Modelling the Energy Consumption of Driving Styles Based on Clustering of GPS Information †
Abstract
:1. Introduction
2. Motivation: On Energy Efficiency and Jerk
2.1. Basic Physical Considerations
2.2. Invariances
2.3. Formalisation
3. Driving Style Analysis by GPS Data
3.1. Data Preprocessing
- 1.
- All GPS logs having the same longitude and latitude values as well as the same sampled time as their adjacent logs. These logs represent repeated GPS values being sampled at least twice due to hardware failure.
- 2.
- All GPS logs with same longitude and latitude and having different sampled time compared with their adjacent GPS logs. These GPS logs represent a car standing still.
3.2. New Modelling and Algorithmic Adaptations
Modelling Details
Algorithmic Details
Algorithm 1: Agglomerative hierarchical clustering of movement patterns |
4. Experimental Results and Validation
4.1. Findings for Beijing City Centre Area
4.2. Findings for the Freeway Area
Summary on Comparison with Other Possible Approaches
5. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Breuß, M.; Sharifi Boroujerdi, A.; Mansouri Yarahmadi, A. Modelling the Energy Consumption of Driving Styles Based on Clustering of GPS Information. Modelling 2022, 3, 385-399. https://doi.org/10.3390/modelling3030025
Breuß M, Sharifi Boroujerdi A, Mansouri Yarahmadi A. Modelling the Energy Consumption of Driving Styles Based on Clustering of GPS Information. Modelling. 2022; 3(3):385-399. https://doi.org/10.3390/modelling3030025
Chicago/Turabian StyleBreuß, Michael, Ali Sharifi Boroujerdi, and Ashkan Mansouri Yarahmadi. 2022. "Modelling the Energy Consumption of Driving Styles Based on Clustering of GPS Information" Modelling 3, no. 3: 385-399. https://doi.org/10.3390/modelling3030025