Regional impact of Montreal's new highway toll bridge on road traffic and road safety
Notice bibliographique
Résumé
In May 2011, a toll bridge was opened to public on Highway 25 to provide a new link connecting the islands of Montreal and Laval. The objective of this infrastructure was to relieve the road network of traffic by providing an additional alternative route for a subgroup of origins and destinations located on the Island of Montreal and its neighboring cities. This research examined the impact of the new infrastructure on traffic redistribution and safety conditions. In order to complete this work, multiple data sources were used, among others, such as traffic counts before and after the bridge opening, accident data, and the transportation model of the region of Montreal. The first part of the research examined the evolution of traffic counts using different indicators and a linear regression technique. The second part presents the development of collision prediction models used to assess the safety impact of the infrastructure. In addition, a tool was developed to automate the collision estimation process for different network scenarios. Considering the traffic impact, the average daily volume indicator showed that the two most impacted bridges by the new Highway 25 toll bridge were the Highway 40 Charles-de-Gaulle (CDG) Bridge (-14% of the average daily traffic) and the Pie-IX Bridge (-11% of the average daily traffic), which are the immediate adjacent bridges with respect to the new infrastructure.These bridges were also examined for each direction of traffic; it was found that the most important traffic level reductions were observed on the peak traffic directions. Examining the evolution in hourly volumes, it was noted that the most impacted periods of the day were the peak periods, where traffic conditions are worse than the other periods of the day. The average hourly traffic indicator also presented a shift of some off-peak trips to the peak periods on the Highway 40 CDG Bridge. The last method employed to assess the traffic impact of the new infrastructure was based on a linear regression and aimed to integrate the effects of different variables such as the temperature, precipitation, and gas price. It was found that temperature and precipitation had positive and negative correlations with traffic volumes, respectively.Regarding the safety impact of the Highway 25 toll bridge, the negative binomial regression model was used to estimate collision frequencies for vehicle-vehicle and vehicle pedestrian collisions on links and intersections. The statistical model's results predicted a change in the collisions' pattern matching the change in the traffic pattern following the opening of the new bridge. Considering a constant demand, the overall impact of the new infrastructure was found to have a positive effect on safety since the total number of link and intersection collisions was reduced. However, the 2016 traffic demand increased the collision frequencies for both types of collisions on links and intersections. The Collision Estimation Tool was also developed and is capable of estimating collision frequencies for a road network and comparing the collision estimates of different scenarios. The applicability of this tool was demonstrated through its use for the Highway 25 toll bridge impact assessment.
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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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».