Working with spatial trajectories in Boost Geometry

Recently, there is a growing interest in geospatial trajectory computing. We call trajectories the sequences of time-stamped locations. As the technology for tracking moving objects becomes cheaper and more accurate, massive amounts of spatial trajectories are generated nowadays by smartphones, infrastructure, computer games, natural phenomena, and many other sources. In this talk we will present the set of tools available in Boost Geometry to work with trajectories highlighting latest as well as older library developments. Starting with more basic operations like length, distance and closest points computations between trajectories we move forward to more advanced operations like compression or simplification as well as the conceptually opposite operation of densify by interpolating or generating random points on a given trajectory. We conclude with the important topic of similarity measurements between trajectories. All implemented algorithms are parameterized by using the Boost Geometry's strategy mechanism that control the accuracy-efficiency trade-off and work for 3 different coordinate systems (namely, cartesian, spherical and ellipsoidal) each of which comes with its own advantages and limitations.