Analytical Error Propagation in Four-Step Transportation Demand Models

Abstract

Transportation demand models currently lack a rigorous and analytic treatment to quantify the error propagation from different sources through the models. The error of traffic forecasts is attributed to two main sources: the model specification error and the input variable measurement error. Since Four-Step Transportation Demand Model (FSTDM) is commonly used in practice but its error is not well-studied, the first part of the current study illustrates how the errors of the input variables as well as of the model specification are propagated analytically step by step and how these errors interact to result in inaccurate traffic forecasts.The proposed approach is able to quantify separately and collectively the share of different sources of error in the traffic forecast error. The proposed procedure is an efficient method that is less time consuming than existing simulation-based methods. This enables the proposed procedure to analyse the sensitivity of the traffic forecast to the input measurement error and the quality of modelling in large scale networks. Moreover, comparing the output errors using the proposed approach with the acceptable ranges of error specified in transportation guidelines, decision makers will have a clear opportunity to realise the credibility of a point traffic forecast and its associated variance.The proposed approach derives the variance from calibrated models in each of the four steps, to obtain the variance of the output based on the variance of inputs. The resulting variance formula provides an analytical expression to estimate the forecast errors from the input errors. In addition, the model specification error of each step of the FSTDM is added to the propagated input measurement errors. The proposed approach is applied to the city of Brisbane as a case study spanning the four-step models for eight different trip purposes.As an example, a measurement error of 10 percent for the input variables of the Brisbane

Determination of the Peak Hour Ridership of Metro Stations in Xi’an, China Using Geographically-Weighted Regression

“…Researchers then began to consider the total control and distribution of passenger flow in the rail transit network [3]. With the construction and operation of urban rail transit, some scholars began to analyze the shortcomings of the traditional four-step model [14,15] and carry out improvement measures [16].…”

“…Researchers then began to consider the total control and distribution of passenger flow in the rail transit network [3]. With the construction and operation of urban rail transit, some scholars began to analyze the shortcomings of the traditional four-step model [14,15] and carry out improvement measures [16].The four-step method is one of the best tools for predicting travel volume [17]. However, it is more suitable for large-scale traffic predictions rather than station-level forecasting.…”