MargaDrishti — Models
Trained artefacts for MargaDrishti, a spatio-temporal ML system over Bengaluru parking-violation and traffic-event data (Nov 2023 – Apr 2024).
Live dashboard: adarshcod30/margadrishti
Read this before using the numbers
These models rank predicted enforcement demand, not where illegal parking
occurs. corr(log patrol_hours, log total_captures) = 0.96 across all 2,534
res-9 cells — about 93% of the
variance in per-cell violation counts is explained by how much patrolling
happened there. The project's own
enforcement-bias audit
returns HIGH concern on this system, and that is published rather than
buried: no Getis-Ord hotspot in the data survives exposure adjustment.
Two targets are not what a leaderboard row suggests:
- B3 (priority) is a recovered rule, not a prediction. Corridor status predicts High priority at 99.84% (13 exceptions in 8,173 events). Scores near 1.0 reflect that rule.
- A3 (capture rejection) is not yet answerable. The review process changed regime mid-window — February 2024 rejection jumped to 0.790 from ~0.28 while volume collapsed sixfold — so every family is miscalibrated.
What's here
| File | What it is |
|---|---|
onnx/b1_clearance_duration.onnx | Event clearance duration (log1p minutes), RandomForest |
onnx/b2_road_closure.onnx | Road-closure probability, RandomForest |
onnx/*.features.json | Exact input feature order — ONNX takes a bare float array with no column names |
leaderboard.csv | Module A: every family × A1/A2/A3 |
leaderboard_module_b.csv | Module B: every family × B1–B4 |
model_cards/ | One card per target, generated from the leaderboards |
reports/ | Spatial statistics, CIS validation, enrichment ablation, fairness audit |
Why RandomForest and not the leaderboard winner
The browser ships RandomForest because onnxmltools misroutes ~1% of LightGBM rows at split boundaries — median divergence is float32 noise (4.3e-07) but 5 of 500 sample rows diverged by up to 0.09. skl2onnx converts RandomForest exactly (max 1.0e-06; the classifier is bit-exact).
That trade is only defensible because the Module B leaderboard already put random_forest at 0.5034 against xgboost's 0.4990 — a gap far inside the ±0.13 fold standard deviation. Exact client/server agreement costs no measurable accuracy.
Headline results
| Target | Best family | Metric | vs strongest baseline |
|---|---|---|---|
| A1 · parking intensity | xgboost | 0.4999 Poisson deviance | +43.0% |
| A2 · hotspot | random forest | 0.1365 PR-AUC | 46.9× base rate (0.291%) |
| A3 · capture rejection | xgboost | 0.3088 PR-AUC | not answerable |
| B1 · clearance duration | xgboost | 0.4990 ± 0.128 | +27.7% |
| B2 · road closure | catboost | 0.3615 ± 0.071 PR-AUC | 4.1× (8.7% prevalence) |
| B4 · cause from text | char n-gram + logreg | 0.478 macro-F1 | 10.8× |
B4's notable result is equity rather than accuracy: English 0.483 vs Kannada 0.472, essentially no language gap on a field that mixes both scripts.
Reproduce
git clone https://github.com/adarshcod30/MargaDrishti
cd MargaDrishti && make data && make reproduce
Seed 42. Splits are chronological, never random.
Licence
MIT. Source data are anonymised public releases and remain subject to their original terms.