Relevant Links:

TrafficMining Project goes open source

Quite some time ago I wrote about a little demo that was published at SIGMOD 2010 and SSTD 2011 (see post1 and post2).

The TrafficMining project could be described shortly as:

An academic framework for routing algorithms based on OpenStreetMapdata. Actually this framework is not intended to replace current routing applications but to provide an easy to use GUI for testing and developing new routing algorithms on real OpenStreetMap data.

Well, what makes this worth a post is the fact that we finally switched development over to GoogleCode with a discussion group at Google Groups.
GoogleCode has the major advantage of a Mercurial repository, an issue tracker, easy code reviews and an miproved possibility to contribute code. If you just want to follow the development, just join the google group or keep a bookmark to the project’s update feed.

By the way: the PAROS and MARiO downloads can be found there in the downloads section.

Maximum Gain Round Trips with Cost Constraints

The idea is the following: Finding the shortest/fastes path from A to B is rather exploited. But if you start a hike, knowing that you want to spend 4 hours and then come back to the starting point. Then the problem suddenly starts to become a bit complex (NP-hard to be honest if you do not add any constraints).

We propose a solution to do this kind of search a bit more efficient. but don’t expect linear search time 😉 And – in contrast to quite some other research – we are operating on REAL data obtained from OpenStreetMap.


Searching for optimal ways in a network is an important task in multiple application areas such as social networks, co-citation graphs or road networks. In the majority of applications, each edge in a network is associated with a certain cost and an optimal way minimizes the cost while fulfilling a certain property, e.g connecting a start and a destination node. In this paper, we want to extend pure cost networks to so-called cost-gain networks. In this type of network, each edge is additionally associated with a certain gain. Thus, a way having a certain cost additionally provides a certain gain. In the following, we will discuss the problem of finding ways providing maximal gain while costing less than a certain budget. An application for this type of problem is the round trip problem of a traveler: Given a certain amount of time, which is the best round trip traversing the most scenic landscape or visiting the most important sights? In the following, we distinguish two cases of the problem. The first does not control any redundant edges and the second allows a more sophisticated handling of edges occurring more than once. To answer the maximum round trip queries on a given graph data set, we propose unidirectional and bidirectional search algorithms. Both types of algorithms are tested for the use case named above on real world spatial networks.


At our project site you can find:


  AUTHOR      = {F. Graf and H.-P. Kriegel and M. Schubert},
  TITLE       = {Maximum Gain Round Trips with Cost Constraints},
  INSTITUTION = {Institute for Informatics, Ludwig-Maximilians-University, Munich, Germany},
  YEAR        = {2011},
  LINK        = {}

MARiO: Multi Attribute Routing in Open Street Map

Yeah, I got a new Publication accepted at Symposium on Spatial and Temporal Databases (SSTD) 2011 that is dealing with OpenStreetMap Data (using the JXMapKit and JXMapViewer).

MARiO: Multi Attribute Routing in Open Street Map

Franz Graf, Hans-Peter Kriegel, Matthias Schubert, Matthias Renz

Published at Symposium on Spatial and Temporal Databases (SSTD) 2011
Conference Date: August 24th – 26th, 2011
Conference Location: Minneapolis, MN, USA.


In recent years, the Open Street Map (OSM) project collected a large repository of spatial network data containing a rich variety of information about traffic lights, road types, points of interest etc.. Formally, this network can be described as a multi-attribute graph, i.e. a graph considering multiple attributes when describing the traversal of an edge. In this demo, we present our framework for Multi Attribute Routing in Open Street Map (MARiO). MARiO includes methods for preprocessing OSM data by deriving attribute information and integrating additional data from external sources. There are several routing algorithms already available and additional methods can be easily added by using a plugin mechanism. Since routing in a multi-attribute environment often results in large sets of potentially interesting routes, our graphical fronted allows various views to interactively explore query results.



  AUTHOR      = {F. Graf and H.-P. Kriegel and M. Renz and M. Schubert},
  TITLE       = {{MARiO}: Multi Attribute Routing in Open Street Map},
  BOOKTITLE   = {Proceedings of the 12th International Symposium on Spatial and Temporal Databases (SSTD), Minneapolis, MN, USA},
  YEAR        = {2011}

PAROS download!

Das Ziel war nobel: Code aufräumen, schöner machen, refactoren und dokumentieren und dann online stellen.

Die Realität war derart, dass es leider wichtigeres zu tun gibt. Daher stelle ich das PAROS-Projekt, das dieses Jahr auf der SIGMOD war so online wie es ist: lauffähig, und vom Softwareengineeringaspekt ziemlich hässlich. Aber vielleicht kann ja jemand etwas damit anfangen – zumindest die kleinen hacks um größere Graphen auch annehmbar schnell zeichnen zu können.

Ausserdem ist es ein schönes Beispiel, wie man JXMapKit und OpenStreetMap (OSM) zu Forschungszwecken im Bereich Datamining, GIS (GeoInformationssysteme) und auch SpatialIndexing  verwenden kann. Auf der Konferenz kannten viele OSM nämlich erstaunlicherweise gar nicht, obwohl sie auf dem Bereich tätig waren.

Und zur nächsten Version muss ich nochmal nachsehen, ob es nach den Google Maps Terms of Services  immernoch verboten ist, Maps in Nicht-Browser-Anwendungen zu integrieren. Wäre natürlich auch sehr nett, oder weiß jemand Bescheid? (Update Jan. 2011: das ist nicht mehr verboten!)

Relevante Links: