Aggregation and comparison of trajectories

Abstract

Dealing with moving objects necessitates having available complete geographical traces for determining exact or possible locations that objects have had, have or will have. This is where trajectory determination plays an important role, and on which classification, aggregation and comparison methods must be built. The purpose of aggregation is to identify similar trajectories and to represent them by a single trajectory.Although much work has been done in similarity measurements for time series data, they mainly deal with one dimensional time series. On the other hand, they are good for short time series and in absence of noise, which is definitely not the case for moving objects. This paper describes different approaches to aggregate similar trajectories.

Searching for similar trajectories in spatial networks

“…In Meratnia and de By (2002), an approach is studied to aggregate similar trajectories using a grid-based spatial unit aggregation. The notion of spatial similarity lies on the neighboring cells of the grid in a standard two-dimensional Euclidean space.…”

One Way Distance: For Shape Based Similarity Search of Moving Object Trajectories

“…Recently interests of similarity search also arise in trajectory comparison [1], [2], [6], [10]- [13], [16], [18]- [20]. DTW has some variants for trajectories similarity search [12], [18].…”

“…But it still requires mapping between trajectories to be monotonic. In [13] an aggregation method based on rasters is developed for both spatial and spatialtemporal trajectories. But the method can only support aggregation queries, not similarity search queries.…”

“…Similarity search is not a new topic and has been investigated in various context, e.g., motion tracking in videos [10], [12], time series analysis [4], [5], [8], [9], [14], [16], [17], and recently, trajectories, [6], [11], [13], [16], [18]- [20]. Partly due to the difficulty in formalizing "similarity" and partly due to the diversity of applications, known results are not satisfactory in many application contexts.…”

Reconstructing spatiotemporal trajectories from sparse data

“…Thus, research focuses on forming variable clustering and aggregation techniques according to data set formations in projective spaces (Agarwal and Mustafa, 2004), sub-spaces (Gan and Wu, 2004), moving objects (Zhang and Lin, 2004), etc. Aggregation in trajectories and image data in a raster form is seen in and Meratnia and de By (2002).…”