In regions with scarce data, such as Norway, predicting cost performance in large-scale road (LSR) projects presents a unique challenge due to the high risk of cost overruns and their significant economic implications. This study aims to develop a data-driven framework for predicting cost performance in LSR projects by combining synthetic data generation and machine learning models. The approach employs synthetic data generation via Conditional Generative Adversarial Networks (CTGAN) to enhance the data pool and improve predictive accuracy. By integrating 173 synthetically generated samples with 52 actual project samples, a robust dataset of 225 road projects was created. Three machine learning classifiers (i.e., XGBoost, MLP, and SVM) were applied to this enriched dataset. The models achieved an average accuracy of 0.76 and an F1 score of 0.74 when tested against real-world data, demonstrating substantial alignment with actual project outcomes. Further validation with 5-fold cross-validation on the combined datasets confirmed the consistency of these results, with similar accuracy and F1 scores. This research highlights the effectiveness of synthetic data in overcoming the limitations of small datasets and underscores its potential to substantially improve decision-making in highway engineering by providing more accurate, data-driven insights for project planning, design, and management.

Toll road initiatives play a pivotal role in financing infrastructure projects, particularly when traditional government funding sources are constrained. These initiatives typically involve imposing tolls for a predefined period, after which tolls are removed to reduce the long-term financial burden on road users. Despite their widespread application, the transportation literature offers limited systematic evidence on how governments meet their stated tolling objectives, the potential biases in their forecasts, and the determinants of deviations from planned outcomes. This paper addresses these gaps through an ex-post evaluation of Norwegian toll projects implemented and completed between 1984 and 2020. Covering approximately 90 % of all relevant cases during this period, the study uses robust econometric techniques to assess the presence of systematic biases in government forecasts and identify key factors influencing the duration of tolling. The results reveal several important findings. First, the government's primary objective-removal of tolls within the planned time frame-is generally achieved, with tolls being removed, on average, 1.45 years earlier than estimated. Second, traffic volumes tend to exceed forecasts, while cost estimates are typically optimistic, resulting in modest overruns. Third, although mean percentage errors indicate moderate under- and overestimations-well within expected margins and significantly smaller than the forecast deviations commonly reported internationally-statistical tests confirm that the government's forecasts are systematically biased. This suggests that the deviations are not due to random variation alone but reflect consistent patterns in estimation. Finally, variations in toll durations are significantly influenced by financing conditions, particularly interest rates, as well as project characteristics such as road type, centrality, and cost structure. These insights demonstrate that Norway's tolling system has largely delivered on its promises and highlight the importance of refining forecasting methodologies for future policy planning. This study underscores the value of ex-post evaluations in understanding infrastructure performance and calls for more empirical research on the fulfillment of public sector commitments in transport financing.

Governments rely heavily on ex-ante cost-benefit analysis to justify major transport investments. Yet in many jurisdictions there is no systematic verification of whether promised costs, traffic volumes and benefit streams materialize once projects are delivered. This policy note argues that routine, institutionalized and published expost cost-benefit analysis is essential for fiscal accountability, methodological calibration and sustained public trust in infrastructure governance. Drawing on evidence from Norway, the United Kingdom, Australia, New Zealand and European Union practice, the paper synthesizes findings on discrepancies between ex-ante forecasts and realized outcomes, compares institutional approaches to post-evaluation, and demonstrates how ex-post evidence improves appraisal guidance, forecasting realism and investment discipline. It concludes with a sequenced reform roadmap for embedding ex-post cost-benefit analysis within national transport governance systems.

