Currently I'm receiving position data of moving assets every 90 or 120 seconds (depending on the asset). What I'd like to achieve now is to detect standstill and movement phases (tracks/trips) on the receiving side for these assets.

What are the generic principles of detecting tracks/trips only by GPS data?

For the most assets I'll have additional data like ignition state or other engine parameters that will help to distinguish movement and standstill. But that's not the case for all assets.

As I have no university degree and clearly lacking the mathematical basics that should help in solving such problems I'm generally interested in pointers on how to solve such a problem and sharpen my math/arithmetical skills for the future.

  • This is a space-time clustering problem. A variety of good solutions (with working code) appear at mathematica.stackexchange.com/questions/2711/….
    – whuber
    Commented May 8, 2013 at 14:56
  • Wow. That looks like hard stuff right now. Thanks for the pointer. Will check it out and try to understand what they are writing about.
    – J.D.
    Commented May 8, 2013 at 19:09
  • This question is too broad but likely answered by whuber's comment.
    – kttii
    Commented Oct 17, 2016 at 17:23

2 Answers 2


I Solved a related problem by figuring out the gps error. Mine was around 5m

Whenever 3 or more points occurred consecutively within that distance of each other, I coded it as "stopped" then, when it left that 5m radius, I coded it as "moving" again. This will occasionally give you moves just because the GPS signal jitters a bit, but is relatively accurate. It will also miss very short stops.


The MovingPandas library provides stop detection tools. To find the "meeting place", you could afterwards run density-based clustering (e.g. DBSCAN) on the extracted stops.

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