Update: 2026-07-06 19:27:17

backend/pricing-engine/src/analysis/regression.ts

@@ -2,6 +2,7 @@ import { PricingTier, RideSample, RegressionResult } from './types';
 import {
   twoStageRegression,
   robustMultipleLinearRegression,
+  simpleDistanceModel,
   calcRMSE,
   calcRSquared,
   detectMinimumFare,
@@ -78,17 +79,29 @@ export function analyzeTier(tier: PricingTier): PricingTier {
       price:        s.price,
     }));
 
-  // Run both models
-  const modelA = twoStageRegression(input);        // Stage 1: flag fall | Stage 2: km + min
-  const modelB = robustMultipleLinearRegression(input);  // Current robust approach
+  // Run all three models
+  const modelA = twoStageRegression(input);              // Stage 1: flag fall | Stage 2: km + min
+  const modelB = robustMultipleLinearRegression(input);  // Iterative outlier removal
+  const modelC = simpleDistanceModel(input);             // distance-only: price = k × dist
 
   const best = selectBestModel(modelA, modelB, samples);
-  if (!best) {
+
+  // Distance-only fallback: if it's within 10% of the best model, prefer it
+  let finalModel = best;
+  if (finalModel && modelC) {
+    const rmseBest = evalRMSE(finalModel.model, samples);
+    const rmseDist = evalRMSE(modelC, samples);
+    if (rmseDist <= rmseBest * 1.10) {
+      finalModel = { model: modelC, name: 'distance-only' };
+    }
+  }
+
+  if (!finalModel) {
     tier.regression = null;
     return tier;
   }
 
-  const { model: mlrResult, name: modelName } = best;
+  const { model: mlrResult, name: modelName } = finalModel;
 
   // Compute final metrics using the winning model
   const actualPrices    = samples.map(s => s.price);