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Scientific paper - Original scientific paper
Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology
Energies, 15 (2022), 11; 4108-4129. https://doi.org/10.3390/en15114108
Dabčević, Zvonimir; Škugor, Branimir; Topić, Jakov; Deur, Joško

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

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Dabčević, Z., Škugor, B., Topić, J. & Deur, J. (2022). Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology. Energies, 15. (11), 4108-4129. doi: 10.3390/en15114108

Dabčević, Zvonimir, et al. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology." Energies, vol. 15, no. 11, 2022, pp. 4108-4129. https://doi.org/10.3390/en15114108

Dabčević, Zvonimir, Branimir Škugor, Jakov Topić and Joško Deur. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology." Energies 15, no. 11 (2022): 4108-4129. https://doi.org/10.3390/en15114108

Dabčević, Z., et al. (2022) 'Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology', Energies, 15(11), pp. 4108-4129. doi: 10.3390/en15114108

Dabčević Z, Škugor B, Topić J, Deur J. Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology. Energies [Internet]. 2022 June 02 [cited 2024 November 29];15(11):4108-4129. doi: 10.3390/en15114108

Z. Dabčević, B. Škugor, J. Topić and J. Deur, "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology", Energies, vol. 15, no. 11, pp. 4108-4129, June 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:235:321736. [Accessed: 29 November 2024]

Metadata
Title (english)Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology
AuthorZvonimir Dabčević
AuthorBranimir Škugor
AuthorJakov Topić
AuthorJoško Deur
Author's institutionUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
(Department of Robotics and Production System Automation)
(Chair of Engineering Automation)
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract (english)
The authors of this paper propose a Markov-chain-based method for the synthesis of naturalistic, high-sampling-rate driving cycles based on the route segment statistics extracted from low-sampling-rate vehicle-tracking data. In the considered case of a city bus transport system, the route segments correspond to sections between two consecutive bus stations. The route segment statistics include segment lengths and maps of average velocity, station stop time, and station-stopping probability, all given along the day on an hourly basis. In the process of driving cycle synthesis, the transition probability matrix is built up based on the high-sampling-rate driving cycles purposely recorded in a separate reference city. The particular emphasis of the synthesis process is on satisfying the route segment velocity and acceleration boundary conditions, which may be equal to or greater than zero depending on whether a bus stops or passes a station. This enables concatenating the synthesized consecutive micro-cycles into the full-trip driving cycle. The synthesis method was validated through an extensive statistical analysis of generated driving cycles, including computational efficiency aspects.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleEnergies
Numberingvol. 15, no. 11, pp. 4108-4129
p-ISSN1996-1073
DOIhttps://doi.org/10.3390/en15114108
URN:NBNurn:nbn:hr:235:321736
Publication2022-06-02
Document URLhttps://www.mdpi.com/1996-1073/15/11/4108
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-07-05 11:16:22