The propagation of uncertainty through travel demand models: An exploratory analysis

Yong Zhao () and Kara Maria Kockelman ()
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Yong Zhao: The University of Texas at Austin, 6.9 E. Cockrell Jr. Hall, Austin, TX 78712-1076, USA
Kara Maria Kockelman: The University of Texas at Austin, 6.9 E. Cockrell Jr. Hall, Austin, TX 78712-1076, USA

The Annals of Regional Science, 2002, vol. 36, issue 1, 145-163

Abstract: The future operations of transportation systems involve a lot of uncertainty - in both inputs and model parameters. This work investigates the stability of contemporary transport demand model outputs by quantifying the variability in model inputs, such as zonal socioeconomic data and trip generation rates, and simulating the propagation of their variation through a series of common demand models over a 25-zone network. The results suggest that uncertainty is likely to compound itself - rather than attenuate - over a series of models. Mispredictions at early stages (e.g., trip generation) in multi-stage models appear to amplify across later stages. While this effect may be counteracted by equilibrium assignment of traffic flows across a network, predicted traffic flows are highly and positively correlated.

JEL-codes: C15 D80 R41 (search for similar items in EconPapers)
Date: 2002-04-09
Note: Received: March 2001/Accepted: August 2001
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Citations: View citations in EconPapers (34)

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