Cost overruns in road transport projects have long been portrayed as inevitable. They were typically measured as the percentage difference between actual and estimated costs at the time of decision-to-build, expressed relative to the estimated cost. Influential studies, notably Flyvbjerg et al. (2002), popularized the view that projects almost always overrun and by large margins. Yet recent evidence tells a different story. Norwegian studies show that overruns have been low and stable over time and declined further after governance reforms such as the Quality Assurance scheme. Much of the escalation occurs during front-end planning, while estimates at decision-to-build are generally accurate. Similar patterns appear elsewhere. New measurement approaches reveal that earlier studies in England and Sweden overstated overruns. In Sweden, estimates stabilize once construction begins. Swiss highway projects show median overruns of around 5%, and Australian and New Zealand studies also find modest medians once outliers are accounted for. Other research highlights the role of reforms in planning, assurance, procurement, and accountability in improving delivery. This essay argues that overruns are not destiny. With the right governance and institutional reforms, governments can act to improve cost performance in road projects. Early studies relied on measurement that overstated central tendencies. Whereas we reflect on the wider and supportive international literature, we focus more on evidence from the Norwegian context.

This paper addresses three key gaps in transportation research on cost and time overruns: (1) the overuse of Mean Percentage Error (MPE), despite the Median Percentage Error (MdnPE) being more appropriate for skewed data; (2) limited evaluation of government interventions aimed at reducing overruns; and (3) the failure to account for the interdependence between cost and time overruns, leading to biased estimates. Using a dataset of 2,228 Norwegian road projects (1993-2016), we assess overruns using both MdnPE and MPE, evaluate the effects of major organizational reforms, and apply a Three-Stage Least Squares (3SLS) estimation to correct for endogeneity between cost and time overruns. Unlike some studies, we rely solely on statistically observable factors and avoid subjective explanations such as the planning fallacy or strategic misrepresentation. Results show a median cost overrun of 4% (mean: 12%) and a median time overrun of 0% (mean: 18%), confirming a skewed data distribution. This demonstrates how MPE can exaggerate typical performance, while MdnPE offers a more representative measure of central tendency. Still, the choice between MPE and MdnPE should depend on the analytical objective-whether the goal is to highlight extreme cases or typical project performance. We find that government reforms, particularly the introduction of full procurement competition and a Quality Assurance (QA) regime, significantly reduced overruns. However, results for time overruns vary by method: the Kruskal-Wallis test shows lower median time overruns during the QA and competition periods, while the 3SLS model-controlling for other project factors-reveals that the transition to full competition was associated with increased time overruns. This suggests that improvements in medians may reflect changing project composition rather than reform effects alone. This study underscores the need to consider how overruns are evaluated and encourages the use of empirical evidence over speculative explanations like the planning fallacy.

A critical step in the road investment decision-making process is to conduct cost-benefit analyses (CBAs) to assess the net social benefit of project alternatives. CBA results are often presented to decision-makers as point estimates. However, such results rely on numerous forecasts, parameters, and unit values, which introduce uncertainty. This paper adds to the literature on transport CBAs by evaluating how their inherent uncertainty can be considered by analysts and presented to decision-makers. We develop a Monte Carlo simulation procedure that uses variations in the most critical factors related to CBAs. We apply this procedure to a set of projects to demonstrate how it could inform decision-makers in terms of the uncertainties involved in CBAs. We show that if analysts present ranges or confidence intervals, decision-makers may make other choices that differ from those based on point estimates.

Med data fra over 1000 norske og svenske vegprosjekter har vi jaktet på indikatorer som kan hjelpe myndighetene å skille lønnsomme og ulønnsomme prosjekter i en tidlig fase. Ambisjonen er å lage et indikatorsett som kan si noe om hvorvidt et tenkt vegprosjekt har håp om å kunne bli lønnsomt – lenge før det er mulig å gjennomføre en fullstendig nytte-kostnadsanalyse.

Resultater fra nytte-kostnadsanalyser av vegprosjekter er langt mer usikre enn det som presenteres for beslutningstakere. Denne usikkerheten bør synliggjøres bedre.

We investigate factors that determine road users' experiences with congestion based on a questionnaire survey conducted in the Oslo area of Norway. The rationale is to add more knowledge on factors that determine users' experience with congestion. Furthermore, we use a succinct econometric framework to assess the data. We find that the following factors influence road users' experiences with congestion: (1) whether they experienced congestion on their reference trip; (2) how often road users undertake trips during congestion; (3) the extent to which road users had potential alternative modes of transport other than car use; (4) education; (5) whether respondents had time commitments at their destinations; (6) travel time used during their journey; (7) how often they experience congestion as a problem during their journey; (8) when participants begin to experience discomfort with congestion; and (9) age. The direction of these factors' impact on experience is explained in the results section. For example, those who did not experience congestion on their previous trip are 26 percentage points more likely to report a negative experience with congestion than those who did. The results provide new insight into the factors that determine road users' experience with congestion. Finally, we warn that one should not be indifferent regarding the logit models used, as the results can be quite different.

