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Behavior measuremen...
Behavior measurement, analysis and regime classification in car following
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- Ma, Xiaoliang (författare)
- KTH,Centrum för trafikforskning, CTR
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- Andreasson, Ingmar (författare)
- KTH,Centrum för trafikforskning, CTR
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(creator_code:org_t)
- IEEE Press, 2007
- 2007
- Engelska.
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE Press. - 1524-9050 .- 1558-0016. ; 8:1, s. 144-156
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-behavior data, mainly car-following and lane-changing patterns, on Swedish roads. To eliminate the measurement noise in acquired car-following patterns, the Kalman smoothing algorithm was applied to the state-space model of the physical states (acceleration, speed, and position) of both instrumented and tracked vehicles. The denoised driving patterns were used in the analysis of driver properties in the car-following stage. For further modeling of car-following behavior, we developed and implemented a consolidated fuzzy clustering algorithm to classify different car-following regimes from the preprocessed data. The algorithm considers time continuity of collected driver-behavior patterns and can be more reliably applied in the classification of continuous car-following regimes when the classical fuzzy C-means algorithm gives unclear results.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering (hsv//eng)
Nyckelord
- car-following regime classification; driver behavior; fuzzy clustering algorithms; instrumented vehicle; Kalman smoothing; MODELS; SIMULATION
- TECHNOLOGY
- TEKNIKVETENSKAP
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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