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A Bayesian Monte Carlo approach to model calibration for queuing systems
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/2006
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| Title: | A Bayesian Monte Carlo approach to model calibration for queuing systems |
| Authors: | Gurian, Patrick L.
Castro, Felipe
Chiu, Yi-Chang |
| Issue Date: | Jan-2005 |
| Citation: | Paper presented at The 84th Annual Meeting of the Transportation Research Board, Washington, DC |
| Abstract: | Calibrating models of queuing processes typically requires the collection of data on the arrival
and departure of individual vehicles. In this paper an alternative procedure is described which
uses Bayesian Monte Carlo methods to update prior estimates of model parameters using
observations of changes in queue length over time. Substantial reductions in parameter
uncertainty can be achieved by this calibration procedure. The procedure is most effective at
reducing uncertainty in arrival rates when service rates are already well characterized. A number
of transportation systems, including toll plazas and inspections stations at ports-of-entry, have
well-characterized service capacities, indicating that this method may be appropriate for use with
these systems. In general, posterior uncertainties for arrival rates and service rates are higher
than for conventional calibration procedures. This procedure is likely to be useful in cases, such
as international ports-of-entry, where security and access concerns render the collection of data
on individual vehicles infeasible, but abundant information is available on queue lengths. |
| URI: | http://hdl.handle.net/1860/2006 |
| Appears in Collections: | Faculty Research and Publications (CAEE)
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