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Collide-O-Scope ODP
- 1. A window into real-time traffic hazardsA window into real-time traffic hazards Frank BentremFrank Bentrem Insight Data ScienceInsight Data Science Fall 2016Fall 2016 Collide-O-ScopeCollide-O-Scope
- 2. Traffic Safety ● Vision Zero – Reduce traffic injuries ● Provide real-time traffic hazard predictions to NYC public safety officials. 2015 53,176 Injuries 232 Fatalities
- 3. Collide-O-Scope ● Highlight roadways at significant risk for accidents ● NYPD may include this information in their patrol officer deployment plan. Clear Day
- 4. Collide-O-Scope ● Highlight roadways at significant risk for accidents ● NYPD may include this information in their patrol officer deployment plan. Rainy Day
- 5. Getting the Data ● Traffic Speed Cameras – NYC DOT (18 months) – Read license plate numbers ● Localized precipitation forecast – darksky.net ● Vehicle collisions – Office of Public Safety Brooklyn Bridge
- 7. Building the Model Generalized Linear Regression Cross Terms (e.g. Precipitation Intensity * Average Crashes) Coefficients Real-Time Data Traffic speeds Precipitation forecast Predicted crashes per hour for each road segment Bronx Brooklyn Manhattan Staten Island Queens R squared 0.39 0.60 0.50 0.21 0.54 Features Traffic speeds Precipitation forecast Daily/Weekly patterns
- 8. Building the Model Coefficients Real-Time Data Traffic speeds Precipitation forecast Predicted crashes per hour for each road segment Bronx Brooklyn Manhattan Staten Island Queens R squared 0.39 0.60 0.50 0.21 0.54 Features Traffic speeds Precipitation forecast Daily/Weekly patterns Generalized Linear Regression Cross Terms (e.g. Precipitation Intensity * Average Crashes)
- 9. Building the Model Generalized Linear Regression Cross Terms (e.g. Precipitation Intensity * Average Crashes) Predicted crashes per hour for each road segment Bronx Brooklyn Manhattan Staten Island Queens R squared 0.39 0.60 0.50 0.21 0.54 Features Traffic speeds Precipitation forecast Daily/Weekly patterns Real-Time Data Traffic speeds Precipitation forecast Fitted Coefficients
- 10. Building the Model Generalized Linear Regression Cross Terms (e.g. Precipitation Intensity * Average Crashes) Coefficients Real-Time Data Traffic speeds Precipitation forecast Bronx Brooklyn Manhattan Staten Island Queens R squared 0.39 0.60 0.50 0.21 0.54 Features Traffic speeds Precipitation forecast Daily/Weekly patterns Predicted crashes per hour for each road segment
- 11. Building the Model Generalized Linear Regression Cross Terms (e.g. Precipitation Intensity * Average Crashes) Coefficients Real-Time Data Traffic speeds Precipitation forecast Features Traffic speeds Precipitation forecast Daily/Weekly patterns Bronx Brooklyn Manhattan Staten Island Queens R squared 0.39 0.60 0.50 0.21 0.54 Predicted crashes per hour for each road segment
- 13. My Journey Frank Bentrem Scientific Computing, Ph.D. Polymer Simulations Acoustic Remote Sensing Teaching Physics Quantitative Finance Data Science Fellowship
- 14. A window into real-time traffic hazardsA window into real-time traffic hazards Collide-O-ScopeCollide-O-ScopeCollide-O-Scope
- 15. Future Improvements ● Compile collisions by road ● Increase data sample (snow) ● Study data anomalies – Missing data (May be missing at most relevent times) – Traffic Construction
- 16. Data Overview ● Individual road segments ● Removed holidays ● Seperated weekdays from weekends ● Hourly traffic profiles show morning/afternoon rush hours ● Speed negatively correlated to collisions
- 17. Data Overview ● Hourly Weather – Dark Sky App – darksky.net ● Speeds Cameras – NYC DOT – data.beta.nyc ● Vehicle Collisions – NYC DPS
- 18. Predict Traffic Flow using Precipitation Forecast ● Reduce traffic accidents – Advisories – Driver avoidance ● Improve delivery scheduling for businesses ● Improve travel-time estimates for apps