I don't really know what's possible, and would like a pointer in the right direction. Many thanks.
I have measurements of time and position which could be anything from a person walking, a vehicle on a road, or in a car park, or a printer in an office. I need to work out journey times for vehicles between two points. It may be they take a meandering route, or even take days to get from A to B. Or they may be a pedestrian, or an emergency service vehicle.
I want the estimated journey time for a normal vehicle along the main route.
The detections are whenever someone is near enough to the detector, which has a particular radius. Sometimes there are very few detections, which probably means the road is empty and journey time would be good, although it could indicate the road is closed, and the journey time would be terrible. Or there could be lots of detections showing traffic not moving, and it could be queueing to turn off the road, but other vehicles are travelling at normal speeds.
The plots look like random noise.
At the moment I am looking at two methods:
- Use the interquartile range to discard outliers
- Use a Kalman filter.
I think the filter is the wrong way to go as I don't have a model for the journey time other than from moment to moment I don't expect it to change much.