Transforming Road Data Management with Multi-Agent AI
RoadFusion is a cutting-edge platform that revolutionizes how road infrastructure data is validated, analyzed, and merged into production systems. Using a sophisticated multi-agent AI architecture, RoadFusion ensures that only high-quality, validated road data makes it into mapping systems, improving navigation accuracy and safety for millions of users worldwide.
Analyst dashboard showing the multi-agent analysis pipeline in action
Interactive map visualization of road data with quality indicators
Vendor trust score dashboard showing historical performance metrics
Detailed decision analysis with confidence scores and reasoning
Mobile companion app for field validation and user feedback collection
RoadFusion employs a sophisticated multi-agent system where specialized AI agents work collaboratively to analyze road data:
- Data Extraction Agent: Processes raw GeoJSON data to identify road segments, intersections, and traffic signals
- External Validation Agent: Cross-references data with trusted sources like Google Maps, Waze, and OpenStreetMap
- News Analysis Agent: Scans recent news for road construction, closures, or changes that might affect data accuracy
- Decision Agent: Combines all inputs to make the final merge recommendation with confidence scoring
Our platform features a unique vendor trust scoring system that evolves over time:
- Vendor trust scores are dynamically updated based on data quality history
- Each successful merge increases vendor reliability ratings
- Poor quality submissions decrease trust scores
- System automatically adjusts approval thresholds based on historical performance
RoadFusion incorporates real-world feedback to continuously improve:
- Companion mobile app allows users to submit field observations
- Feedback is used to validate vendor data submissions
- Reinforcement learning mechanisms incorporate user reports into trust calculations
- Creates a virtuous cycle of data improvement
Unlike black-box systems, RoadFusion provides clear reasoning for all decisions:
- Detailed confidence scores with component breakdowns
- Visual comparison of submitted data vs. existing map data
- Comprehensive reasoning for approval/rejection decisions
- Full audit trail of all data validations
- Python: Core application logic and API endpoints
- Flask: Web framework for RESTful API
- SQLite: Database for storing GeoJSON files and analysis results
- Google Gemini Pro: Advanced LLM for complex reasoning and decision-making
- React: UI framework with TypeScript
- Tailwind CSS: Utility-first CSS framework
- Shadcn/UI: Component library
- Vite: Build tool and development server
- GeoJSON: Standard format for geospatial data
- HERE Maps API: Geospatial data validation and enrichment
- OpenStreetMap: Reference data for validation
flowchart TD
subgraph "User Interfaces"
VP[Vendor Portal]
AP[Analyst Portal]
MA[Mobile App\nUser Feedback]
end
subgraph "API Layer"
API[REST API Services]
end
subgraph "Multi-Agent AI System"
MAIS[AI Orchestrator]
subgraph "Specialized Agents"
DEA[Data Extraction Agent]
EVA[External Validation Agent]
NAA[News Analysis Agent]
DMA[Decision Making Agent]
end
DEA --> EVA
EVA --> NAA
NAA --> DMA
end
subgraph "External Services"
GM[Google Maps API]
OSM[OpenStreetMap API]
WZ[Waze API]
HM[HERE Maps API]
GEM[Google Gemini Pro]
end
subgraph "Data Storage"
DB[(SQLite Database)]
GJ[(GeoJSON Files)]
end
VP --> API
AP --> API
MA --> API
API --> MAIS
MAIS --> DEA
DEA --> DB
EVA --> GM
EVA --> OSM
EVA --> WZ
EVA --> HM
NAA --> GEM
DMA --> GEM
DEA --> GJ
MAIS --> API
classDef interfaces fill:#00c2ff,stroke:#333,stroke-width:2px,color:#000
classDef api fill:#ff3e9d,stroke:#333,stroke-width:2px,color:#000
classDef agents fill:#0066ff,stroke:#333,stroke-width:2px,color:#000
classDef external fill:#ff9e00,stroke:#333,stroke-width:2px,color:#000
classDef storage fill:#00e5ff,stroke:#333,stroke-width:2px,color:#000
class VP,AP,MA interfaces
class API api
class DEA,EVA,NAA,DMA,MAIS agents
class GM,OSM,WZ,HM,GEM external
