Map-matched trajectory compression

The wide usage of location aware devices, such as GPS-enabled cellphones or PDAs, generates vast volumes of spatiotemporal streams of location data raising management challenges, such as efficient storage and querying. Therefore, compression techniques are inevitable also in the field of moving object databases. Related work is relatively limited and mainly driven by line simplification and data sequence compression techniques. Moreover, due to the (unavoidable) erroneous measurements from GPS devices, the problem of matching the location recordings with the underlying traffic network has recently gained the attention of the research community. So far, the proposed compression techniques have not been designed for network constrained moving objects, while on the other hand, existing map matching algorithms do not take compression aspects into consideration. In this paper, we propose solutions tackling the combined, map matched trajectory compression problem, the efficiency of which is demonstrated through an extensive experimental evaluation on offline and online trajectory data using synthetic and real trajectory datasets.

► We define the problem of the compression of road map-matched moving object trajectories.
► We formulate it as a cost-optimization problem.
► We propose two algorithms – for both online and offline data – that tackle it.
► We show the effectiveness of our methods using synthetic and real trajectory data.