Evaluating the Dual Impacts of Public Transit: A Comparative Approach to Assess Physical Activity, Health, and Road Safety in Bus Rapid Transit
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".