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Scoring Calculations Fix Summary

Issues Fixed

Three critical scoring calculation issues have been resolved in hub_demand_processor.py:

1. Num_Modes - All showing 0

Problem:

  • Calculated as len(Mode_Planned) which counts modes in the list
  • Did not account for whether those modes actually have lines

Solution:

  • Now counts mode-specific line columns (e.g., 'BRT Lines', 'Metro Lines', etc.) that have values > 0
  • Uses count_positive_mode_lines() function
  • Example: If hub has 2 in 'BRT Lines' and 3 in 'LRT Lines', Num_Modes = 2

Code Location: hub_demand_processor.py:536-542, 595

2. score - All showing 0 (or not calculated)

Problem:

  • Mode service score was not being calculated at all in the hub_demand_processor

Solution:

  • Now calculates weighted score based on:
    • Line count for each mode
    • Mode weight (from CLAUDE.md specification)
    • Diversity bonus (10% per additional mode)
  • Formula: score = Σ(line_count × mode_weight) × (1 + 0.1 × (Num_Modes - 1))
  • Mode weights:
    • HighSpeed Rail: 8.0
    • Rail/Interurban Rail: 7.0
    • Suburban Rail: 6.0
    • Metro: 5.0
    • LRT: 4.0
    • BRT: 3.0
    • Cable Line/Bus: 2.0
    • Funicular: 1.0

Code Location: hub_demand_processor.py:514-561, 598

3. RegionLocation - All showing 1

Problem:

  • RegionLocation was not being calculated, or was using incorrect logic
  • Should be Region_category × Location_category

Solution:

  • Now properly calculates:
    • Region_category: 0 for Tel Aviv/Center, 1 for periphery
    • Location_category: 3 for Core (גלעין), 2 for Ring (טבעת), 1 for other
    • RegionLocation: Product of the two
  • Checks 'area' column for region determination
  • Checks 'location' column for metropolitan position
  • Example: Tel Aviv Core = 0 × 3 = 0, Haifa Ring = 1 × 2 = 2

Code Location: hub_demand_processor.py:563-612

Files Modified

  1. hub_demand_processor.py

    • Added MODE_WEIGHTS dictionary (lines 514-527)
    • Added MODE_LINE_COLS list (lines 529-534)
    • Added count_positive_mode_lines() function (lines 536-542)
    • Added calculate_mode_score() function (lines 544-561)
    • Added get_region_category() function (lines 563-575)
    • Added get_location_category() function (lines 577-592)
    • Added calculations for all three scoring columns (lines 595-612)
    • Added debug output (lines 614-619)
    • Added 'location' to spatial columns aggregation (line 501)

  2. scripts/test_scoring_fixes.py (NEW)

    • Test script to validate calculations
    • Can be run to verify all three fixes work correctly

How to Verify the Fixes

Option 1: Run the Test Script

cd /home/user/HubPrioritizing
python scripts/test_scoring_fixes.py

Expected output: All tests should pass with ✓ marks

Option 2: Re-process Your Data

  1. Re-run the pipeline with your actual data
  2. Check the debug output during processing: 
    ✓ Calculated scoring columns:
   Num_Modes: min=1, max=4, mean=2.15
   score: min=3.00, max=45.50, mean=18.23
   RegionLocation: min=0, max=3, mean=1.35
   Hubs by Num_Modes: {1: 45, 2: 78, 3: 32, 4: 5}
  3. Inspect the output CSV to verify:
    • Num_Modes has values > 0 for hubs with multiple modes
    • score has varied values reflecting mode diversity and line counts
    • RegionLocation has varied values (0, 1, 2, or 3) based on location

Option 3: Manual Spot Check

Pick a few hubs from your data and manually verify:

Example Hub:

  • BRT Lines: 2
  • Metro Lines: 1
  • All other modes: 0

Expected Values:

  • Num_Modes: 2 (two mode-specific columns > 0)
  • score: (2 × 3) + (1 × 5) = 11, then 11 × (1 + 0.1 × 1) = 12.1
  • RegionLocation: Depends on 'area' and 'location' columns

Expected Impact on Results

After applying these fixes, you should see:

  1. Num_Modes distribution:

    • Most hubs: 1-3 modes
    • Large interchange hubs: 3-5 modes
    • No more "all zeros"

  2. score distribution:

    • Range: approximately 3.0 to 60.0
    • Higher scores for hubs with:
      • More lines
      • Higher-weight modes (Rail, Metro)
      • Greater mode diversity
    • No more "all zeros"

  3. RegionLocation distribution:

    • Tel Aviv Core: 0 (0 × 3)
    • Tel Aviv Ring: 0 (0 × 2)
    • Periphery Core: 3 (1 × 3)
    • Periphery Ring: 2 (1 × 2)
    • Periphery Other: 1 (1 × 1)
    • No more "all ones"

  4. score_norm (downstream impact):

    • Will now vary properly (1-10 scale per hub type)
    • No more "all 5.5" due to all zeros in input

Dependencies

The fix requires these columns to be present in the input data:

Required:

  • Mode-specific line columns: 'BRT Lines', 'LRT Lines', 'Metro Lines', etc.

Optional (but recommended):

  • 'area': For regional categorization (default: periphery if missing)
  • 'location': For metro position categorization (default: periphery if missing)

If these columns are missing, the calculations will use defaults but won't be fully accurate.

Next Steps

  1. ✅ Fixes have been committed and pushed to branch claude/fix-scoring-calculations-fOnHv
  2. 📋 Re-run your data processing pipeline to generate updated results
  3. 🔍 Verify the output has varied values for Num_Modes, score, and RegionLocation
  4. 📊 Compare before/after results to see the impact on final rankings

Questions or Issues?

If you encounter any problems:

  1. Check that your input data has the mode-specific line columns
  2. Verify 'area' and 'location' columns exist and have proper values
  3. Run the test script to validate the calculation logic
  4. Check the debug output during processing for statistics

The fixes follow the methodology specified in CLAUDE.md and match the notebook logic from complete_hub_scoring_pipeline.ipynb.