ProjectBus: Load-Adaptive Acceleration System

SBS Transit Fuel Efficiency Optimization Solution

Problem

Bus drivers lack visibility into passenger load and its impact on fuel consumption. Heavy buses (61+ passengers) driven with aggressive acceleration waste 17% more fuel than gentle acceleration, costing SBS Transit $2.18M annually fleet-wide.

Solution

4-step algorithm system that:

1. Classifies passenger load (Light/Medium/Heavy)
2. Detects acceleration patterns (Gentle/Moderate/Aggressive)
3. Estimates fuel consumption with physics-based penalties
4. Calculates savings potential and provides driver guidance

Key Insight

• Light load + Aggressive acceleration = 2% fuel penalty (negligible)
• Heavy load + Aggressive acceleration = 17% fuel penalty (critical)

Impact

• Route 12: 220L/week savings, $17K/year
• Fleet-wide: $2.18M/year potential
• Uses existing infrastructure (door sensors + GPS)

Project Structure

projectbus-sbs/
├── frontend/          # Figma Make UI prototype (React + Vite)
│   ├── src/          # React components
│   └── public/data/  # Backend-generated JSON
│
└── backend/          # Python algorithms (core logic)
    ├── algorithms/   # 4 core algorithms
    ├── pipeline/     # Data processing
    └── output/       # Generated JSON files

Documentation

• Docs index: docs/README.md
• Architecture and diagrams: docs/ARCHITECTURE.md
• Backend pipeline and algorithms: docs/BACKEND.md
• Frontend UI and data flow: docs/FRONTEND.md
• Data model and JSON outputs: docs/DATA.md
• Runbooks and guides: docs/GUIDES.md

Tech Stack

• Frontend: React + TypeScript + Vite + Tailwind CSS
• Backend: Python 3.x
• Data: JSON (simulated SBS Transit data)

Setup

Frontend (Figma Make UI)

cd frontend
npm install
npm run dev
# Open http://localhost:5173

Backend (Algorithms - Coming Soon)

cd backend
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python process_trips.py

Demo Scenarios

1. Light Load Trip - Efficient (any acceleration OK)
2. Heavy Load Optimal - Gentle acceleration, 0.98 L/km
3. Heavy Load Wasteful - Aggressive acceleration, 1.15 L/km (17% penalty)

Hackathon Context

Built for SBS Transit Hackathon 2025 - Problem Statement 4: Fuel Efficiency Optimization

Team: Matilda, Junming Date: December 2025