An Approach to Incorporate Uncertainty and Risk in Transportation Investment Decision Making: Detroit River International Crossing Case Study
Bibliographic record
Abstract
Large scale transportation projects represent major investments devoted to the construction, operation, and maintenance of facilities over an extended period. Typically, these investments are irreversible in nature and require long-term commitment by the public at large relative to utilization, maintenance, and operation. Traditional economic analysis techniques used to evaluate the financial feasibility of such projects are based upon the assumption of deterministic future cash flows that are not subject to any uncertainty and risk. In reality, many of these projects are associated with significant uncertainties and risks stemming from lack of knowledge about future cost and benefit streams. There is a lack of comprehensive literature in addressing uncertainty and risk in transportation investment decision making. The authors present a framework for addressing uncertainty and risk for large scale transportation investments involving joint participation by the public and private entity. Demand, fare/toll, and demand responsive costs are considered in the uncertainty analysis. A bi-level programming is proposed, where the upper level constitutes the preference of the policy maker, and the lower level determines the user’s response to the policy. The uncertainty analysis provides economic feasibility of the transportation project. A set of relaxation policies is proposed to form various Ownership, Tenure, and Governance (OTG) strategies reflecting the nature and level of participation by the public and private entity. The uncertainty analysis output serves as input to the risk analysis. Monte Carlo Simulation is used to address risks for feasible policy options selected from uncertainty analysis. The concept of Value at Risk (VaR) is used to quantify risk. A methodology is proposed to integrate uncertainty and risk. The framework is tested on the proposed multi-billion dollar international river crossing entitled as the Detroit River International Crossing (DRIC) connecting the city of Detroit in the US and the city of Windsor in Canada. The combination of both uncertainty and risk reveals insights to the probable outcomes for a transportation infrastructure investment. This methodology can be used as a tool for transportation infrastructure investment decision making process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".