Abstract
We give an overview of an intelligent urban traffic management system. Complex events related to congestions are detected from heterogeneous sources involving fixed sensors mounted on intersections and mobile sensors mounted on public transport vehicles. To deal with data veracity, sensor disagreements are resolved by crowdsourcing. To deal with data sparsity, a traffic model offers information in areas with low sensor coverage. We apply the system to a real-world use-case.
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Schnitzler, F. et al. (2014). Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44845-8_49
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DOI: https://doi.org/10.1007/978-3-662-44845-8_49
Publisher Name: Springer, Berlin, Heidelberg
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