Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data
In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/14/4704 |
| _version_ | 1797526074492452864 |
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| author | Nikolaos Peppes Theodoros Alexakis Evgenia Adamopoulou Konstantinos Demestichas |
| author_facet | Nikolaos Peppes Theodoros Alexakis Evgenia Adamopoulou Konstantinos Demestichas |
| author_sort | Nikolaos Peppes |
| collection | DOAJ |
| description | In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO<sub>2</sub> emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle’s fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers’ behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver’s behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset. |
| first_indexed | 2024-03-10T09:25:02Z |
| format | Article |
| id | doaj.art-bf778c1a8d044ac1859bce1f2f678feb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:25:02Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bf778c1a8d044ac1859bce1f2f678feb2023-11-22T04:54:52ZengMDPI AGSensors1424-82202021-07-012114470410.3390/s21144704Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular DataNikolaos Peppes0Theodoros Alexakis1Evgenia Adamopoulou2Konstantinos Demestichas3Institute of Communication and Computer Systems, Zografou, 15773 Athens, GreeceInstitute of Communication and Computer Systems, Zografou, 15773 Athens, GreeceInstitute of Communication and Computer Systems, Zografou, 15773 Athens, GreeceInstitute of Communication and Computer Systems, Zografou, 15773 Athens, GreeceIn the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO<sub>2</sub> emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle’s fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers’ behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver’s behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset.https://www.mdpi.com/1424-8220/21/14/4704driving behaviour analysis (DBA)machine learning (ML)deep learning (DL)data streamingvehicle sensors |
| spellingShingle | Nikolaos Peppes Theodoros Alexakis Evgenia Adamopoulou Konstantinos Demestichas Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data Sensors driving behaviour analysis (DBA) machine learning (ML) deep learning (DL) data streaming vehicle sensors |
| title | Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data |
| title_full | Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data |
| title_fullStr | Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data |
| title_full_unstemmed | Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data |
| title_short | Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data |
| title_sort | driving behaviour analysis using machine and deep learning methods for continuous streams of vehicular data |
| topic | driving behaviour analysis (DBA) machine learning (ML) deep learning (DL) data streaming vehicle sensors |
| url | https://www.mdpi.com/1424-8220/21/14/4704 |
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