aliivaezii

aliivaezii / RL-Mobility-Optimizer

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MoveWise: Reinforcement Learning for Behaviorally-Aware Mobility-as-a-Service. Deep Q-Network engine with React/Three.js frontend for sustainable urban transport nudging. NEXUS 2026 — Politecnico di Torino.

aliivaezii.github.ioRL-Mobility-Optimizer behavioral-economics deep-q-network docker fastapi gamification
20
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100% credibility
Found Mar 14, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

MoveWise is a mobile super-app that combines route planning, payments, carpooling, insurance tools, and rewards to encourage sustainable urban travel.

How It Works

1
📱 Download MoveWise

You hear about this app from a friend or university email and install it on your phone to make your daily commute greener and cheaper.

2
✅ Welcome and agree

The app greets you warmly, explains how it protects your privacy, and you tap to accept and continue.

3
🤖 Meet your guide Movi

A friendly robot tutor pops up and walks you through the app with fun animations, showing how it finds smart routes just for you.

4
🗺️ Plan your first trip

Pick where you're going, like home to campus, and see colorful route options ranked by what's best for you, cheapest, fastest, or greenest.

5
💳 Tap QR and go

Choose your favorite route, scan a simple QR code to pay for buses, trains, or scooters, and watch your journey track live.

6
🏆 Earn rewards

Finish your trip and collect green points, badges, and climb the leaderboard with friends while seeing your CO2 savings grow.

🌿 Smarter, greener you

Over time, you save hundreds of euros yearly, cut car insurance costs, and feel great knowing your commutes help the planet.

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AI-Generated Review

What is RL-Mobility-Optimizer?

MoveWise is a behaviorally-aware mobility-as-a-service optimizer that uses deep Q-network learning to recommend sustainable urban routes and nudge users from car dependency to multimodal options like trains, e-scooters, and carpools. Built for the NEXUS 2026 hackathon at Politecnico di Torino, it delivers a full super-app experience via a React/Three.js HTML frontend simulating a phone interface, paired with a Python RL engine for personalized suggestions based on habits, costs, and emissions. Users get gamified nudging, QR payments, insurance discounts, and leaderboards to cut CO2 and save money on Torino commutes.

Why is it gaining traction?

It stands out with a production-ready demo that visualizes RL decisions through interactive 3D route arcs, emissions skylines, and tutor bots, making abstract deep learning tangible for mobility apps. The behaviorally-aware engine personalizes nudges like social proof or loss framing, outperforming static planners by adapting to user phases from park-and-ride to full green trips. Developers dig the dockerized setup and mock API endpoints ready for real integrations like GTFS or Stripe.

Who should use this?

Transport engineers prototyping MaaS platforms, urban planners testing nudging strategies, or hackathon teams building RL demos for sustainable mobility. Ideal for Politecnico students or devs simulating behavior shifts in city corridors like Caselle to Orbassano.

Verdict

Grab it for inspiration on RL in mobility— the frontend shines, and the deep engine shows real nudge power—but with 19 stars and 1.0% credibility, treat it as a polished hackathon prototype, not production code. Fork and extend for your next transport project.

(198 words)

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