Developing Models for Estimating Winter Road Weather and Surface Conditions–An Empirical Investigation
Notice bibliographique
Résumé
Inclement weather poses a threat to road safety and mobility for motorists in cold regions during winter. To facilitate more efficient winter maintenance decision support and reduce weather-related collisions, many transportation agencies have adopted one of the most critical highway infrastructures; namely, road weather information systems (RWIS). While RWIS are effective in collecting real-time road surface conditions (RSC) information, they are costly to install and operate. Equally important, RWIS provide point measurements that are often unrepresentative of distant surrounding areas. Acknowledging the limitations in present knowledge and methods pertaining to improving its spatial coverage, this research proposes a new systematic framework that uses one of the most advanced geostatistical interpolation techniques, namely, regression kriging (RK), to estimate continuous RSC between different pairs of existing RWIS stations.This research contains two phases: Phase I first evaluates the feasibility of applying RK to road surface temperature (RST) and road surface index (RSI) estimations. A comparison study using different spatial interpolation methods, including inverse distance weighting, global polynomial interpolation, local polynomial interpolation, and thin plate spline, is conducted to further verify the robustness of the RK method proposed herein. Phase II of the thesis extends the application of the previously developed model in Phase I to estimate RSC using stationary RWIS data only. A sensitivity analysis is also carried out to investigate the influence of RWIS stations density on model performance. Lastly, a recommendation to optimize the RWIS network is introduced by incorporating a renowned combinatorial particle swarm optimization method with the objective of minimizing the total kriging estimation errors.The case study areas are Highways 2 and 16, which are major traffic corridors between Edmonton and Calgary (approximately 300 km) and between Edmonton and Edson (approximately 150 km), respectively. The datasets used in this study are from twelve surveys on four winter nights on Highway 16 and six surveys on two winter nights on Highway 2. Weather events are classified based on the wind speed and snow on ground information to investigate the generalization potential of the models developed herein.The main findings of this thesis are summarized as follows.The findings of Phase I indicate that the kriging models developed in this thesis have a strong predictive ability in estimating road weather and surface conditions, as indicated by low average root mean square errors (RMSE) of 0.254oC and 0.046oC for RST and RSI estimations, respectively. The results also suggest that the RSC estimations can be greatly enhanced with the help of additional covariates included in the models. Furthermore, there exists a strong dependency between the variability in data sets and weather event categories, which can be further used to generalize the findings of this study. The comparison analysis further confirms the robustness of the RK models, whereby improving the accuracy of estimation by up to 50% when compared to other methods. The findings in Phase II of the thesis suggests that the use of stationary RWIS data alone can generate reliable results (i.e., RMSE less than 1oC) when a known semivariogram model is available. The sensitivity analysis also reveals that the increase in the number of RWIS stations will improve the accuracy of estimation until it reaches a certain level, when the magnitude of benefits decreases and stabilizes. Lastly, a proposed RWIS location allocation optimizer is recommended to minimize the total kriging estimation error, for transportation authorities to delineate new site locations for improved monitoring capabilities.The proposed approaches provide a unique opportunity for continuous monitoring and visualization of road weather and surface conditions, to promote more efficient mobilization of winter maintenance resources. It is also anticipated that the findings of this research will, undoubtedly, contribute to improving the overall quality of winter road maintenance services and create a safer and more mobile environment for all travellers.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».