New method for management of moving objects trajectories in intelligent transportation systems

There are always challenges of collecting, indexing and query processing of moving objects trajectories in the field of Moving Objects Databases (MODB). Trajectory data collected by location-aware devices are usually prone to different errors and in most situations indexing and query processing are performed on this erroneous data. As a result, responses provided for the queries that posed by various application scenarios of Intelligent Transportation Systems (ITS) and Location Based Services (LBS), have low accuracy, besides having significant time delay. In this paper, we proposed a model that, tries to reduce the errors of data using map-matching algorithms and index them by exploiting road network information. The indexing can be carried out on past and current movements separately. There is also a possibility of data reduction prior to indexing in order to improve the query processing performance, which attempts to reduce the volumes of data while keeping their quality acceptable for applications. Experiments show the improvement in accuracy and response time of different spatio-temporal queries. In addition, the effect of map-matching and data reduction on indexing methods are also studied.