Behavior measurement, analysis and regime classification in car following
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
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.
car-following regime classification; driver behavior; fuzzy clustering algorithms; instrumented vehicle; Kalman smoothing; MODELS; SIMULATION
IdentifiersURN: urn:nbn:se:kth:diva-6691DOI: 10.1109/TITS.2006.883111ISI: 000244929200016ScopusID: 2-s2.0-33847763742OAI: oai:DiVA.org:kth-6691DiVA: diva2:11471
QC 201006022006-12-292006-12-292016-09-05Bibliographically approved