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

Zhao, Yong; Kockelman, Kara M.

doi:10.1007/s001680200072

Cited by 150 publications

(102 citation statements)

References 9 publications

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“…e parameter space in such modeling systems is vast, frustrating attempts to fully understand the highly complex interactions of parameters that appear simple when viewed in isolation. e uncertainties arising from such interactions are compounded rather than attenuated over time (Naess et al 2015;Zhao and Kockelman 2002). e challenges posed by parameter storm in SWIM are still being tackled in practice.…”

Section: Design Issuesmentioning

confidence: 99%

Oregon's transportation and land use model integration program: A retrospective

Donnelly

2018

JTLU

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An ambitious and innovative integrated land use-transport modeling system has been developed in Oregon over the past two decades. is work, completed under the Transportation and Land Use Model Integration Program (TLUMIP), included the development of two generations of models and the data required to build and use them and spawned the development of two others that have continued independently. An outreach program and collaborative development of freight data and forecasts were also included, as well as system testing and applications. A brief description of the motivation behind TLUMIP and the resulting modeling systems are presented. Perhaps more interesting is the story behind the models, describing several major model design, institutional, and methodology issues that were overcome. Using an integrated model in practice also entailed addressing a wider range of analytical requirements and stakeholder expectations about usability, accuracy, and extensibility than typically considered in academic pursuits. e key lessons learned through development and use of the models are discussed, with the hope that they will inform the development of similar large-scale modeling systems.

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Section: Design Issuesmentioning

confidence: 99%

Oregon's transportation and land use model integration program: A retrospective

Donnelly

2018

JTLU

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“…The assignment mechanism itself can change the amount of uncertainty (e.g. reduce it, as in Zhao and Kockelman, 2001), because larger transport demand leads to more congestion and this increases travel times, which reduces demand for specific routes, periods and modes, etc. This also implies that we had to run the full assignment procedure (with the normal number of iterations 6 ).…”

Section: Treatment Of Model Uncertaintymentioning

confidence: 99%

Uncertainty in traffic forecasts: literature review and new results for The Netherlands

Jong

Daly

Pieters³

et al. 2006

Transportation

101

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PrefaceThe objectives of the project 'Uncertainty in traffic forecasts' that RAND Europe carried out for the Transport Research Centre of the Dutch Ministry of Transport, Public Works and Water Management were:To develop a methodology to estimate the amount of uncertainty in forecasting for new infrastructure (especially roads).To implement and test this methodology in two case-studies (using the Dutch National Model system LMS and the New Regional Models NRM respectively).This report presents the outcomes of all phases of this project:Literature review for public projects;Literature review for public-private partnership (PPP) projects; Development of a method to quantify the uncertainty in traffic forecasts for the LMS and NRM;Outcomes from a large number (100) of model runs with the LMS to derive uncertainty margins around the mean traffic forecasts;Outcomes from a large number (100) of model runs with the NRM for the Dutch province of Noord-Brabant to derive uncertainty margins around the mean traffic forecasts.This report was written for modellers with an interest in the uncertainty margins around the model forecasts and methods to quantify the uncertainty margins.RAND Europe is an independent not-for-profit policy research organisation that serves the public interest by improving policymaking and informing public debate. This report has been peer-reviewed in accordance with RAND's quality assurance standards (see

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“…Since the variance of model specification error measured by the proposed method uses the information provided in the validation process, a realistic figure of the predictive power of the model is represented. This improves the common approach in the literature Johnston, 2005, 2006;Zhao and Kockelman, 2002;Yang et al, 2013) in which a predetermined standard deviation of error (e.g. 10% of the mean value) is attributed to the calibrated parameters and may lead to an incorrect representation of the total model error.…”

Section: -3-2-specification Errormentioning

confidence: 60%

“…Without explicit statistical recognition of error in transportation forecasts, transportation planning takes an unnecessary risk (Zhao and Kockelman, 2002). de Jong et al (2007) expressed that in order to make an informed decision on infrastructure projects, it is essential to estimate not only the most plausible outcome, but also the possible range of outcome variation.…”

Section: -2-importance Of Error Analysis In the Traffic Forecastsmentioning

confidence: 99%

“…In the literature, the significant sources of error are classified into two error groups: measurement and specification errors (Zhao and Kockelman, 2002;de Jong et al, 2007;Fagnant and Kockelman, 2012;Yang et al, 2013;Manzo, Nielsen, and Prato, 2015). In the present study, the effect of the measurement error as well as the model specification error is taken into consideration.…”

Section: -3-source Of Traffic Forecast Errorsmentioning

confidence: 99%

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Analytical Error Propagation in Four-Step Transportation Demand Models

Rezaeestakhruie¹

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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

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

Cited by 150 publications

References 9 publications

Oregon's transportation and land use model integration program: A retrospective

Oregon's transportation and land use model integration program: A retrospective

Uncertainty in traffic forecasts: literature review and new results for The Netherlands

Analytical Error Propagation in Four-Step Transportation Demand Models

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