Modeling Pipeline Driving Behaviors: A Hidden Markov Model Approach

Xi Zou and David Levinson

No 200607, Working Papers from University of Minnesota: Nexus Research Group

Abstract: Driving behaviors at intersection are complex because drivers have to perceive more traffic events than normal road driving and thus are exposed to more errors with safety consequences. Drivers make real-time responsesin a stochastic manner. This paper presents our study using Hidden Markov Models (HMM) to model driving behaviors at intersections. Observed vehicle movement data are used to build up the model. A single HMM is used to cluster the vehicle movements when they are close to intersection. The re-estimated clustered HMMs provide better prediction of the vehicle movements compared to traditional car-following models. Only through vehicles on major roads are considered in this paper.

JEL-codes: R41 (search for similar items in EconPapers)
Date: 2006
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

Published in Journal of the Transportation Research Board: Transportation Research Record #1980 (Driver Behavior, Older Drivers, Simulation, User Information Systems, and Visualization) pp. 16-23 [ISBN: 0309099900]

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http://hdl.handle.net/11299/179934 First version, 2007 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:nex:wpaper:hiddenmarkov

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