Skip to content

README.md

Latest commit

 

History

History
30 lines (22 loc) · 1.27 KB

README.md

File metadata and controls

30 lines (22 loc) · 1.27 KB

SmartBus-AI

SmartBus-AI allocates bus seats using a MILP optimizer (hard constraints + strong group cohesion) enhanced by ML signals:

  • Global models (trained offline from backend’s feedback data)
  • Personalized runtime models (tiny per-passenger regressors using their own history)

This repo exposes a FastAPI service (/allocate) and includes an offline trainer to build global models. All data persistence is handled by backend; this service is stateless.

Architecture

Optimizer (MILP)

  • One seat per passenger.
  • No seat reuse when passenger trip intervals overlap.
  • Safety zones:
    • Male passengers cannot sit in female-only rows.
    • Non-priority (not disabled & under 60) cannot sit in accessible rows.
    • Wheelchair users must be in accessible rows.
  • Objective: strong group cohesion (horizontal > vertical), with optional ML bonuses:
    • Global seat type preference (front/window/aisle).
    • Global penalty for group separation distance.
    • Personalized bonus if the passenger historically rates similar seat locations higher.

ML

  • Offline global models (trained with src/train_global_models.py) — saved into models/.
  • Runtime personalization per passenger (if history available from backend).