Toronto Traffic Safety Initiative

Transforming urban mobility through data-driven solutions for a safer, smarter, and more sustainable future.

The Challenge

Urban centers like Toronto face increasing pressure from growing populations, rising vehicle usage, and the urgent need for sustainability. Our mission is to analyze and optimize traffic flow to improve transportation efficiency, safety, and sustainability through innovative data-driven solutions.

30%

Reduction in accidents with upgraded traffic signals

78%

Reduction in severe crashes with roundabouts

15%

Fewer collisions with automated enforcement

Traffic Patterns & Analysis

Daily Traffic Patterns

Vehicle traffic by day

Analysis shows peak congestion Tuesday through Thursday, with moderate traffic on Saturdays and lowest volume on Sundays.

Vulnerable Road Users

Vulnerable road users by day

Pedestrian and cyclist activity peaks during weekdays, particularly during school and work commute hours.

Hourly Patterns

Hourly vulnerable road users

Peak activity occurs between 8-10 AM and 3-7 PM, with the highest volume during the 5-6 PM rush hour.

High-Risk Intersections

Top Intersections by Volume

  • Bloor St W / Major St W - Over 400,000 pedestrian and cyclist movements
  • Bloor St W / Avenue W - High foot traffic near major transit routes
  • Bloor St W / Madison Cres - Heavy commuter and student pedestrian volume
  • Bloor St W / Spadina Ave - Significant mix of vehicle and pedestrian activity
  • Bloor St W / Bathurst St W - High risk for pedestrian-vehicle conflicts
  • Top Intersections by Pedestrian-to-Vehicle Ratio

  • Shaw St / Essex St - Extremely high pedestrian-to-vehicle ratio
  • Argyle St / Argyle Pl - More pedestrians than vehicles
  • Ontario St / Winchester St - High pedestrian priority potential
  • Kensington Ave / Baldwin St - Located in pedestrian-heavy market area
  • Kensington Ave / St Andrew St - High cyclist and pedestrian usage
  • Top Vulnerable Volume

    Top 10 intersections by vulnerable users

    Analysis of intersections with the highest volume of pedestrian and cyclist traffic.

    Vulnerability Ratio

    Top 10 intersections by vulnerability ratio

    Areas with the highest ratio of vulnerable road users to vehicles, indicating potential pedestrian zones.

    Interactive Traffic Map

    What the Smart Pedestrian Protection System Will Do

    🔄

    Automation

    AI-Driven Adaptive Smart Signals: Real-time pedestrian detection adjusts crossing times and optimizes vehicle flow.

    🛡️

    Prevention

    Red-Light Guardian System: Prevents pedestrian-vehicle collisions by extending red lights when pedestrians are detected.

    💡

    Visibility

    Smart Crosswalks with LED Alerts: Embedded LED crosswalks signal drivers and pedestrians in high-risk situations.

    How Effective is SPPS?

    Predictive Algorithm Impact

    By implementing a predictive algorithm to determine collision factors, our system has demonstrated the potential for a 35% reduction in collision rates. This significant improvement is achieved through:

    • Real-time traffic pattern analysis
    • Automated risk assessment
    • Proactive safety measure deployment
    • Continuous system learning and adaptation
    Graph showing collision risk before and after intervention

    Collision Risk Reduction Analysis

    Why This Strategy Will Work

    Minimal Infrastructure Disruption

    Integrates with Toronto's existing SCATS system, making it a cost-effective and scalable solution.

    Proven Global Success

    Singapore's GLIDE system reduced pedestrian-vehicle collisions by 35%, demonstrating the effectiveness of this approach.

    Data-Driven Risk Detection

    Uses AI & IoT sensors to analyze real-time traffic and pedestrian flow, ensuring safety measures are deployed where needed most.

    Our Methodology

    Data Collection

    Comprehensive traffic data collection from multiple sources including:

    • Traffic cameras and sensors
    • Pedestrian counters
    • Historical accident reports
    • Public transit data

    Analysis Techniques

    Advanced data analysis methods including:

    • Machine learning algorithms
    • Pattern recognition
    • Predictive modeling
    • Risk assessment frameworks

    Validation Process

    Rigorous validation through:

    • Cross-referencing multiple data sources
    • Statistical significance testing
    • Expert consultation
    • Community feedback

    Implementation Timeline

    Phase 1: Initial Deployment

    Implementation of AI-driven smart signals at top 5 high-risk intersections

    Phase 2: Infrastructure Updates

    Installation of LED crosswalks and protected bike lanes

    Phase 3: System Integration

    Integration of Red-Light Guardian System with existing infrastructure

    Phase 4: Full Deployment

    Citywide rollout of complete Smart Pedestrian Protection System

    Projected Impact

    45%

    Reduction in pedestrian-vehicle conflicts at smart intersections

    30%

    Decrease in average wait times for all road users

    25%

    Reduction in emissions from improved traffic flow

    60%

    Increase in cyclist safety at protected intersections

    Environmental Impact

    Our solution contributes to Toronto's sustainability goals through:

    • Reduced vehicle idling time and emissions
    • Promotion of active transportation
    • Support for electric vehicle adoption
    • Integration with green infrastructure initiatives