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Published on: September 21, 2017
Cyclist activity and injury risk analysis at signalized intersections: a Bayesian modelling approach
Jillian Strauss1, Luis F Miranda-Moreno, Patrick Morency
1Department of Civil Engineering and Applied Mechanics, McGill University, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, QC H3A 2K6, Canada.
Accident; Analysis and Prevention
|June 8, 2013
Summary
More cyclists mean more injuries, but lower injury rates, at intersections. This Bayesian study identifies factors influencing cyclist safety and activity, highlighting urban planning
Area of Science:
- Transportation Safety
- Urban Planning
- Statistical Modeling
Background:
- Signalized intersections are critical points for cyclist safety and traffic flow.
- Understanding the interplay between cyclist activity and injury risk is crucial for urban mobility.
- Existing models often fail to account for endogeneity and unobserved factors in cyclist safety.
Purpose of the Study:
- To develop and apply a joint Bayesian modeling approach for cyclist injury occurrence and bicycle activity.
- To identify key factors influencing both cyclist injuries and bicycle volumes at signalized intersections.
- To assess cyclist injury risk and identify high-risk corridors in Montreal, Quebec, Canada.
Main Methods:
- A two-equation Bayesian model was employed to analyze cyclist injury occurrence and bicycle activity as joint outcomes.
- Data included disaggregate traffic volumes, bicycle flows, intersection geometry, traffic control, and built environment characteristics.
- Manual bicycle counts were standardized to obtain average annual daily volumes for the period 2003-2008.
Main Results:
- Increased cyclist and motor-vehicle flows correlate with cyclist injury occurrence, though higher cyclist volumes show a non-linear decrease in injury rates.
- Factors like bus stops and crosswalk length increase injury occurrence, while raised medians decrease it.
- Bicycle activity is positively associated with employment, metro stations, mixed land use, commercial areas, bicycle facilities, and schools; negatively with intersection approach numbers.
Conclusions:
- The study quantifies the impact of traffic volumes and intersection characteristics on cyclist safety.
- Bayesian analysis enabled the estimation of injury risk for ranking corridors, revealing lower-risk corridors in central Montreal despite higher overall injury counts.
- Findings support the 'safety in numbers' hypothesis and suggest cyclist route preferences towards safer environments.
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