Tx-trajectory: um modelo para representação de trajetórias de táxi

Abstract

The large-scale capture of data about the motion of moving objects has increased the development of geospatial tools to analyze and present the characteristics of these objects’ behavior in many different fields. Intelligent Transportation Systems, for instance, make intensive use of data collected from embedded in-vehicle devices to analyze and monitor roads conditions and the flow of vehicles and passengers of the public transportation system. The taxi fleet is an important transport modality complementary to the public transportation system. Thus, analysis of taxis’ movements can be used to capture information about the condition of the traffic and to understand at a finer level of granularity the movement of people in an urban environment. This dissertation addresses the problem of mapping taxi raw trajectory data onto a more abstract and structured data model. The proposed data model aims to create an infrastructure to facilitate the implementation of algorithms for data mining and knowledge discovery about taxi movements and people’s behavior using this means of transport. This work also presents an implementation of the conceptual model, the strategies used to convert raw trajectory data into more abstract and semantic rich entities of the model, and an evaluation of the model through the realization of some analyses of a dataset containing almost a month of taxis trajectory data collected in a major metropolitan area.