SafePulse helps urban commuters choose the safest route, not just the fastest one.
Instead of relying only on ETA, SafePulse uses real-time or simulated urban signals like crowd density, lighting, incidents, traffic, and weather to generate a Safety Score and recommend the most reliable route within seconds.
Built for hackathon impact: clear problem, fast actionable output, realistic logic, and an instantly understandable demo.
Urban travel decisions are often made with incomplete information.
A route may be fast but poorly lit, isolated, recently reported unsafe, or less active at night.
SafePulse addresses one focused problem:
“How can a commuter quickly decide which route is safest right now?”
Instead of building a full mobility platform, SafePulse delivers a practical micro-solution:
- Compare route options
- Understand risk instantly
- Get an AI-generated recommendation
- Act within seconds
- Solves one real problem well instead of trying to solve all of urban mobility
- Actionable output in seconds with a clear recommended route
- Explainable AI logic through transparent safety scoring
- Live intelligence feel using dynamic/simulated real-time signals
- Hackathon-ready prototype that is visually polished, feasible, and demo-friendly
- Source → destination route analysis
- Three route options with ETA
- Safety Score for each route
- Risk tags such as:
- Low lighting
- Recent incident
- Isolated stretch
- High pedestrian activity
- Rain impact
- Congestion shift
- AI recommendation engine
- Live city conditions panel
- Explainable “Why this route?” section
- Emergency confidence features
- Share trip status
- Safer alternative alert
- Fast interactive prototype experience
SafePulse simulates urban mobility intelligence by combining multiple signals into a route-level safety evaluation.
- Time of day
- Weather
- Crowd density
- Street lighting quality
- Recent reported incidents
- Traffic conditions
- Public activity level
- User preference:
- Safest
- Balanced
- Fastest
Each route is scored based on weighted safety signals.
If conditions change, the route ranking changes too.
Example reasoning:
- “This route is 3 minutes longer but significantly safer due to better lighting and active shops.”
- “Avoiding this stretch because footfall drops after 9 PM.”
- “Rain and low visibility reduced the confidence score on Route B.”
A judge or user can understand the product in under a minute:
- Enter source and destination
- Select commuting preference
- View 3 route options
- Compare ETA vs Safety Score
- See live city signals update
- Read AI recommendation
- Pick the safest available route
- Frontend: HTML, CSS, JavaScript
- Backend: Node.js, Express
- Prototype Logic: Simulated real-time route intelligence
- Architecture: Lightweight, fast, hackathon-friendly MVP
Safepulse/
│── node_modules/
│── index.html
│── server.js
│── package.json
│── package-lock.json
│── README.mdgit clone https://github.com/pavani-n-hash/Safepulse
cd Safepulsenpm installnode server.jshttp://localhost:3000Imagine a commuter returning home at night.
Traditional map apps may recommend the fastest path.
But SafePulse identifies that this path has:
- low lighting,
- low pedestrian presence,
- and a recent incident flag.
So instead, it recommends another route that is:
- 3 minutes longer,
- better lit,
- more active,
- and meaningfully safer.
This makes SafePulse not just a navigation idea, but a decision intelligence tool for safer movement in cities.
SafePulse is intentionally built as a micro-solution.
It does not require a full smart-city infrastructure to demonstrate value.
The concept can begin with simulated inputs and later integrate with:
- map APIs
- public transport feeds
- city safety reports
- IoT lighting or traffic signals
- anonymized crowd/activity data
That makes the prototype both practical for a hackathon and credible beyond the event.
Most route tools optimize for speed.
SafePulse optimizes for commuter confidence and safety awareness.
That shift changes the product from:
- “Which route is fastest?” to
- “Which route is safest for me right now?”
This human-centered framing is the key innovation.
SafePulse can help support:
- safer night commutes
- better informed urban travel choices
- improved commuter confidence
- more inclusive mobility for vulnerable users
- smarter real-time route decisions in uncertain environments
Potential beneficiaries:
- students
- office commuters
- women traveling late
- solo travelers
- people unfamiliar with a city
- parents monitoring dependents’ travel
- Real-time API integrations
- Personalized commuter profiles
- Voice-based assistant mode
- Safety heatmap overlays
- Incident-aware adaptive rerouting
- Smartwatch emergency support
- Predictive unsafe zone alerts
Add these before submission:
-
Hero page screenshot :
-
Route comparison screenshot :
-
Live signals screenshot :
-
AI explanation screenshot :
SafePulse is designed to match hackathon judging expectations:
- Clear problem statement
- Focused MVP
- Functional prototype
- Explainable technical logic
- Strong user impact
- High demo clarity
- Real-world scalability
Team Name: SafePulse
Project: Smart Mobility Intelligence System
Built by: Pavani
SafePulse is an AI-powered urban mobility micro-solution that helps commuters make safer route decisions in real time.
By combining live or simulated city signals into an explainable Safety Score, the system recommends the best route within seconds and turns navigation into a smarter, safer decision-making experience.
This project is created for hackathon/demo purposes.