Evaluation of driver visual demand in complex two dimensional rural highway alignments
Notice bibliographique
Résumé
Transportation has proven to be one of the most important infrastructures in the economic development of any country. Safe and effective traffic operations support growth of the economy and help in future developments. Highway alignment design plays a crucial role in implementing safer traffic operation and management. Road accidents not only jeopardize safety, but also have a major effect on the national economy. These accidents can be divided in three classes, grouped according to their severity. Statistics in North America and Europe show that one of the major reasons for such road accidents is driver error. Wrong decisions during navigation may be the primary reason for such errors. Wrong decisions occur when a driver is unable to process the range of visual information available in a complex highway situation. Drivers need to have sufficient visual information in guiding and controlling vehicles along the correct path. Drivers scan the roadway to collect visual information. This visual information consists mainly of the traffic situation, roadway signs, and the information from the highway alignment itself. The information from the highway alignment plays a major role in decision-making during maneuvering. All drivers, therefore, need sufficient visual information for perfect navigating, and for guiding and controlling their vehicles on the road. The main focus of this research study was on evaluating visual demands on two-dimensional highway alignments with an emphasis on determining the effect of complex curves on visual demand. Complex curves are defined as combinations of simple, compound, and reverse curves in a series. Eighteen hypothetical alignments for two-lane rural highways have been developed following the standard guidelines of the Transportation Association of Canada (TAC) and American Association of State Highway Transportation Officials (AASHTO). These alignments were simulated in a low-cost driving simulator. A series of experiments was carried out using the visual occlusion method. Nine subject drivers drove in the simulator, and the output data related to visual demand information and positioning of the subject vehicle were connected. The data relating to visual demand information and lateral positioning on curves and tangents were processed using Microsoft ExceFM and analyzed using SAS, a statistical software. The turning directions, characteristics of preceding elements, and the combination of curve to curve, tangent to curve, or curve to tangent have been considered as nominal variables and analyzed as independent variables with visual demand. It has been observed that visual demand varies widely with the inverse of radius of curvature of the preceding and current elements, and the characteristics of the combination of the current and the preceding element. Visual demand also varies on identical tangents, depending on the deflection angle, inverse of radius, and turning direction of the preceding curve. The standard deviation of lateral positioning of the subject vehicle was evaluated with respect to the centre-line of the driving lane. This was supposed to have a considerable impact on visual demand evaluation, but it has been observed that this does not bear any significant relationship to visual demand. In addition to curves, tangents, as preceding elements have an immense impact on visual demand evaluation on following curves. Besides, visual demand on tangents has also been observed as highly dependent on the preceding curve and their turning directions.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».