Abstract
For most Origin–Destination (OD) matrix estimation methods, a priori information in the form of a matrix (so-called a priori matrix) is necessary as an initial guess. In the estimation process, this matrix is updated with traffic counts until a final estimated matrix has been found. The more this a priori matrix matches the real matrix, the better the final outcome of the estimation will be.
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- 1.
Data from local traffic sensors are, for instance, vehicle counts, flows, time or harmonic mean speeds, “local” densities, proportions of vehicle types, vehicle lengths, etc.
- 2.
A statistical measure for capturing the variation of a given set of data points.
- 3.
This variable was tested in a sensitivity analysis.
- 4.
This variable was tested in a sensitivity analysis.
- 5.
This variable was tested in a sensitivity analysis.
- 6.
In order to simplify the calculations and decrease the size of the dataset, all measurements from outside the study area were deleted. By doing that, rules 2 and 3 were combined. Hereafter the parameter for these combined rules will be referred to as break.
- 7.
Intrazonal trips are trips that start and end in the same zone.
- 8.
Interzonal trips are trips that start and end in different zones.
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Ásmundsdóttir, R., Chen, Y., van Zuylen, H.J. (2010). Dynamic Origin–Destination Matrix Estimation Using Probe Vehicle Data as A Priori Information. In: Barceló, J., Kuwahara, M. (eds) Traffic Data Collection and its Standardization. International Series in Operations Research & Management Science, vol 144. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6070-2_7
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