> What’s the reason behind the data sample period of 5-10 per minute? Is it a 
> tuning to a specific “zoom level” and average vehicle speed (ie car)?

It's 5-10 seconds between samples. So its 6 to 12 samples per minute.

> Our bike GPS data is sampled every second

You can absolutely down-sample that. 5 seconds between samples is
absolutely fine in my experience (even more so for bikes, which move
slower).

> Would it possible to change the "sample rate" of the algorithm at either 
> query time or with program options?

You can try change the `map_beta` parameter, but higher sample rates
like in your case should work. The thing is that 60 samples/minute is
pretty wasteful and the request takes much longer than one for 6
samples/minute with gives a similar quality.

One thing you need to watch out for is that the result will contain
multiple sub-matchings. So in case of a bike trace that runs through
some bollards the matching on data prepared by the car profile will
most likely contain two matchings: the trace gets split at the bollard
node because the algorithm fails to find a sensible connecting route.
When you match it on bike data the trace will not get split. In this
case the confidence for each subt-race might be high in both case, the
defining difference is the splitting of the trace for cars but not for
bikes.


On Sat, May 9, 2015 at 6:29 PM, Emil Tin <[email protected]> wrote:
>
> Hi Patrick,
>
> What’s the reason behind the data sample period of 5-10 per minute? Is it a 
> tuning to a specific “zoom level” and average vehicle speed (ie car)?
>
> Our bike GPS data is sampled every second, which makes more sense for bikes 
> which don’t travel that fast. My guess is we would need this this resolution 
> to capture many of the details that separate car/foot/bike, like how you turn 
> in intersections, etc.
>
> Would it possible to change the "sample rate" of the algorithm at either 
> query time or with program options?
>
>
> Another challenge could be situations where ways are missing in OSM. But we 
> might be able to recognize these by lookng for low matching matching 
> confidence for all modes.
>
> The approach would require us to run additional OSRM instances for foto and 
> car - at the moment we only run bike.
>
>
> Emil
>
>
>
>> On 09 May 2015, at 15:49 , Patrick Niklaus <[email protected]> 
>> wrote:
>>
>> Hey Emil!
>>
>> yes that sounds like a good application for the map matching API. Good
>> catch on the missing documentation, I fixed that. :-)
>> The only problem I see is that the classification highly depends on a
>> sample periods around 5-10s.
>>
>> I'm very interested in hearing about the results of this!
>>
>> Best,
>> Patrick
>>
>>
>> On Fri, May 8, 2015 at 8:14 PM, Emil Tin <[email protected]> wrote:
>>> Hi,
>>> I’m wondering if the new map matching feature could be used for guessing
>>> travel mode?
>>>
>>> We’re currently working on adding a GPS tracking feature to our I BIke CPH
>>> ap. Both iOS and Android now come with build-in APIs for automatically
>>> detecting the travel mode, but on iOS the results are surprisingly low
>>> quliaty. Since we already use OSRM, I’m wondering it it could be used
>>> improve the detection quality.
>>>
>>> For example, suppose I have a GPS track and need to guess whether the user
>>> was biking, walking or in a car? Could I use the matching algorithm with
>>> different profiles (bike/foot/car), and get values expresssing how well the
>>> track fits each network, ie. the probability that the user was
>>> biking/walking/driving?
>>>
>>> It might be useful in siutations where the networks differ slighty due to
>>> things like:
>>>
>>> - Topology. Some ways allow only bikes/walking/cars. Some intersections
>>> provide different lanes/ways for turning by car/bike/foot.
>>> - Oneway. Streets might be oneway for cars, but not for bikes.
>>> - Barriers. You don’t usually pass stairs or bollards by car.
>>>
>>>
>>> At https://github.com/Project-OSRM/osrm-backend/wiki/Server-api is written
>>> that you can pass classify=true to get a confidence value for the matching,
>>> but it’s not mentioned in the response section?
>>>
>>>
>>> Thanks!
>>>
>>> Emil Tin
>>> CIty of Copenhagen
>>>
>>>
>>> _______________________________________________
>>> OSRM-talk mailing list
>>> [email protected]
>>> https://lists.openstreetmap.org/listinfo/osrm-talk
>>>
>>
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