Lossless
compression of all vehicle trajectories in a common roadway segment
David J.
Lovell
Computer-Aided
Civil and Infrastructure Engineering, vol. 33, pp. 481-497.
This article describes a method for compressing position and identification
data for files containing comprehensive trajectory records of all vehicles
traversing the same roadway segment over an arbitrary time period, in such a
way that no loss of information content occurs. Such complete trajectory
records are important for the study of traffic flow theory and could become
increasingly relevant as test data against which to study the behavior of
autonomous vehicles in a mixed traffic environment. Compression steps include
differential encoding, motion prediction, and parsimonious binary storage, plus
steps that are unique to the context of vehicle trajectories, including vehicle
ID numbers and lane occupancy. The effectiveness of the compression is due, in part,
to the strong correlations between positions (and speeds) of vehicles traveling
near each other, as well as recognition that certain trajectory artifacts
change with spatial and temporal frequencies much lower than the sampling
rates. The algorithm is demonstrated on two sets of publicly available complete
trajectory records, and compression performance statistics are given.
Compression ratios in the range 10:1 to more than 20:1 are achieved for the
sample files.