This project is a database-driven travel itinerary planner that generates structured, multi-day travel plans for a city and visualizes optimized routes on an interactive map.
Unlike AI-generated systems, this application follows a procedural, deterministic approach, ensuring reproducible and explainable outputs.
This version (v2.0) additionally includes user authentication, session-based access control, dynamic time-based location ordering, and improved route visualization with merged polylines for smoother navigation rendering.
- Flask-based REST API
- SQLite database for persistent city and site data
- Procedural itinerary generation logic
- Graph-based route computation using OpenStreetMap data
- Session-based authentication and user-linked trip history
- Interactive map visualization using Leaflet.js
- Animated route drawing
- Timeline-style itinerary display
- Navigation instruction summary panel
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City & Location Database
- Cities and tourist locations stored persistently
- Easy expansion via database population scripts
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Procedural Itinerary Generation
- Automatically groups locations into day-wise plans
- Fixed rules ensure consistency and predictability
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Graph-Based Route Computation
- Uses road network graphs for route calculation
- Includes graceful fallback when routes are unavailable
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Interactive Visualization
- Animated route drawing
- Map markers and bounds adjustment
- Clean, modern UI for itinerary presentation
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User Authentication & Trip History
- Secure password hashing using PBKDF2
- Session-based login protection
- User-specific trip storage and retrieval
This project intentionally avoids AI-generated content to:
- Ensure deterministic outputs
- Improve explainability
- Enable consistent evaluation
- Support patentability and reproducibility
- Python (Flask, SQLAlchemy, Werkzeug Security)
- SQLite
- OpenStreetMap / OSMnx
- NetworkX
- Leaflet.js
- HTML, CSS, JavaScript
This system introduces a deterministic, database-driven approach to multi-day travel itinerary generation combined with graph-based route visualization.
Key novel aspects include:
- Rule-based itinerary generation without AI dependence
- Tight integration of persistent city databases with live road graphs
- Graceful fallback routing strategies for disconnected graphs
- Deterministic outputs suitable for academic evaluation and patent filing
The following components are intended for copyright and/or patent protection:
- Procedural itinerary generation logic
- Database schema and data organization strategy
- Route computation and fallback algorithms
- Frontend visualization workflow and interaction model
Graph cache files are generated locally and are not part of the core source code.
- Designed for deployment using Gunicorn in production environments
- Graph caching implemented to reduce repeated OpenStreetMap downloads
- Debug mode disabled for production builds
- Suitable for hosting on platforms such as Render or similar cloud services
License to be determined. All rights reserved © 2026 Aryan Mishra.
Aryan Mishra
B.Tech CSE – Manipal University Jaipur