class DB,GJ storage
flowchart LR
GJ[GeoJSON Data] --> DEA
subgraph "Multi-Agent Pipeline"
DEA[Data Extraction Agent] --> |Road Segments\nIntersections\nTraffic Signals| EVA
EVA[External Validation Agent] --> |Validation Results\nMatch Rates| NAA
NAA[News Analysis Agent] --> |News Impact\nRecent Changes| DMA
DMA[Decision Making Agent] --> |Final Decision\nConfidence Score| OUT
end
subgraph "Data Extraction Agent"
direction TB
DEA1[Parse GeoJSON] --> DEA2[Extract Features]
DEA2 --> DEA3[Identify Road Segments]
DEA2 --> DEA4[Detect Intersections]
DEA2 --> DEA5[Locate Traffic Signals]
DEA3 & DEA4 & DEA5 --> DEA6[Enrich with Metadata]
end
subgraph "External Validation Agent"
direction TB
EVA1[Query Google Maps] --> EVA4[Calculate Match Rates]
EVA2[Query OpenStreetMap] --> EVA4
EVA3[Query Waze] --> EVA4
EVA4 --> EVA5[Identify Discrepancies]
EVA5 --> EVA6[Generate Validation Score]
end
subgraph "News Analysis Agent"
direction TB
NAA1[Search Local News] --> NAA2[Filter Road-Related Articles]
NAA2 --> NAA3[Analyze with Gemini Pro]
NAA3 --> NAA4[Extract Relevant Changes]
NAA4 --> NAA5[Calculate News Impact Score]
end
subgraph "Decision Making Agent"
direction TB
DMA1[Combine All Inputs] --> DMA2[Apply Vendor Trust Score]
DMA2 --> DMA3[Calculate Confidence Score]
DMA3 --> DMA4[Generate Reasoning]
DMA4 --> DMA5[Make Recommendation]
end
OUT[Decision Output] --> |Approve/Reject| DB[(Database)]
classDef agent fill:#0066ff,stroke:#333,stroke-width:2px,color:#000
classDef subProcess fill:#00c2ff,stroke:#333,stroke-width:1px,color:#000
classDef data fill:#ff3e9d,stroke:#333,stroke-width:1px,color:#000
class DEA,EVA,NAA,DMA agent
class DEA1,DEA2,DEA3,DEA4,DEA5,DEA6,EVA1,EVA2,EVA3,EVA4,EVA5,EVA6,NAA1,NAA2,NAA3,NAA4,NAA5,DMA1,DMA2,DMA3,DMA4,DMA5 subProcess
class GJ,OUT,DB data
flowchart TD
subgraph "Vendor Trust System"
direction LR
VU[Vendor Upload] --> QA[Quality Assessment]
QA --> |Score| TS[Trust Score Update]
TS --> |Affects| AT[Approval Threshold]
AT --> |Influences| FD[Future Decisions]
FD --> |Feedback| TS
end
subgraph "User Feedback Loop"
direction LR
MD[Merged Data] --> UF[User Feedback]
UF --> |Field Validation| QV[Quality Verification]
QV --> |Reinforcement Signal| TS
end
subgraph "Trust Score Components"
direction TB
HS[Historical Submissions] --> TSC
AR[Approval Rate] --> TSC
DR[Discrepancy Rate] --> TSC
UFB[User Feedback] --> TSC
TSC[Trust Score Calculation] --> TS
end
classDef process fill:#0066ff,stroke:#333,stroke-width:2px,color:#000
classDef data fill:#00c2ff,stroke:#333,stroke-width:1px,color:#000
classDef feedback fill:#ff9e00,stroke:#333,stroke-width:1px,color:#000
class VU,MD data
class QA,TS,AT,FD,TSC process
class UF,QV,UFB feedback
flowchart TD
%% Main flow
Start([Start]) --> VU[Vendor Uploads GeoJSON]
VU --> QC{Quality Check}
QC -->|Pass| MA[Multi-Agent Analysis]
QC -->|Fail| RE[Return to Vendor]
RE --> VU
%% Multi-agent analysis process
MA --> DEA[Data Extraction Agent]
DEA --> EVA[External Validation Agent]
EVA --> NAA[News Analysis Agent]
NAA --> DMA[Decision Making Agent]
DMA --> AR[AI Recommendation]
%% Human review process
AR --> HR{Human Review}
HR -->|Approve| MI[Merge Into Production]
HR -->|Reject| RJ[Reject with Feedback]
RJ --> VU
%% Feedback loop
MI --> PD[Production Database]
PD --> UF[User Feedback Collection]
UF --> QV[Quality Verification]
QV --> TS[Update Vendor Trust Score]
TS --> VTS[(Vendor Trust Score DB)]
VTS -.->|Influences| DMA
%% Data enrichment
MI --> RL[Reinforcement Learning]
RL --> IM[Improve Models]
IM -.->|Enhances| DEA
IM -.->|Enhances| EVA
IM -.->|Enhances| NAA
IM -.->|Enhances| DMA
%% Detailed steps for each agent
subgraph "Data Extraction Process"
DEA1[Parse GeoJSON] --> DEA2[Extract Road Features]
DEA2 --> DEA3[Identify Connectivity]
DEA3 --> DEA4[Calculate Quality Metrics]
end
DEA --- DEA1
subgraph "External Validation Process"
EVA1[Query External Maps] --> EVA2[Compare Features]
EVA2 --> EVA3[Calculate Match Rates]
EVA3 --> EVA4[Identify Discrepancies]
end
EVA --- EVA1
subgraph "News Analysis Process"
NAA1[Search Local News] --> NAA2[Filter Road-Related]
