| The emergence and presence of satellites and GPS devices have led to the creation of a huge amounts of spatial and spatio-temporal data, which had significant effects on creating new applications to analyze and mine these data. In this regard, a lot of research has been done on moving objects databases as a part of spatial and spatio-temporal databases. In this dissertation, we focus on those moving objects that are not allowed to move in all directions freely, but they (almost) always are restricted to travel on a specific network. One of the most popular example of these moving objects are vehicles that are supposed to travel on a Road Network. This kind of databases are called Network-constrained (or Fixed-network) Moving Object databases. First, we formalize Network-constrained Moving Object databases, and we come up with a Data Model, Data Schema, and Query Schema. Then, we introduce a data structure to index these kinds of databases. We also present a Traffic Congestion Prediction tool by using Deep Artificial Neural Network. Map Integration, Map Matching, Map Integrity are other applications in this area that we consider in this dissertation. |
