Authors:

Alexander Artikis, Matthias Weidlich, Francois Schnitzler, Ioannis Boutsis,Thomas Liebig, Nico Piatkowski, Christian Bockermann, Katharina Morik,Vana Kalogeraki, Jakub Marecek, Avigdor Gal, Shie Mannor, Dermot Kinane and Dimitrios Gunopulos

Abstract:

Urban traffic gathers increasing interest as cities becomebigger, crowded and “smart”. We present a system for heterogeneous stream processing and crowdsourcing supporting intelligent urban traffic management. Complex events relatedto traffic congestion (trends) are detected from heterogeneous sources involving fixed 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 datasparsity, a traffic modelling component offers information in areas with low sensor coverage. We demonstrate the system with a real-world use-case from Dublin city, Ireland.

Link:

Heterogeneous Stream Processing and Crowd sourcing for Urban Traffic Management