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Modeling Pipeline Driving Behaviors: A Hidden Markov Model Approach

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Author Info
Xi Zou
David Levinson () (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)

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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.

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File URL: http://nexus.umn.edu/Papers/HiddenMarkov.pdf
File Format: application/pdf
File Function: First version, 2007
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Publisher Info
Paper provided by University of Minnesota: Nexus Research Group in its series Working Papers with number 200607.

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Date of creation: 2006
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Publication status: 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]
Handle: RePEc:nex:wpaper:hiddenmarkov

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Find related papers by JEL classification:
R41 - Urban, Rural, and Regional Economics - - Transportation Systems - - - Transportation: Demand, Supply, and Congestion

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This page was last updated on 2008-9-30.

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