[OSRM-talk] Map Matching Plugin Questions

Matthias Schwamborn schwamborn at informatik.uos.de
Thu May 7 13:29:35 UTC 2015

Thanks a bunch, Patrick! That cleared things up for me considerably.

Best, Matthias

On 07/05/15 12:40, Patrick Niklaus wrote:
>> - Did you implement all of the described HMM break conditions (route
>> localization, low probability routes, GPS outliers)? After reading the
>> code in OSRM, I was only able to find the "low probability routes"
>> condition. Did I overlook something?
> The localization is implemented by choosing the candidates before we
> start the algorithm. For each input point we adaptively chose between
> 5 and 10 candidates based on the distance to the previous input point.
> That part of the algorithm can be found in "plugins/match.hpp". The
> outliers test is not implemented, I'm not sure it would add much value
> over the limited search radius for candidates combined with the
> pruning based on transition probability.
>> - As far as I understand, MAX_DISTANCE_DELTA corresponds to the delta
>> when comparing the route length and great circle distance for the "low
>> probability routes" condition. The paper states a delta of 2000m, the
>> implementation uses a delta of 200m. Feature or bug?
> I found that 2000m is a little bit on the conservative side. At least
> for my data 200m worked pretty well (sampling period was approximately
> 7s).
> Please not that most parameters are tuned for sampling periods of
> around 5 to 10 seconds.
>> - What exactly does the "confidence" return value mean?
> Since we are dealing with real world data, matching will fail for some
> traces. That might be cause the trace is too noisy or the data from
> OpenStreetMap has problems like connectivity errors. To get a handle
> on that I gathered some empirical data on mismatched traces and tried
> to find a good feature to classify matchings are valid or invalid. The
> feature that worked best for me was the ratio between trace length and
> matching length (the intuition here is that invalid matchings tend to
> contain "loops" where detours are taken). I used that labeled data to
> fit a Laplacian distribution and constructed a naive Bayes classifier
> based on that.
> The "confidence" is the probability P(x \in valid). The values are
> only based on ~800 labeled traces which specific sampling rate, so
> take that value with a grain of salt for your data.
> What is missing is a good parameter selection based on the sample rate
> of the input. Its not clear when I will have time again to do that
> (for now massaging the data to fit the current constraints works quite
> well).
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Matthias Schwamborn

University of Osnabrück         Tel.:   +49-541-969-7167
Institute of Computer Science   Fax:    +49-541-969-2799
Albrechtstr. 28                 E-mail: schwamborn at informatik.uos.de
D-49076 Osnabrück, Germany      http://cs.uos.de/schwamborn/

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