Assessing the Value of TMCs and Methods to Evaluate the Long Term Effects of ITS: Measuring Congestion, Productivity and Benefi t Flow from Implementation
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,029 | 0,076 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».