Build notes · Goh Kun Ming
Trip Safety Prediction System
AI · Public project
Jan 2026 – Feb 2026
Project overview
An offline trip-classification application that distinguishes model-backed batch predictions from single-record heuristic outputs.
An offline Windows application that converts vehicle sensor records into trip-level safety classes using a bundled decision-tree pipeline. Batch predictions use a defined feature contract, while single-record outputs are clearly labelled as heuristic fallbacks. Reproducible inference is demonstrated; held-out predictive quality is not established by the committed material.
Inside this build
- Validated missing-value markers, GPS accuracy, bearings and required numeric fields.
- Derived 19 trip-level features covering duration, movement, speed and sensor quality.
- Produced model-backed SAFE or DANGEROUS batch outputs with inference-source labels.
- Separated full-trip aggregation from single-record heuristic mode.
- Packaged the model and interface assets into a Windows executable.
- Checked cleaning, aggregation, persistence, desktop flows and packaged inference through automated tests.