Modeling Pipeline Driving Behaviors: A Hidden Markov Model Approach
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.
|Date of creation:||2006|
|Date of revision:|
|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]|
|Contact details of provider:|| Postal: Dept. of Civil Engineering, 500 Pillsbury Drive SE, Minneapolis, MN 55455|
Phone: +01 (612) 625-6354
Fax: +01 (612) 626-7750
Web page: http://nexus.umn.edu
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:nex:wpaper:hiddenmarkov. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (David Levinson)
If references are entirely missing, you can add them using this form.