Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles
• Microscopic analysis of driving volatility in real-world connected vehicle environment. • Driving task is divided into distinct yet unobserved regimes. • Quantify the regimes, associated volatility, and regime switching durations. • Map time-series instantaneous driving volatility to surrounding d...
Ausführliche Beschreibung
Autor*in: |
Khattak, Asad J. [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Schlagwörter: |
Markov-switching dynamic regressions |
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Umfang: |
26 |
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Übergeordnetes Werk: |
Enthalten in: Dithiocarbamate-calix[4]arene functionalized gold nanoparticles as a selective and sensitive colorimetric probe for assay of metsulfuron-methyl herbicide - Rohit, Jigneshkumar V. ELSEVIER, 2016, an international journal : a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:84 ; year:2017 ; pages:48-73 ; extent:26 |
Links: |
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DOI / URN: |
10.1016/j.trc.2017.08.004 |
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Katalog-ID: |
ELV020415796 |
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Enthalten in Dithiocarbamate-calix[4]arene functionalized gold nanoparticles as a selective and sensitive colorimetric probe for assay of metsulfuron-methyl herbicide Amsterdam [u.a.] volume:84 year:2017 pages:48-73 extent:26 |
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• Microscopic analysis of driving volatility in real-world connected vehicle environment. • Driving task is divided into distinct yet unobserved regimes. • Quantify the regimes, associated volatility, and regime switching durations. • Map time-series instantaneous driving volatility to surrounding driving contexts. • Sophisticated two- and three-regime Dynamic Markov Switching models. |
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• Microscopic analysis of driving volatility in real-world connected vehicle environment. • Driving task is divided into distinct yet unobserved regimes. • Quantify the regimes, associated volatility, and regime switching durations. • Map time-series instantaneous driving volatility to surrounding driving contexts. • Sophisticated two- and three-regime Dynamic Markov Switching models. |
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• Microscopic analysis of driving volatility in real-world connected vehicle environment. • Driving task is divided into distinct yet unobserved regimes. • Quantify the regimes, associated volatility, and regime switching durations. • Map time-series instantaneous driving volatility to surrounding driving contexts. • Sophisticated two- and three-regime Dynamic Markov Switching models. |
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Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles |
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