hokiesoutsideee

Inspiration

Going out in downtown Blacksburg often starts with the same questions: Which places can I enter? How long is the line? How crowded is it? Is it worth walking there?

We created HokiesOutside to put those answers in one simple, mobile-friendly experience for Virginia Tech students. Our goal is to help people make better nightlife decisions before leaving their current location.

What it does

HokiesOutside helps users explore participating downtown venues and compare:

• Estimated line length and wait time
• Entry rate in people per minute
• Crowd level through the Hokie Meter
• Whether a venue is getting busier or quieter
• Typical age requirements
• Estimated walking time from the user’s location
• Directions through Apple Maps or Google Maps

Users can filter venues by age, search for a specific venue, and rank results by factors such as shortest wait, fastest entry, or current activity.

Our hackathon demonstration uses clearly labeled simulated data. It does not claim to display verified, live venue occupancy.

How we built it

We built HokiesOutside as a mobile-first web application using Next.js, React, TypeScript, Tailwind CSS, MongoDB, and Vercel.

A mock data pipeline generates predictable venue conditions for the initial demonstration, including line length, entry rate, wait time, crowd level, and trends. The architecture separates the data provider from the interface so simulated data can eventually be replaced with permission-based and partner-provided information.

Browser location permissions are optional and used to estimate walking time. Users may also opt in to anonymously help improve future crowd estimates. Venue cards include search, filters, map directions, and detailed condition information.

Challenges we ran into

One of our biggest challenges was presenting useful nightlife information without suggesting that simulated values were live or verified. We added clear simulation labels and confidence indicators throughout the experience.

We also had to handle location permissions, walking-distance calculations, age filtering, responsive venue cards, and situations where no simulation data was available. Another challenge was ensuring that valid zero values such as an empty Hokie Meter were displayed instead of being mistaken for missing data.

Accomplishments that we're proud of

We are proud that we created a complete, deployed experience rather than only a prototype. HokiesOutside includes:

• A responsive mobile-first interface
• Search and age-based venue filtering
• Simulated wait-time and crowd estimates
• The visual Hokie Meter
• Location-based walking estimates
• Apple Maps and Google Maps integration
• Venue detail pages
• Privacy-conscious opt-in controls
• A custom domain and production deployment

We also designed the system so its simulated data source can later be replaced without rebuilding the entire application.

What we learned

We learned how important transparency is when building products around predictions and crowd information. A useful estimate must clearly communicate its source, confidence, and limitations.

We also gained experience with geolocation, distance calculations, data modeling, responsive interface design, deployment, DNS configuration, testing, and building an extensible full-stack application under hackathon time constraints.

What's next for hokiesoutsideee

Our next step is to move beyond simulated conditions through venue partnerships and privacy-conscious, opt-in community signals.

Future improvements could include:

• Anonymous occupancy contributions from participating users
• Venue-provided entrance counts and wait-time updates
• Improved crowd and wait-time prediction models
• Historical activity trends
• Event-specific age and admission information
• Notifications when a selected venue’s conditions improve
• Expansion to additional college towns

Our long-term vision is to make HokiesOutside a reliable decision-making tool for safer, easier, and more informed nights out.

Built With

• api
• apple
• css
• google
• gps
• learning
• machine
• maps
• mongodb
• next.js
• node.js
• playwright
• python
• react
• scikit-learn
• tailwind
• typescript
• vercel
• vitest