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Enregistrement W7161978714 · doi:10.82308/32031

Evaluating the Dual Impacts of Public Transit: A Comparative Approach to Assess Physical Activity, Health, and Road Safety in Bus Rapid Transit

2025· dissertation· en· W7161978714 sur OpenAlexaboutno aff
Alejandro Pérez Villaseñor

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueUrban Transport and Accessibility
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBus rapid transitPublic transportTransit (satellite)Data collectionService (business)Quality (philosophy)Key (lock)

Résumé

récupéré en direct d'OpenAlex

Public transit is crucial in Latin America, promoting social equity, physical activity, economic development, and environmental sustainability. While transit users benefit from these advantages, they are also exposed to various risk factors affecting their health, well-being, and safety. These risks depend on factors such as local context, transit service characteristics, and infrastructure design. This thesis investigated both the impacts of public transit systems on physical activity and the public health and road safety risks faced by transit users, with a particular focus on BRT systems. Specifically, the research examined three key aspects: 1) the effect of BRT implementation on physical activity levels; 2) the air quality conditions within transit vehicles, compared across different public transit modes; and 3) the risk factors related to road safety near BRT facilities, particularly for vulnerable road users. The findings aimed to enhance understanding of the benefits and risks of BRT systems, providing guidance for safety measures, improved service and infrastructure designs, and policies that lead to safer, more comfortable transit systems.A systematic approach was adopted to design and implement the research methodologies. The first step involved comprehensive data collection campaigns using different instruments and technologies. Next, the data was prepared and analyzed, implementing alternative statistical and machine learning techniques. Finally, key results and recommendations were formulated considering their practical implications. To examine the effects of BRT implementation on physical activity, we used the data from the International Physical Activity Questionnaire applied in Rio de Janeiro, Brazil, and Mexico City before and after the implementation of BRT systems in each city. The dataset includes over 8,000 responses from the population living in the systems’ catchment area. A Propensity Score Matching methodology was then applied to identify users with similar sociodemographic characteristics in both periods. Then, a Cragg-hurdle regression model that accounts for the zero-inflated results was implemented to evaluate the change in the time people walked after the project’s start. To examine the air quality conditions inside transit units, fixed-interval data on Carbon Dioxide (CO2) and Black Carbon (BC) concentrations were measured in BRTs, subways, and buses in Montreal (Canada), Mexico City, and Puebla (Mexico) to identify those factors across modes and environments that affect the indoor air quality. Over 116 CO2 and 30 BC hours of observations were collected between 2023 and 2024. Then, statistical comparison and autoregressive multilevel random-effects models evaluated the impact of the system’s characteristics —mode, route environment, level of crowdedness, and air-exchange systems— on air quality. Finally, to evaluate the effects of a BRT system on road safety, extensive video data collection was conducted at nine intersections in proximity to transit access points. From video data, individual road-user trajectories and a set of road-user characteristics were extracted using specialized software. This was followed by a comparative analysis of intersections with and without BRT facilities. TA multilevel mixed-effects regression analysis was used to identify which salient factors, such as vehicle-pedestrian interaction scenarios, play a role in road safety.Among other results, this work showed that BRT systems positively affect physical activity; however, significant variations can be observed across cities and population groups. Specifically, we observed that the additional time people spent walking in Mexico City increased by approximately 29% compared to the period before the BRT implementation, while in Rio de Janeiro, the increase was about 58%. Similarly, the differences varied across groups in the two cities. For example, in Mexico City, female respondents increased more than twice their all-purpose walking time than male respondents (9% vs. 4%). In Rio de Janeiro, the difference was minimal, as female and male respondents increased their walking time at comparable rates (53% for females versus 49% for males).While BRT systems positively impact physical activity, they also present challenges related to air quality and safety. CO2 and BC measurements in BRT units exceed in 23% and 50% of observations the critical values established by organisms like the World Health Organization and the Occupational Safety and Health Organization of America, respectively; it is essential to highlight that these variations also depend on the context and vehicle technology. For instance, In Montreal, only 21% of BC observations surpassed the accepted threshold, while in Puebla, this percentage increases to 78%. This could be explained by the newer engine technologies in Montreal’s bus units and stricter regulations and maintenance strategies. Despite the high levels of CO and BC in BRT systems, other modes such as regular buses and subways showed poorer air quality levels, especially when considering BC. For instance, 80% of the observations were above the accepted thresholds in subways. This situation is likely related to the underground subway segments, which suggests that the elevated values are caused by BC accumulating over time and not necessarily by the propulsion systems.Concerning road safety, our findings suggest that intersections with BRT facilities are less safe, particularly for pedestrians. Intersections in the proximity to BRT stations showed smaller Post Encroachment Times (PET) while having lower vehicle speeds. This translates in pedestrian-vehicle interactions being 12% more dangerous at intersections with BRT facilities compared to intersections with bus stops, observing the ratio between PET and the time it takes a vehicle to stop —which is dependent on speed. This research offers valuable contributions by providing easily replicable methodologies and empirical evidence that can aid in identifying policies and countermeasures to enhance public transit service conditions regarding users’ health and road safety. Additionally, it underscores the importance of analyzing each city and transit system as an independent entity despite potential similarities in socio-geographical contexts. Finally, despite the contributions of our research, further studies and more data across seasons are recommended to validate the results in other LA cities. Additionally, exploring alternative statistical and machine-learning methods may yield insights beyond this work's scope

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,031

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0050,004
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,243
Tête enseignante GPT0,472
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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