The Influence of the Impedance Function on Uncertainty Propagation Through a Four-Step Model

Transport demand models imply a wide range of uncertainties originated from the variability of natural processes, imperfection of knowledge about the phenomenon to be modeled and exogenous factors influencing it. The greater the uncertainty on the demand estimates is the higher will be the requirements on the needs of robustness and flexible design for the final project. The variety of different types and sources of uncertainty helps its propagation through the travel modeling steps, and contributes to the overall output uncertainty. There is often a possibility for a decision-maker to reduce the input and calibration uncertainty by gaining additional information (e.g. conducting a survey, collecting expert judgments, etc.). However, every piece of supplementary data has its cost and so the decision-maker should thoroughly scope for which information any improvement efforts need to be concentrated. Two major rules on this choice were suggested in the literature: (1) focusing on the variables with a larger uncertainty, and (2) focusing on the variables influencing the dependent variables most. The former means that the variables with larger error first should be identified and the latter advocates model parsimony. This work analyzes the impact of impedance function inputs and specifications on the uncertainty of a four-step model and identifies the relevant major error contributors. For that, the authors consider different specifications of the impedance function (including its choice and parameters distribution) and uncertainty of its attribute (which are the generalized costs). To illustrate this analyses, the authors use as case study data from the city of Aveiro which is a medium sized city in Portugal with a multimodal transportation system available.