Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks
We exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution is controlled and public services are improved...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/14/4733 |
| _version_ | 1797526032171925504 |
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| author | Federico Delussu Faisal Imran Christian Mattia Rosa Meo |
| author_facet | Federico Delussu Faisal Imran Christian Mattia Rosa Meo |
| author_sort | Federico Delussu |
| collection | DOAJ |
| description | We exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution is controlled and public services are improved. We deploy heuristic algorithms and exhaustive ones to generate Bayesian networks among the monitored variables. The aim is to describe the relevant relationships between the variables, to discover and confirm the possible cause–effect relationships, to predict the fuel consumption dependent on the contextual conditions of traffic, and to enable an intervention analysis to be conducted on the variables so that our goals are achieved. We propose a validation technique using Bayesian networks based on Granger causality: it relies upon observations of the time series formed by successive values of the variables in time. We use the same method based on Granger causality to rank the Bayesian networks obtained as well. A comparison of the Bayesian networks discovered against the ground truth is proposed in a synthetic data set, specifically generated for this study: the results confirm the validity of the Bayesian networks that agree on most of the existing relationships. |
| first_indexed | 2024-03-10T09:24:25Z |
| format | Article |
| id | doaj.art-216cab20b0a5460992143021da8be11e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:24:25Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-216cab20b0a5460992143021da8be11e2023-11-22T04:55:16ZengMDPI AGSensors1424-82202021-07-012114473310.3390/s21144733Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian NetworksFederico Delussu0Faisal Imran1Christian Mattia2Rosa Meo3Dipartimento di Informatica, University of Torino, 10149 Turin, ItalyDipartimento di Informatica, University of Torino, 10149 Turin, ItalyDipartimento di Matematica G. Peano, University of Torino, 10123 Turin, ItalyDipartimento di Informatica, University of Torino, 10149 Turin, ItalyWe exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution is controlled and public services are improved. We deploy heuristic algorithms and exhaustive ones to generate Bayesian networks among the monitored variables. The aim is to describe the relevant relationships between the variables, to discover and confirm the possible cause–effect relationships, to predict the fuel consumption dependent on the contextual conditions of traffic, and to enable an intervention analysis to be conducted on the variables so that our goals are achieved. We propose a validation technique using Bayesian networks based on Granger causality: it relies upon observations of the time series formed by successive values of the variables in time. We use the same method based on Granger causality to rank the Bayesian networks obtained as well. A comparison of the Bayesian networks discovered against the ground truth is proposed in a synthetic data set, specifically generated for this study: the results confirm the validity of the Bayesian networks that agree on most of the existing relationships.https://www.mdpi.com/1424-8220/21/14/4733bayesian networksgranger causalityhill climbingbrute forcefuel reductionpublic transportation |
| spellingShingle | Federico Delussu Faisal Imran Christian Mattia Rosa Meo Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks Sensors bayesian networks granger causality hill climbing brute force fuel reduction public transportation |
| title | Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks |
| title_full | Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks |
| title_fullStr | Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks |
| title_full_unstemmed | Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks |
| title_short | Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks |
| title_sort | fuel prediction and reduction in public transportation by sensor monitoring and bayesian networks |
| topic | bayesian networks granger causality hill climbing brute force fuel reduction public transportation |
| url | https://www.mdpi.com/1424-8220/21/14/4733 |
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