Fast followers? Speed contagion: Assessing the Impact of the Montreal F1 Grand Prix on High-Speed Collision Rates (2000–2022)

This project evaluates the potential influence of the Montreal Formula 1 Grand Prix on high-speed collision rates in Quebec from 2000 to 2022. We explore whether temporal spikes in dangerous driving behavior are associated with the F1 event, using a variety of causal inference and time-series modeling approaches. Principal investigator behind the project: Dr. Ignacio Nazif-Muñoz.

Project Structure

step1_weather_vars.html

Weather Variables and Event Setup

• Identifies Grand Prix dates by year and determines availability based on whether the event occurred.
• Defines pre/post-event time windows and day-of-week controls.
• Links these periods to weather data from nearby meteorological stations.
• Merges with high-speed collision data from provincial sources.

step1_5_weather_vars.html

Time-Series Construction and Exploration

• Collapses weather and collision variables into structured time-series datasets.
• Evaluates temporal dynamics including autocorrelation and seasonality.
• Prepares inputs for advanced modeling in later steps.

step2_analysis.html

Statistical Modeling: Case-Crossover and DiD

• Implements a stratified case-crossover design to control for time-invariant confounding.
• Uses Synthetic Control Methods (SCM) to build counterfactual trajectories for high-speed collisions.

step3_analysis_part_2.html

Causal Inference Using Google's CausalImpact package

• Estimates the causal impact using a Bayesian time series analysis of the F1 event across different years and windows.

step3_analysis_part_2.html

Post review analyses and additions

• Post review adjustments and additional analyses based on peer feedback.

step4_sensitivity_analysis.html