Multicriteria Project Portfolio Selection: Case Study for Intelligent Transportation Systems
Bibliographic record
Abstract
Transportation strategies encompass a portfolio of projects in which choices must be made between competing alternatives. An appropriate portfolio of projects is essential for the success and growth of transportation agencies. The introduction and implementation of emerging technologies such as intelligent transportation systems (ITS) increase the need for more effective decision-making approaches and project selection in the coming years. Transportation projects, particularly, have a broad impact on the public and are multicriteria in nature. The projects also involve several elements of risk, such as project success, public acceptance, or public image. Traditional methods of project evaluation such as benefit–cost analysis focus mainly on the financial rewards of projects and do not sufficiently consider multicriteria and risk evaluations in an integrated framework. Development of an objective and systematic methodology that could address the multicriteria nature of the projects and also deal with their risks and rewards is necessary for both private and public agencies. This need is important particularly when new technologies are implemented, information on project impacts is insufficient, and resources are constrained. An integrated project portfolio selection model is introduced based on the well-established methodologies used for multicriteria evaluation and proven concepts used for portfolio selection in the finance discipline. The new methodology significantly facilitates decision making by integrating both the risk and the value of projects. A case study for selecting ITS projects in a public agency is demonstrated. Guidance is provided in nontechnical language for interpreting the outputs of the methodology.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".