Heterogeneous stream processing and crowdsourcing for traffic monitoring: Highlights

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

doi:10.1007/978-3-662-44845-8_49

Heterogeneous stream processing and crowdsourcing for traffic monitoring: Highlights

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings
Pages	520-523
Number of pages	4
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-662-44845-8_49
State	Published - 2014
Event	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014 - Nancy, France Duration: 15 Sep 2014 → 19 Sep 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	8726 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014
Country/Territory	France
City	Nancy
Period	15/09/14 → 19/09/14

Keywords

big data
crowdsourcing
event pattern matching
smart cities
stream processing
traffic

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-662-44845-8_49

Cite this

Schnitzler, F., Artikis, A., Weidlich, M., Boutsis, I., Liebig, T., Piatkowski, N., Bockermann, C., Morik, K., Kalogeraki, V., Marecek, J., Gal, A., Mannor, S., Kinane, D., & Gunopulos, D. (2014). Heterogeneous stream processing and crowdsourcing for traffic monitoring: Highlights. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings (PART 3 ed., pp. 520-523). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8726 LNAI, No. PART 3). https://doi.org/10.1007/978-3-662-44845-8_49

Schnitzler, François ; Artikis, Alexander ; Weidlich, Matthias et al. / Heterogeneous stream processing and crowdsourcing for traffic monitoring : Highlights. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings. PART 3. ed. 2014. pp. 520-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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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.",

keywords = "big data, crowdsourcing, event pattern matching, smart cities, stream processing, traffic",

author = "Fran{\c c}ois Schnitzler and Alexander Artikis and Matthias Weidlich and Ioannis Boutsis and Thomas Liebig and Nico Piatkowski and Christian Bockermann and Katharina Morik and Vana Kalogeraki and Jakub Marecek and Avigdor Gal and Shie Mannor and Dermot Kinane and Dimitrios Gunopulos",

year = "2014",

doi = "10.1007/978-3-662-44845-8_49",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Schnitzler, F, Artikis, A, Weidlich, M, Boutsis, I, Liebig, T, Piatkowski, N, Bockermann, C, Morik, K, Kalogeraki, V, Marecek, J, Gal, A , Mannor, S, Kinane, D & Gunopulos, D 2014, Heterogeneous stream processing and crowdsourcing for traffic monitoring: Highlights. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8726 LNAI, pp. 520-523, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014, Nancy, France, 15/09/14. https://doi.org/10.1007/978-3-662-44845-8_49

Heterogeneous stream processing and crowdsourcing for traffic monitoring: Highlights. / Schnitzler, François; Artikis, Alexander; Weidlich, Matthias et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings. PART 3. ed. 2014. p. 520-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8726 LNAI, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Schnitzler, François

AU - Artikis, Alexander

AU - Weidlich, Matthias

AU - Boutsis, Ioannis

AU - Liebig, Thomas

AU - Piatkowski, Nico

AU - Bockermann, Christian

AU - Morik, Katharina

AU - Kalogeraki, Vana

AU - Marecek, Jakub

AU - Gal, Avigdor

AU - Mannor, Shie

AU - Kinane, Dermot

AU - Gunopulos, Dimitrios

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KW - event pattern matching

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Y2 - 15 September 2014 through 19 September 2014

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Schnitzler F, Artikis A, Weidlich M, Boutsis I, Liebig T, Piatkowski N et al. Heterogeneous stream processing and crowdsourcing for traffic monitoring: Highlights. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings. PART 3 ed. 2014. p. 520-523. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-662-44845-8_49

Heterogeneous stream processing and crowdsourcing for traffic monitoring: Highlights

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this