Development of a speed limit model for two-lane rural highways
Notice bibliographique
Résumé
Two-lane rural highways usually have to face the traffic volumes generated by small villages that developed along their path. These roads were normally capable to ensure uninterrupted flow that provides a high level of mobility with high speed limits. The increase in lateral road occupation and some shortcomings in land planning along these roads have led to a rethink in the methodologies and criteria used to define the speed limits namely in stretches that have systematic variations in the surrounding environment. In fact, up until the mid 1980's the approach used for rural and interurban road design tended to put the emphasis on standardized geometric solutions with little consideration being given to its integration with the surrounding environment. By that time, however, in many European countries but also in the United States, Canada and Australia, the emphasis started to shift towards traffic safety and, progressively, even more in environmental and quality of life related issues, particularly during the crossing of small urban areas or towns. At present there is a widely accepted understanding that only an integrated approach is capable of taking into consideration in a coherent way the interests and needs of all the stakeholders. These are, on the one hand, the vehicles' drivers and passengers who mainly want a rapid and safe, route, and on the other hand, they are the needs and aspirations of the other road users and residents of the surrounding areas, and, finally, those of the society as a all. In the case of two lane roads with regional or national importance where the road tends to cross over many different environments, from the pure rural ones with very little marginal access and interactions, passing through suburban ones where there is a non-negligible level of lateral occupancy by human activities, to pure urban ones. So there is still some work to be done in the development of strategies and solutions which are at the same time efficient and can be applied in a standardized, widespread and systematic way. One of the basic problems related with this question is the one related to the selection of the adequate speed limits for the different surrounding environments, and functional and physical characteristics of the roads. Xlimits and USlimits (from Australia and United States respectively) are examples of the types of software that enable the estimation of speed limits according to a set of conditions related to the infrastructure, land use, operational conditions and local accident rates. These models are, however, adapted to their origin countries' main conditions and require some inputs that are not always available. The development of more detailed analytical models capable of supporting this selection process through the production of objective estimates for the speed limits, based on the quantification of simple explanatory variables representing these aspects, is of significant relevance. In the present paper a linear multiple regression model capable of objectively supporting the process of defining the adequate speed limit levels throughout the full length of a regional or national single carriageway through road is presented. The model, which was developed using real life data, enables the production of estimates for the speed limits to be applied along the route based on the quantification of a small set of variables which describe the functional and physical characteristics of the different stretches of the road and its surrounding environment. In addiction the suitability of the application of logit models and fuzzy logic techniques to the problem is evaluated in an attempt to create discrete response models. This paper presents the methodological approach to this issue as well as the different modelling options assumed, particularly those related to the criteria for the selection of the relevant explanatory variables and of their adequate degree of aggregation.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».