Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights
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.
Cite
Text
Schnitzler et al. "Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44845-8_49Markdown
[Schnitzler et al. "Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/schnitzler2014ecmlpkdd-heterogeneous/) doi:10.1007/978-3-662-44845-8_49BibTeX
@inproceedings{schnitzler2014ecmlpkdd-heterogeneous,
title = {{Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights}},
author = {Schnitzler, François and Artikis, Alexander and Weidlich, Matthias and Boutsis, Ioannis and Liebig, Thomas and Piatkowski, Nico and Bockermann, Christian and Morik, Katharina and Kalogeraki, Vana and Marecek, Jakub and Gal, Avigdor and Mannor, Shie and Kinane, Dermot and Gunopulos, Dimitrios},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {520-523},
doi = {10.1007/978-3-662-44845-8_49},
url = {https://mlanthology.org/ecmlpkdd/2014/schnitzler2014ecmlpkdd-heterogeneous/}
}