Indeed, but carriers don't get data from the device's sensors, so they need to be a little smart to properly recognize the mode of transportation.

It's easier with historical data since you know the full trip, but even real-time should be doable.

This should be simple if there’s a beacon or Wifi on the train. Train goers will have that beacon in range on each location update while car goers will not (or may briefly if in-range of the train)

Pretty easy to train some ML network to differentiate between transport modes, assuming the device has a accelerometer / gyroscope / whatever.

Not really that easy with just IMU sensors, I’d assume ?

I did one project in school (albeit a simple SVM based model) to classify walking vs running (and so on) and ambiguity was still there. Stationary vs. Walking was easy to draw a hyper plane in between, not Running.

I still find it a difficult problem to get into, after 5 years. Perhaps ANN models might work better? Although my heart is still at Hidden MM states...

Do you have the code for your classification project? It would be great to see it on github or something.

I have it, not only the code but labelled dataset I created with an arduino, first, and then iPhone. It’s been a private repository forever, but thanks! I’ll make it public and Edit post the link down here.

(Its not pretty though...)

Edit: https://github.com/prashnts/MPU-9250

I have proper project reports I’d submitted somewhere for sure, as well. I’ll add it there in repo. I never bothered earlier.

Edit 2: this was the paper i finally submitted at school.

https://github.com/prashnts/MPU-9250/blob/master/Docs/submit...

I enjoyed reading that, thank you!

Thanks! To be more honest, this paper had a "constraint" against using FFT, simply because I didn't really know how it worked then...