Home

Status of trajectory analysis

Oct 7, 2013

A single trip is a Track object, a set of (cohering) trips is a Tracks object, Tracks for different IDs (persons, cars, devices, individual animals) are in TracksCollection objects. Construction generates summary statistitics (length, nr of points, duration) but also segment properties (length, duration, speed, direction; TODO are acceleration, curvature)

selection on trips/tracks

What we can do:

Tr[1:2] # returns a TracksCollection, and
Tr[2] # returns a Tracks object
Tr[2][1] # selects a Track object

selects the first two IDs; what we can't do yet:

Tr[list(1:2, 2:3)] # to select the first two trips from ID1, and trip 2 and 3 from ID2.
Tr[Muenster] # select those tracks that cross (object) Muenster

For the latter, we basically need a

over(Tr, geometry(Muenster))

to return the right indices.

simple manipulation of attributes

What we can't do yet

Tr$log_pm10 = log(Tr$pm10)

coercion:

we can already do

as(Tr, "data.frame") # convert to data.frame, trips separated by an NA record
as(Tr[1], "data.frame") # convert Tracks
as(Tr[1][1], "data.frame) # convert Track
as(Tr, "segments") # like data.frame, but record for each segment with all x0 y0 x1 y1 segments, and segment attributes

we still need:

as(Tr, "xts") # convert to time series
as(Tr, "STIDF") # convert to spatio-temporal points
as(Tr, "SpatialLines") # convert trips to SpatialLines, aggregate() would aggregate attributes
as(Tr, "SpatialPointsDataFrame") # simply "dump" as points with attributes

aggregation:

aggregate(Tr, "1 hour", mean) # compute 1-hourly mean attributes, change of geometry
aggregate(Tr, Muenster, max) # compute max of attributes, change geometry

plotting

plot(Tr)
stplot(Tr)
stplot(Tr, attr = "elevation", by = "direction")

both work, and can do sth useful, but only work for SpatialPoint geometries; if we aggregate to lines (e.g. road segments), they probably don't. TODO: all special cases, or more generic code?

Analysis

1. interpolation: predict trajectory values e.g. at exact 1-minute or 1-hour values
2. smoothing: predict the true location, using some error model for the GPS reading -> gives interpolation
3. constrained smoothing: map matching
4. sampling: random, or systematic sampling (thinning)
5. generalization: retain a smaller number of points, loosing as little as possible information

After map matching

after map matching (matching points to road segments), do we still represent location by points, or rather by e.g. the pair (road segment ID, distance from start of this ID)

Further Analysis

1. sparse trajectories, e.g. collected from fixed sensors (payments, traffic monitoring, etc)
2. comparison across trajectories, (re-)identification of individuals, privacy
3. classifying activities from movement
4. statistical models for trajectories (low-level: smoothing, high-level: identification, activity classificaiton)