Exploring the impact of a dedicated streetcar right-of-way on pedestrian motor vehicle collisions: A quasi experimental design
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The frequency of pedestrian collisions is strongly influenced by the built environment, including road width, street connectivity and public transit design. In 2010, 2159 pedestrian collisions were reported in the City of Toronto, Canada with 20 fatalities. Previous studies have reported that streetcars operating in mixed traffic pose safety risks to pedestrians; however, few studies evaluate the effects on pedestrian-motor vehicle collisions (PMVC). The objective of this study was to examine changes in the rate and spatial patterning of PMVC, pre to post right-of-way (ROW) installation of the St. Clair Avenue West streetcar in the City of Toronto, Canada. METHODS: A quasi-experimental design was used to evaluate changes in PMVC rate, following implementation of a streetcar ROW. Collision data were extracted from all police-reported PMVC, complied and verified by the City of Toronto, from January 1, 2000 to December 31, 2011. A zero-inflated Poisson regression analysis estimated the change in PMVC, pre to post ROW. Age and injury severity were also examined. Changes in the spatial pattern of collisions were examined by applying the G function to describe the proportion of collision events that shared a nearest neighbor distance less than or equal to a threshold distance. RESULTS: A total of 23,607 PMVC occurred on roadways during the study period; 441 occurring on St. Clair Ave, 153 during the period of analysis. There was a 48% decrease in the rate of collisions on St. Clair [Incidence rate ratio (IRR)=0.52, 95% CI: 0.37-0.74], post ROW installation. There were also decreases noted for children (IRR=0.13, 95% CI: 0.04-0.44), adults (IRR=0.61, 95% CI: 0.38-0.97), and minor injuries (IRR=0.56, 95% CI: 0.40-0.80). Spatial analyses indicated increased dispersion of collision events across each redeveloped route segment following the changes in ROW design. CONCLUSIONS/IMPLICATIONS: Construction of a raised ROW operating on St. Clair Ave. was associated with a reduction in the rate of collisions. Differences in pre- and post collision spatial structure indicated changes in collision locations. Results from this study suggest that a streetcar ROW may be a safer alternative for pedestrians compared to a mixed traffic streetcar route and should be considered by city planners where appropriate to the street environment.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".