Assessing the Value of TMCs and Methods to Evaluate the Long Term Effects of ITS: Measuring Congestion, Productivity and Benefi t Flow from Implementation
Bibliographic record
Abstract
The study carries out an evaluation of TMCs (traffic management centers) using three methodologies; case studies, performance based regressions and time series analysis. The study is an extension of previous work that assessed the contribution of different types of intelligent transportation investments and initiatives. However, this research sought to distinguish the separate contributions of the ITS investments from the synergies of integration under a traffic management center. Secondly, the research investigated the time dimension of benefits where we investigated if there was an 'S' curve effect in which a change in the network due to an ITS investment or the introduction of a TMC lead to benefits distributed over time. The distribution was important to evaluating ITS investments. If one measured the impact of the investment too soon, in the disequilibrium period, it would underestimate the true contribution of the investment or change in process or management strategy. We found the institutions that affect TMC operations with their designation of responsibilities, who can do what, when and where, requires change before the TMC can be an effective addition to the management of the transportation network. TMCs represent an integration of hardware and people and that process and management were most important in ensuring the TMCs had added value. Our performance related regressions used levels and changes in congestion (measured by a congestion index) and changes in VMT for autos and trucks. We found that for auto VMT ramp meters were more important than CMSs in improving the system. This was, more VMT can be obtained from the system, holding congestion constant, with ramp meters. We found that TMCs had no statistical impact on auto VMT. In the case of truck VMT, the results were just the reverse; CMSs appeared to be more important than ramp meters in improving system efficiency when efficiency was measured by extracting more truck VMT from the system, holding congestion constant. As with the auto results, TMCs were not significant in the analysis. The regression using the congestion index found ramp meters appear to be 4 times as effective as CMSs. TMCs as before were not statistically significant in affecting congestion. Overall the model did not have a lot of explanatory power in sorting out the differences in congestion among counties or what the underlying contribution is of ITS relative to investments. But it is evident that among conventional congestion relief measures maintaining infrastructure (roads) is more effective than expanding capacity. It also appears that ramp meters and CMSs, indicators of improved network management are more effective in reducing congestion than are expanding the network.
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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 | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".