Intelligent Transportation Systems Investment Evaluation: To Purchase or Lease
Bibliographic record
Abstract
The use of intelligent transportation systems (ITS) is increasing as further attempts are made to alleviate traffic problems by means other than expanding the physical capacity and size of roadways. ITS field trials have demonstrated various benefits associated with individual applications and integrated systems. However, few agencies have considered the option of leasing the ITS applications and therefore no guidance or framework exists to evaluate the purchase/lease options. This paper presents and tests a methodology to analyze ITS investment strategies. A number of economic evaluation frameworks and criteria were considered. Of the considered criteria, the net present value (NPV) criterion was selected and, in particular, life cycle cost analysis (LCCA) was determined to be the most relevant evaluation method. The LCCA method was tested for a case study and was shown to provide relevant and meaningful results. Overall, it is believed that the LCCA methodology is the most valid for comparing investment alternatives. Finally, in order to fully assess which investment option is preferred, it was determined that the analyst must know or be able to estimate what the anticipated usage of the ITS application(s) will be in the future. This paper was written and submitted as credit for an asset management graduate course at the University of Calgary during the 2005/2006 academic year. In the Fall 2005 term, an asset management reading course introduced and discussed the concepts of asset management. A pavement management course in the Winter 2006 term introduced more specific pavement management concepts and economic evaluation tools, which can be extrapolated to other asset classes. This paper represents the combination of concepts and information provided in the asset management reading course and introduced in the pavement management course.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".