Evaluation of real-time traffic applications based on data stream mining

Abstract

Traffic management today requires the analysis of a huge amount of data in real-time in order to provide current information about the traffic state or hazards to road users and traffic control authorities. Modern cars are equipped with several sensors which can produce useful data for the analysis of traffic situations. Using mobile communication technologies, such data can be integrated and aggregated from several cars which enables intelligent transportation systems (ITS) to monitor the traffic state in a large area at relatively low costs. However, processing and analyzing data poses numerous challenges for data management solutions in such systems. Real-time analysis with high accuracy and confidence is one important requirement in this context. We present a summary of our work on a comprehensive evaluation framework for data stream-based ITS. The goal of the framework is to identify appropriate configurations for ITS and to evaluate different mining methods for data analysis. The framework consists of a traffic simulation software, a data stream management system, utilizes data stream mining algorithms, and provides a flexible ontology-based component for data quality monitoring during data stream processing. The work has been done in the context of a project on Car-To-X communication using mobile communication networks. The results give some interesting insights for the setup and configuration 0 traffic information systems that use Car-To-X messages as primary source for deriving traffic information and also point out challenges for data stream management and data stream mining.