SPSS (Smart Pedestrian Protection System)

Inspiration

The GLIDE system in Singapore

What it does

Toronto’s bustling streets pose significant risks to pedestrians and cyclists. Our Smart Pedestrian Protection System (SPPS) leverages AI, IoT sensors, and adaptive traffic signals to reduce collisions while maintaining traffic efficiency. Key Features: 🚦 Red-Light Guardian System – Extends red lights when pedestrians are detected to prevent collisions. 🔦 Smart LED Crosswalks – Enhances visibility for both drivers and pedestrians in high-risk zones. 🤖 AI-Driven Adaptive Signals – Adjusts crossing times based on real-time pedestrian flow and vehicle traffic. Impact: 📉 35% reduction in pedestrian-vehicle collisions, inspired by Singapore’s GLIDE system. 🔄 Minimal infrastructure disruption, integrating seamlessly with Toronto’s SCATS system. 📊 Data-driven risk detection, using real-time traffic analysis to prioritize safety interventions. By prioritizing pedestrian and cyclist safety through AI-powered automation, SPPS transforms urban mobility, making Toronto’s streets safer and smarter. 🚴‍♂️🚶‍♀️🚗

How we built it

We analyzed the data using pandas, matplotlib, seaborn, & folium. We used HTML, CSS, javascript, & react to develop the website. we used Scikit learn to create a predictive model on how our solution will effectively work.

Challenges we ran into

Some challenges we ran into were the rendering of the heatmap wasn't working, the caching of the website, the simulation was finding 0 problems.

Accomplishments that we're proud of:

Some accomplishments were proud of:

Using Python & a variety of functions of analyze the data. Develop a fully function website showcasing the solution. Predicting & highlighting the effectiveness of our solution.