Cyclist injury risk and pollution exposure at urban signalized intersections
Notice bibliographique
Résumé
Cycling as a mode of travel is becoming more popular especially in urban areas like Montreal, Canada. With this reality come serious concerns for cyclist safety and health. These concerns have initiated the need to study the determinants of cyclist injury risk as well as cyclist exposure to traffic-related air pollution. These two issues are particularly important at intersections where cyclists are exposed to high vehicular traffic and as a result are exposed to the risk of collisions and air pollution. With the goal of improving road safety and reducing cyclist exposure to air pollution, this report seeks to meet the following objectives, to: i) investigate the impact of motor-vehicle traffic, geometric design and built environment factors on cyclist injury occurrence and bicycle activity at signalized intersections in Montreal and ii) study the association between bicycle activity (volume) and traffic-related air pollution concentrations. As an application environment, this research makes use of a large sample of signalized intersections on the island of Montreal. In this work, cyclist injury risk was examined looking not only at aggregate cyclist and motor-vehicle flows passing through intersections but also at disaggregate traffic movements and potential conflicts. It was found that a 10% increase in bicycle flow is associated with a 5.3% increase in the frequency of cyclist injuries whereas a 10% increase in motor-vehicle flow would result in a 3.2% increase in cyclist injury occurrence. When disaggregating motor-vehicle flows into its constituent movements it becomes apparent that right turn movements have the greatest effect on injury occurrence. The conflict measure again confirms this result. Regarding the geometric design and built environment factor analysis, the presence of an arterial and bus stops were found to increase cyclist injury occurrence whereas protected left turn signals, pedestrian signals with countdown and there being three approaches instead of four were found to have the opposite effect on cyclist injury risk. From a health perspective, applying the nitrogen dioxide (NO2) land use regression model for Montreal, has revealed some interesting results. It was found that NO2 levels are highest in the central neighbourhoods of the island of Montreal which is also where cyclist flows are the greatest. The central neighbourhoods are also where Montreal's bicycle network is most dense and most frequented. Also, the corridor analysis revealed that corridors with a bicycle facility have more than twice as many cyclists as those without any facility but they also have, on average, higher air pollution levels. To investigate the indirect impact of built environment and bicycle infrastructure on the two variables of interest (cyclist injury risk and air pollution exposure at intersections), the determinants of bicycle activity were investigated. For this purpose, a bicycle activity modeling framework was developed to measure the impact of built environment, road and transit network attributes and bicycle facilities on bicycle activity. Regression models accounting for spatial autocorrelation between intersections were developed and it was found that land use mix, metro (subway) stations, schools and bicycle facilities all have a positive effect on bicycle activity whereas average street length and presence of parking entrances have a negative impact. Knowledge of the factors that increase or decrease cyclist injury occurrence combined with the knowledge of the factors that increase or decrease bicycle activity through intersections can guide engineering countermeasures and recommendations of land use strategies as well as the location of new facilities. This report provides initial insight into the currently limited body of research into cyclist injury risk and pollution exposure at urban signalized intersections.
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 | 0,000 | 0,001 |
| 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 | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».