Summary
This paper proposes a model for parameterizing time dependent origin-destination (OD) matrices. This model builds the basis for the exploitation of high frequent count data for the estimation of OD-matrices. Such data sets become increasingly available due to recent advances in automatic pedestrian counting technology paired with a business interest to analyze the number as well as the fluctuation of the number of customers for shopping centres or passengers for junctions of mass transport. OD-matrices are needed for simulation, monitoring of the demand and (depending on the location of the counting sensors) for the evaluation of the attractivity of parts of the building. The model contains a full specification of the data set including a stochastic model for the measurement errors which directly suggests a method for the estimation of the parameters.
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References
S. Seer, N. Brändle, and D. Bauer. Design of decision rules for crowd controlling using macroscopic pedestrian flow simulation. In Pedestrian and Evacuation Dynamics 2008. Springer, Berlin, 2010.
M. Bell. The estimation of origin-destination matrices by constrained generalized least squares. Transp. Res., 25B:13–22, 1991.
M. Cremer and H. Keller. A new class of dynamic methods for the identification of origin-destination flows. Transp. Res., 21B(2):117–132, 1987.
M. Maher. Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach. Transp. Res., 17B:435–447, 1983.
E. Cascetta and S. Nguyen. A unified framework for estimating or updating origin/destination matrices from traffic counts. Transp. Res., 22B:437–455, 1988.
K. Hornik, M. Stinchcombe, and H. White. Universal approximation of an unknown function and its derivatives using multilayer feedforward networks. Neural Networks, 3:551–560, 1990.
W. Newey and D. McFadden Large sample estimation and hypothesis testing. In R.F. Engle and D.L. McFadden, editors, Handbook of Econometrics, pages 2111–2245. Elsevier, Amsterdam, 1994.
D. Bauer. Estimating OD-matrices based on high frequent link volume counts. Technical report, Arsenal Research, 2008.
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Bauer, D. (2010). Modeling the Link Volume Counts as a Function of Temporally Dependent OD-Flows. In: Klingsch, W., Rogsch, C., Schadschneider, A., Schreckenberg, M. (eds) Pedestrian and Evacuation Dynamics 2008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04504-2_43
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DOI: https://doi.org/10.1007/978-3-642-04504-2_43
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