Y. Luo and L. Bölöni

Modeling lane preferences in agent-based multi-lane highway simulation


Cite as:

Y. Luo and L. Bölöni. Modeling lane preferences in agent-based multi-lane highway simulation. In Proc. of 26th International Symposium on Computer and Information Sciences (ISCIS-2011), pp. 263–268, October 2011.

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Abstract:

Simulation of highway driving was traditionally the domain of virtual physics based models. Yet, traffic simulation is incomplete without considering the drivers' conscious strategic and tactical behavior. These aspects can be naturally simulated through an agent-based driver model, which, however, needs to act through the virtual physics model which represents the physical vehicle and the subconscious aspects of the driver behavior. In this paper, we describe a model of the strategic lane preferences of the drivers, with a special attention to the optimal lane positioning for a safe exit. Our experiments show that the simulated traffic of Orlando's Highway 408 matches well with the real world traffic data. The increased simulation detail can be applied to crash prediction and the control of intelligent transportation system devices, such as variable speed limits.

BibTeX:

@inproceedings{Luo-2011-ISCIS,
   title = "Modeling lane preferences in agent-based multi-lane highway simulation",
   author = "Y. Luo and L. B{\"o}l{\"o}ni",
   booktitle = "Proc. of 26th International Symposium on Computer and Information Sciences (ISCIS-2011)",
   year = "2011",
   month = "October",
   pages = "263--268",
   abstract = {
   Simulation of highway driving was traditionally the domain of virtual
   physics based models. Yet, traffic simulation is incomplete without
   considering the drivers' conscious strategic and tactical behavior.
   These aspects can be naturally simulated through an agent-based
   driver model, which, however, needs to act through the virtual
   physics model which represents the physical vehicle and the
   subconscious aspects of the driver behavior.
   In this paper, we describe a model of the strategic lane preferences
   of the drivers, with a special attention to the optimal lane
   positioning for a safe exit. Our experiments show that the simulated
   traffic of Orlando's Highway 408 matches well with the real world
   traffic data. The increased simulation detail can be applied to crash
   prediction and the control of intelligent transportation system
   devices, such as variable speed limits.
   }
}

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