Cyclist injury risk and pollution exposure at urban signalized intersections
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".