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Behavior measurement, analysis and regime classification in car following
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
2007 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 8, no 1, 144-156 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE Press, 2007. Vol. 8, no 1, 144-156 p.
Keyword [en]
car-following regime classification; driver behavior; fuzzy clustering algorithms; instrumented vehicle; Kalman smoothing; MODELS; SIMULATION
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-6691DOI: 10.1109/TITS.2006.883111ISI: 000244929200016Scopus ID: 2-s2.0-33847763742OAI: oai:DiVA.org:kth-6691DiVA: diva2:11471
Note

QC 20100602

Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Driver Modeling based on computational intelligence approaches: exploaration and Modeling driver-following data collected by an instrumented vehicle
Open this publication in new window or tab >>Driver Modeling based on computational intelligence approaches: exploaration and Modeling driver-following data collected by an instrumented vehicle
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

This thesis is concerned with modeling of driver behavior based on data collected from real traffic using an advanced instrumented vehicle. In particular, the focus is on driver-following behavior (often called car-following in transport science) for microscopic simulation of road traffic systems. In addition, the modeling methodology developed can be applied for the design of human-centered control algorithms in adaptive cruise control (ACC) and other longitudinal active-safety technologies.

Driver behavior is a constant research topic in the modeling of traffic systems and Intelligent Transportation Systems (ITS), which could be traced back to the work of GeneralMotor (GM) Co. in 1950’s. In the early time, researchers were only interested in the development of driver models fulfilling basic physical properties and producing reasonable flow dynamics on a macroscopic level. With the booming interest on driver modeling on a microscopic level and needs in ITS developments, researchers now emphasize modeling using microscopic data acquired from real world. To follow this research trend, a methodological framework on car-following data acquisition, analysis and modeling has been developed step by step in this thesis, and the basic idea is to build a computational model for car-following behavior by exploration of collected data. To carry out the work, different techniques within the field of modern Artificial Intelligence (AI), namely Computational Intelligence (CI)1, have been applied in the research subtasks e.g. information estimation, behavioral regime classification, regime model integration and model estimation. Therefore, a preliminary introduction of the CI methods being used in this thesis work is included in the text.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. 81 p.
Series
Trita-TEC-PHD, ISSN 1653-4468 ; 06:004
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-4253 (URN)978-91-85539-11-6 (ISBN)91-85539-11-2 (ISBN)
Public defence
2007-01-19, Sal F3, KTH, Lindstedtsvägen 26, Stockholm, 13:00 (English)
Opponent
Supervisors
Note
QC 20100602Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2010-06-02Bibliographically approved

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