Data Management Issues for Intelligent Transportation Systems

In this paper we discuss the technical challenges of devising a Data Stream Management System (DSMS) in the intelligent transportation scenario considered in the PEGASUS project, where the final aim is to provide reliable and timely information to improve the safety and the efficiency of vehicles’ and goods’ flows. The system should collect and integrate the large amounts of geo-located stream items coming from On Board Units (OBUs) installed on vehicles, with the aim of producing real-time maps including traffic and Points Of Interest (POIs) information to be then distributed to OBUs. OBUs’ smart navigation engines will exploit these maps to enhance mobility and provide user-targeted information. We propose a two-tiered GIS DSMS architecture where stream items are pulled from the source input stream, processed and stored in a result container to be further pulled by other operators. The system reduces the data acquisition costs by adopting communication-saving policies, supports ad-hoc strategies for reducing the storage management costs (lowering response times and memory consumption), and provides the required data access functionalities through an SQL-like query language enhanced with stream, event, spatial and temporal operators. OBU stream items are also exploited to detect Events Of Interest (EOIs) such as jams and accidents and to support a collaborative mechanism for user-powered POI management and rating. EOIs and POIs are modeled through specific ontologies which allow for a flexible and extensible data management and guarantee data independence from the raw streams.