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 >> > > _______________________________________________ > OSRM-talk mailing list > [email protected] > https://lists.openstreetmap.org/listinfo/osrm-talk _______________________________________________ OSRM-talk mailing list [email protected] https://lists.openstreetmap.org/listinfo/osrm-talk
