Abstract
Air pollution is currently a great concern especially in large cities. To reduce pollution levels, governments are imposing traffic restrictions. However, the decision about which grade of traffic restriction must be applied in a particular city zone is a cumbersome task. This decision depends on the pollution scenario occurred at a time period. To face this issue, we propose an analyzable and flexible intelligent system integrating Complex Event Processing (CEP) technology and Colored Petri Net (CPN) formalism to help domain experts to conduct such a decision-making process. This system uses a CEP engine to automatically analyze and correlate real air sensing data to detect pollutant averages at a particular sensor station. This produced information is then consumed in runtime by a CPN model in charge of obtaining the pollution scenarios, which are the basis to make decisions on the traffic regulations.
This study was funded in part by the Spanish Ministry of Science and Innovation and the European Union FEDER Funds under Grants TIN2015-65845-C3-2-R, TIN2015-65845-C3-3-R and TIN2016-81978-REDT, and also by the JCCM regional projects SBPLY/17/180501/000276/1 and SBPLY/17/180501/000276/2, both of them co-financed by the European Union FEDER Funds.
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Notes
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Pt-arcs can also have delays associated, but this feature is not used in this paper.
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Indicated above the transition, by enclosing them in brackets.
- 3.
Available at: http://www.mambiente.madrid.es/sica/scripts/index.php.
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Díaz, G., Brazález, E., Macià, H., Boubeta-Puig, J., Valero, V. (2019). An Intelligent System Integrating CEP and Colored Petri Nets for Helping in Decision Making About Pollution Scenarios. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_60
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