Modelling Risk in Highway Infrastructure Investments: Decision-Theoretic, Bayesian, and Factor Analysis Approaches
Bibliographic record
Abstract
Investments in highway infrastructure are expected to achieve a satisfactory rating for avoiding cost overruns and in fulfilling their role in enhancing the sustainability of cities and regions.Despite challenges, highway infrastructure investment assessments received insufficient research attention in modelling lifecycle cost risk and the associated sustainability effectiveness.The increasing acceptance of the sustainability rating tools for evaluating highway projects implies going beyond former attention to location and design of highway infrastructure guided mainly by functional considerations.Under present high demands from the transportation planning environment, there is emphasis on characterizing risk in the first cost as well as lifecycle costs.Likewise, there is an emphasis on inclusion of sustainability factors, including effective use of resources, in evaluating project alternatives.The use of infrastructure rating tools such as ENVISION has the potential to improve the effectiveness of investment in highway projects in terms of meeting sustainability criteria, including formal recognition of the importance of lifecycle analysis.To support the application of rating tools, research is needed in modelling risk in lifecycle cost estimates, including the identification and quantification of cost overrun factors.Besides, there is a need for a methodology for joint treatment of multi-attribute criteria that encompass cost and other factors of sustainability.To go beyond the current state of knowledge, research was carried out on: The probability models of cost overruns. Treating risk and uncertainty in lifecycle analyses, using decision-theoretic, utilitytheoretic, and Bayesian methods for evaluation of investment alternatives. Factor Analysis of variables that characterize the causes of cost overruns and logistics regression models based on factor analysis results.Data were obtained and analyzed on actual projects that may have experienced cost overruns.Also, a questionnaire study was implemented to obtain data from transportation jurisdictions in Canada, the USA, Middle East, and Australia.Following the study of cost overrun probability models, decision-theoretic and utility-theoretic methods were developed and illustrated in the evaluation of investment alternatives while formally treating lifecycle costs and other factors of sustainability.Finally, factor analysis and associated logistic regression models were implemented for characterizing the effect of factors that cause cost overruns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".