Travana: A Decentralized Ride-Sharing Platform with Fuzzy Location Matching

Problem: Centralized Ride-Sharing is Broken

Today’s ride-sharing platforms pose major concerns:

  • Location Privacy Violation: Exact GPS tracking compromises user safety.

  • Centralized Control: Platforms enforce arbitrary policies, fees, and control payments.

  • Lack of Trust & Transparency: Drivers and riders have little recourse or visibility into how fares are calculated or funds are handled.


Our Solution: Travana

Utilizing Hyperion’s blockchain and on-chain AI, we aim to create a decentralized, privacy-focused ride-sharing platform that prioritizes user needs while ensuring security, transparency, fairness, and scalable real-world mobility.


Fuzzy Location Matching

Travana uses geohashing to protect rider and driver identities by matching based on broader zones rather than precise coordinates. As we gather real-world data, we will adopt AI-based clustering to dynamically create travel zones optimized by real-time demand, traffic flow, and behavior—enhancing both privacy and accuracy.


Smart Contract-Based Escrow (on Hyperion)

Ride payments are locked in Hyperion-native smart contract escrows, released only when both the rider and driver confirm trip completion. This ensures a trustless, tamper-proof transaction process—eliminating third-party control or platform manipulation.


AI Automation & Fair Pricing with Alith Framework

We integrate the Alith Framework to enhance rider experience and economic fairness:

  • Conversational Booking: Book rides by simply typing or speaking (e.g., “Go to Market Zone at 6 PM”). AI translates this into structured on-chain ride requests.

  • Personalized Automation: The system adapts to your ride timing, price sensitivity, and preferences using past behavior.

  • Real-Time Dynamic Pricing: Alith adjusts fares based on real-time supply, demand, weather, and route conditions. This ensures dynamic fair pricing—not platform-imposed fees.


Scalability on the Hyperion Blockchain

Travana on the Hyperion chain, with its high-speed parallel transaction execution and low gas costs. Hyperion allows us to:

  • Handle high volumes of concurrent ride-bookings

  • Process user interactions in real time

  • Keep the experience fast, cost-efficient, and global-ready

This infrastructure ensures Travana scales as user demand grows across cities and regions.


NFTs as Proof-of-Ride

Each completed ride mints a unique NFT on Hyperion, which acts as:

  • A verifiable ride certificate

  • A rewards and gamification asset

  • A tool for dispute resolution

These ride NFTs create a transparent on-chain ride history while preserving user privacy.


Decentralized Identity & Reputation

Users authenticate through wallet addresses, maintaining privacy while building a reputation score based on on-chain behavior: ride completions, feedback, disputes, and referrals. This reputation system empowers safe and reliable interactions.


Community-First Incentivized Engagement

Travana features a point-based rewards model to encourage user adoption, retention, and referrals. Users earn points through actions such as:

Activity Reward Points
Sign up and connect wallet +50
Book your first ride +100
Complete a ride and mint NFT +150
Leave feedback for a driver/rider +30
Refer a friend +50

Points can be redeemed for ride discounts, premium features, or limited-edition ride NFTs—fueling organic growth through viral user loops.

This point system incentivizes frequent use and platform exploration. Users earn rewards for engaging and onboarding others, creating a viral loop that accelerates adoption and builds a loyal user base.

User Onboarding Incentives:

New users are motivated to try the app and refer others to maximize their points and unlock exclusive benefits.

Getting Involved

For Developers:

  • Contribute to smart contracts, backend, or frontend
  • Propose features, fix bugs, and build extensions

For Testers & Riders:

  • Join our beta waitlist
  • Provide feedback and shape feature development

Flow Diagram:

15 Likes