This study explores factors that characterise road projects with high value for money that may be available before a full CBA (cost-benefit analysis) has been conducted. Our rationale for the study is twofold. First, identifying such factors can aid in selecting potentially economically viable project ideas at an early planning stage (and correspondingly, sifting out poor ones), avoiding the further development of projects and thereby also avoiding political lock-in to projects that will entail a negative social benefit. We use data from cost-benefit analyses conducted in Norway and Sweden. The regression analysis uses geographical and other project-specific characteristics as independent variables. Our data confirms the assumption that travel time savings represent the largest share of project benefits. We also demonstrate that Swedish projects have a higher value for money than their Norwegian counterparts.The following characteristics are most important in determining road projects’ value for money: traffic levels, population density and local income level. We also find that smaller projects have a higher value for money than larger ones, contrary to common assumptions. Furthermore, local co-financing affects value for money negatively due to the deadweight loss induced by tolls being greater than the marginal cost of public funds, or because co-funding by the region influences project selection. Our findings contribute to the academic literature and help practitioners identify good project ideas at an early stage.

This paper assesses how transforming a cordon toll ring with flat toll rates to a congestion charging scheme works in achieving its intended objectives. The contribution to the literature on transportation is that few studies have examined how such transformations fulfill their main policy objectives. We use the City of Bergen in Norway as a case study to infer the extent to which policy objectives were achieved two years later. In early 2016, the city council transformed its flat-rate cordon toll ring into a congestion charging scheme. It entailed an increase in toll rates during rush hour and a decrease during non-rush hour periods. The primary aim was to reduce traffic levels entering the city center during rush hour and improve the inner city's air quality. A secondary objective was to maintain income at the current level to complete the preplanned and already sanctioned investment projects. We use traffic counts, NO2 and PM10 emission measurements, and annual toll income to evaluate how the congestion scheme performed two years after implementation. We find that (i) traffic levels during rush hour and non-rush hour periods were reduced by 12% and 2%, respectively, giving a grand reduction of 6%; (ii) the emissions in NO2 and PM10 were reduced by 6.3% and 11%, respectively; (iii) the toll income dropped by 13%, meaning that preplanned investments would not be implemented on time; and (iv) the use of other modes of transportation increased. Overall, congestion charging worked according to transportation economists' longstanding suggestions.

The well-known Oslo cordon toll ring was transformed into a congestion charging scheme in 2017. The transformation implied higher toll rates during rush hours and lower rates during nonrush hours, and battery-driven electric vehicles (BEVs) were exempted from paying tolls as a means of enhancing the uptake of BEVs. This paper studies road users' attitudes towards the transformation that took place. The rationale is that studies examining road users' attitudes towards such transformations are lacking in the transportation literature. We use the results of a survey conducted a month after the transformation, and a question was added regarding users' attitudes towards the transformation. The dataset consists of 2005 responses. We use both descriptive statistics and logitbased regression analyses to assess the data. The results reveal that the average road user has a negative attitude towards the transformation, most likely because the transformation increased the travel cost for the average user. Our results add value to the literature and for researchers and policymakers who may want to consider similar transformations, and groups of users who need to be convinced of the usefulness of congestion charging are identified.

The traditional view of transportation infrastructure projects' success evaluation has been focused on efficient time and cost performance (i.e., operational success), but there is an increased interest in considering a broader perspective that evaluates whether the project has performed as promised with respect to project goals (i.e., tactical success). This study presents a two-level success evaluation of a Norwegian highway that examines project efficiency and effectiveness, i.e., operational and tactical success. The evaluation suggests that while the project experienced a large cost increase during the construction period, it performed operationally efficiently with 2.48% cost underrun. The project was also tactically successful in effectively achieving the predetermined goals of reducing traffic accidents, traffic congestion, and travel time through the project area. Considering both aspects of success provide a more holistic understanding of the project's benefits, and this study contributes to further developing the overall understanding of project success evaluation through the case study.

