Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management

Urban trac gathers increasing interest as cities become bigger, crowded and \smart". We present a system for heterogeneous stream processing and crowdsourcing supporting intelligent urban trac management. Complex events related to trac congestion (trends) are detected from heterogeneous sources involving xed sensors mounted on intersections and mobile sensors mounted on public transport vehicles. To deal with data veracity, a crowdsourcing component handles and resolves sensor disagreement. Furthermore, to deal with data sparsity, a trac modelling component oers information in areas with low sensor coverage. We demonstrate the system with a real-world use-case from Dublin city, Ireland.