Published March 22, 2023
| Version 0.1
Software
Open
Design of a pollution ontology-based event generation framework for the dynamic application of traffic restrictions
Authors/Creators
- 1. International University of La Rioja
- 2. University of Castilla-La Mancha
Description
Madrid City Data 2022 (District 1 and 4)
- Weather data (weather_data_2022.csv)
- Air Quality Data (aq_data_2022.csv)
- Traffic Data (traffic_data_2022.csv)
- Calendar Data (date_data_2022.csv)
- District1 (aq_final_data_district1_2022_2023_input_final)
- District4 (aq_final_data_district4_2022_2023_input_final)
Madrid City Pollution Protocol
- Pollution Protocol (PollutionProtocolSpanish.pdf)
Madrid Pollution Ontology (Neo4J)
- Docker-compose (neo4j-docker-compose.yml)
- Pollution protocol (aq_level.owl)
- Pollution Ontology image (pollution_ontology.png)
- Import Ontology (PollutionOntologyToNeo4J)
- Export ontology results (PollutionOntologyToKafka)
Statistical-based feature selection analysis
- Docker-compose (jupyter-docker-compose.yml)
- Feature selection Analysis (time-series-aq-2022.ipynb)
- LSTM-RNN tuning (aq_pass_lstm_tuning.ipynb)
Files
aq_data_2022.csv
Files
(4.3 GB)
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