This paper examines participant attitudes towards battery electric vehicle (BEV) incentives. Our case study was conducted in the greater Oslo area. Oslo has ranked as the world capital of BEV usage since 2014. The Norwegian government currently leads the comprehensive use of BEV incentives to decarbonize road transport. The data set is from a questionnaire survey conducted annually between 2014 and 2020. A total of 6363 individuals divided equally into annual random samples were asked to express their attitudes towards the different BEV incentives in place in each year. Participants were aged 18 or older and were living in the larger Oslo area. Professional data collection companies used computer-assisted telephone interviews to conduct the survey. Generalized structural equation modelling (GSEM) was used to analyse the data. The sample was 49% women and 51% men, with a mean age of 51 years, ranging from 18 to 99 years old. People in greater Oslo increasingly disagree each year with beneficial BEV incentives such as toll exemptions, access to bus lanes without passengers and free public parking. However, internal combustion engine vehicle (ICEV) users are more likely to disagree than BEV users. The results provide new knowledge about attitudes towards BEV incentives from a longitudinal perspective.

Determining the factors leading to cost inaccuracy in infrastructure projects relates to sustainability by improving the cost performance of the projects (economic sustainability) and reducing the waste of available resources (environmental sustainability). This study investigates the effects of various factors affecting the cost performance of large-scale road projects in Norway in both the planning and construction phases. To this aim, a quantitative approach using a questionnaire survey was employed to understand the attitude of practitioners towards various factors causing cost increases. An advanced multivariate statistical approach of Partial Least Square Structural Equation Modeling (PLS-SEM) and Relative Importance Index (RII) was utilized to analyze the questionnaire responses. The results of the RII analysis show that local wishes, defective estimations, and long processing times had the most impact on the cost increase during the planning phase. At the same time, scope changes, market conditions, and unforeseen ground conditions were the most influential parameters in the construction phase. Moreover, the results obtained from PLS-SEM reveal that external related factors had the most influence among the other grouped factors (i.e., pre-construction, project management and contractual relationship, contractor's site management, and external) on cost overrun during the construction phase. Increasing the knowledge of these factors will allow for developing relevant project management approaches targeted at improving economic and environmental sustainability within both the planning and construction phases.

Car ferry services constitute an important part of the transportation network in several parts of the world, especially in areas with limited alternative modes of transport. A central problem facing decision makers is the socially optimal capacity of ferry services. However, the literature has not examined all the decision variables the are relevant to decision makers in a simultaneous framework, only partially. We add to the literature by treating all the relevant decisions variables in a simultaneous framework, which enables a more complete representation of optimal capacity, than partial frameworks. Our proposed methodology also includes the cost of not being able to board the first arriving departure, which is an essential cost in the case of car ferries with too low capacity. We apply the methodology to a case study of three major ferry crossings in Norway. Results indicate that a too large capacity is provided. Thus, local policy makers should consider revising the current service levels. Other policy makers may enact better decisions based on the findings we provide. Sensitivity test suggests that the method used to estimate the number of users not being able to board due to capacity concerns may be improved. This, however, does not alter our main conclusion.

In this paper, we address the robustness of cost–benefit analysis (CBA) from the perspective of its usefulness for decision-making. After summarizing how CBAs are preformed, we provide an example of how uncertainties in its parameters may jeopardize its usefulness as a decision-making tool. We also elaborate on how the robustness of CBAs is contextual, depending on what they are expected to serve, together with the fact that CBAs are either ex-ante or ex-post. More generally, this paper provides an overview of the inherent uncertainties in CBAs. Major sources of uncertainties in CBAs are addressed, and we point out that the robustness of CBAs can be improved by performing ex-post evaluations. Additionally, the issue of the robustness of CBAs in a future perspective is addressed.