Do Road Planners Produce More ‘Honest Numbers’ than Rail Planners? An Analysis of Accuracy in Road‐traffic Forecasts in Cities versus Peripheral Regions

Næss, Petter; Flyvbjerg, Bent; Buhl, Søren L.

doi:10.1080/01441640500532005

Cited by 34 publications

(9 citation statements)

References 15 publications

(17 reference statements)

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“…Data corresponding to weekday and weekend traffic (7:00 AM -8:00 AM, 12:00 PM -1:00 PM, 5:00 PM -6:00 PM, & 8:00 PM -9:00 PM) were segregated using Microsoft SQL (Microsoft 2017) and RStudio package (RStudio Team, 2020).…”

Section: Travel Time Data Processing Using Microsoft Sqlmentioning

confidence: 99%

Systemic evaluation of spatio-temporal variations in travel time reliability due to a toll road over time

Mathew

Pulugurtha

2020

Urban, Planning and Transport Research

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Section: Travel Time Data Processing Using Microsoft Sqlmentioning

confidence: 99%

Systemic evaluation of spatio-temporal variations in travel time reliability due to a toll road over time

Mathew

Pulugurtha

2020

Urban, Planning and Transport Research

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“…The economic explanation is rooted in terms of economic 'self-interest' or in terms of public interest resulting in deliberate underestimation of investment cost. Political explanations assume strategic misrepresentation when forecasting the outcomes of projects as the main reason for cost overruns also denoted as pessimism bias (Naess et al 2006) and finally, the psychological explanations are rooted in planning fallacy and optimism bias (Flyvbjerg 2007). Osland, Strand (2010) makes a thorough discussion of estimation uncertainty and related factors of influence where an argument against strategic misrepresentation is put forward.…”

Section: Reference Class Forecastingmentioning

confidence: 99%

Transport Project Evaluation: Feasibility Risk Assessment and Scenario Forecasting

Salling

Leleur

2015

Transport

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This paper presents a new approach to transport project assessment in terms of feasibility risk assessment and reference class forecasting. Conventionally, transport project assessment is based upon a Cost-Benefit Analysis (CBA) where evaluation criteria such as Benefit Cost Ratios (BCR) are obtained. Recent research has however proved that substantial inaccuracies are present when obtaining the monetary input to the CBA, particularly as concerns the construction costs and demand forecasts. This paper proposes a new approach in order to address these inaccuracies in a so-called Reference Scenario Forecasting (RSF) frame. The RSF is anchored in the cost-benefit analysis; thus, it provides decision-makers with a quantitative mean of assessing the transport infrastructure project. First, the RSF method introduces uncertainties within the CBA by applying Optimism Bias uplifts on the preliminary construction cost estimates. Hereafter, a quantitative risk analysis is provided making use of Monte Carlo simulation. This approach facilitates random input parameters based upon reference class forecasting, hence, a parameter data fit has been performed in order to obtain validated probability distribution functions. The latter have been placed and ultimately simulated on the inaccuracies of determining demand forecasts, i.e. leading to travel time savings and ticket revenues of the project. Finally, RSF makes use of scenario forecasting where trend scenarios such as economic growth and level of cross-border integration are investigated. The latter is highly relevant as RSF is demonstrated by a case example concerning the fixed link between Elsinore in Denmark and Helsingborg in Sweden.

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“…Flyvbjerg et al (2003a; see also Flyvbjerg, 2007b, 2009; Flyvbjerg et al, 2002, 2003b, 2004, 2005; Næss et al, 2006) compared 258 large infrastructure projects in 20 different countries, and found a number of patterns. For instance, actual costs are on average 45 percent higher than estimates, cost overruns are a global phenomenon, and cost performance has not improved over the past 70 years.…”

Section: Generalization Versus Contextualizationmentioning

confidence: 99%

Understanding and researching complexity with Qualitative Comparative Analysis: Evaluating transportation infrastructure projects

Verweij

Gerrits

2013

Evaluation

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This article proposes a complexity-informed framework for evaluating transportation infrastructure projects. The article does this through four steps. First, the properties of infrastructure development projects are discussed. This leads to the conclusion that the specific locality or contextualization of a given project is important for explaining the outcome. Hence, there is a need for an ontology and epistemology that addresses the importance of this contextualization. The second step concerns the development of the prerequisites for a methodological framework that follows from this epistemology and ontology. The third step is the assessment of common infrastructure evaluation methods against these prerequisites. This leads to the conclusion that a comparative case-based approach is the most suitable way to study the relationship between context and outcomes in projects. A framework based on Qualitative Comparative Analysis (QCA) is presented in the fourth step. The article concludes with a discussion of the further development of QCA.

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Do Road Planners Produce More ‘Honest Numbers’ than Rail Planners? An Analysis of Accuracy in Road‐traffic Forecasts in Cities versus Peripheral Regions

Cited by 34 publications

References 15 publications

Systemic evaluation of spatio-temporal variations in travel time reliability due to a toll road over time

Systemic evaluation of spatio-temporal variations in travel time reliability due to a toll road over time

Transport Project Evaluation: Feasibility Risk Assessment and Scenario Forecasting

Understanding and researching complexity with Qualitative Comparative Analysis: Evaluating transportation infrastructure projects

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