NAA2 --> NAA3[Analyze with LLM]
NAA3 --> NAA4[Calculate Impact Score]
end
NAA --- NAA1
subgraph "Decision Making Process"
DMA1[Combine All Inputs] --> DMA2[Apply Trust Score]
DMA2 --> DMA3[Calculate Confidence]
DMA3 --> DMA4[Generate Reasoning]
end
DMA --- DMA1
%% Styling
classDef process fill:#00c2ff,stroke:#333,stroke-width:1px,color:#000
classDef decision fill:#ff9e00,stroke:#333,stroke-width:1px,color:#000
classDef agent fill:#0066ff,stroke:#333,stroke-width:2px,color:#000
classDef storage fill:#00e5ff,stroke:#333,stroke-width:1px,color:#000
classDef start fill:#ff3e9d,stroke:#333,stroke-width:2px,color:#000
class Start start
class VU,RE,AR,MI,PD,UF,QV,TS,RL,IM process
class QC,HR decision
class DEA,EVA,NAA,DMA agent
class VTS storage
class DEA1,DEA2,DEA3,DEA4,EVA1,EVA2,EVA3,EVA4,NAA1,NAA2,NAA3,NAA4,DMA1,DMA2,DMA3,DMA4 process
- Data Submission: Vendors upload GeoJSON files through the vendor portal
- Initial Quality Check: Basic validation of GeoJSON format and structure
- Multi-Agent Analysis:
- Data Extraction Agent: Processes the raw GeoJSON to identify road segments, intersections, and traffic signals
- External Validation Agent: Cross-references with trusted sources like Google Maps, Waze, and OpenStreetMap
- News Analysis Agent: Checks for relevant road updates, construction, or closures in the area
- Decision Agent: Combines all inputs to make a recommendation with confidence scoring
- Analyst Review: Human analysts review AI recommendations and make final decisions
- Production Integration: Approved data is merged into production systems
- Feedback Loop: User feedback from the companion app validates merged data and updates vendor trust scores
- Continuous Learning: The system uses reinforcement learning to improve agent performance over time
- Map Data Providers: Validate and merge road network updates from multiple sources
- Transportation Departments: Maintain accurate road infrastructure databases
- Navigation Companies: Ensure high-quality road data for routing algorithms
- Urban Planners: Analyze road network changes and plan infrastructure improvements
- Emergency Services: Access the most up-to-date road information for critical response
- Python 3.9+
- Node.js 16+
- pnpm or npm
- SQLite
# Clone the repository
git clone https://github.com/yourusername/roadfusion.git
cd roadfusion
# Set up Python virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
cd backend
pip install -r requirements.txt
# Initialize the database
python create_db.py
# Start the backend server
python app.py# Navigate to the frontend directory
cd frontend
# Install dependencies
pnpm install # or npm install
# Start the development server
pnpm run dev # or npm run devCreate a .env file in the backend directory with the following variables:
FLASK_APP=app.py
FLASK_ENV=development
DATABASE_PATH=database.db
GEMINI_API_KEY=your_gemini_api_key
GOOGLE_MAPS_API_KEY=your_google_maps_api_key
HERE_API_KEY=your_here_api_key
The RoadFusion ecosystem includes a mobile companion app that allows users to submit real-time feedback about road conditions, construction, and map accuracy. This feedback is incorporated into the AI analysis pipeline to improve data quality and validation.
Key features of the mobile app include:
- Real-time road condition reporting
- Photo submission of road issues
- GPS-tagged feedback for precise location mapping
- Integration with the vendor trust scoring system
- Gamification elements to encourage user participation
User feedback serves as a critical reinforcement learning signal that continuously improves the quality of our road data and vendor trust assessments.
We welcome contributions to RoadFusion! Please see our Contributing Guidelines for more information.
This project is licensed under the MIT License - see the LICENSE file for details.
- HERE Technologies for providing mapping APIs and hosting the hackathon
- Google for Gemini Pro API access
- OpenStreetMap community for open geospatial data
- All contributors and testers who helped improve RoadFusion