Centralized Logic for Assessing, Understanding & Determining Evaluation
A systematic framework for identifying, classifying, and prioritizing integrated transport hubs (מתח"מים) in Israel.
This framework implements a comprehensive methodology for evaluating multi-modal transit hubs based on:
- Passenger activity (2050 forecasts)
- Service quality (modes and frequencies)
- Strategic location (national and metropolitan importance)
- Development potential (population and employment catchment)
- Bus network integration (terminal proximity)
Results are aggregated using Monte Carlo simulation to ensure no single criterion dominates the final score.
Option 1: Automatic (Recommended)
# Just run the pipeline - it will check and install dependencies
python scripts/run_complete_pipeline.py
# When prompted, type 'y' to install missing packagesOption 2: Install Script
python scripts/install_dependencies.pyOption 3: Manual
pip install -r requirements.txtSee INSTALL.md for detailed installation instructions and troubleshooting.
fromscripts.run_pipelineimportHubPrioritizationPipeline# Initialize pipelinepipeline=HubPrioritizationPipeline()
# Run complete analysisresults=pipeline.run_complete_pipeline(
nodes_csv="data/raw/All_nodes+lines.csv",
lines_modes_csv="data/raw/Lines_and_Planned_Mode.csv"
)
# View top hubsprint(results.nlargest(10, 'final_score'))# Edit file paths in scripts/run_pipeline.py, then run:
python scripts/run_pipeline.pyHubPrioritizing/
├── src/ # Source code (reusable library)
│ ├── config.py # Configuration and constants
│ ├── utils/ # Utilities (logging, constants, encoding)
│ ├── data/ # Data loading, validation, and processors
│ │ ├── loaders.py # Input loading
│ │ ├── validators.py # Data quality checks
│ │ ├── hub_demand_processor.py # 2050 demand matching & aggregation
│ │ └── influence_area_processor.py # Population/employment catchment
│ ├── spatial/ # H3 operations and merging
│ ├── classification/ # Eligibility and hierarchy
│ ├── scoring/ # 5 scoring criteria + Monte Carlo + AHP
│ └── visualization/ # Maps and charts
│
├── scripts/ # Execution scripts
│ ├── run_complete_pipeline.py # Full end-to-end pipeline (demand + demographics)
│ ├── run_pipeline.py # Simplified pipeline (scoring path only)
│ ├── install_dependencies.py # Dependency installer
│ ├── generate_demo_excel.py # Demo scoring workbook generator
│ └── test_ahp_scoring.py # AHP smoke test
│
├── data/ # Data files (raw/processed/results not in git)
│ ├── *_TEMPLATE.csv # Templates for manual inputs & AHP experts
│ └── results/ # Final outputs
│
├── app/ # Streamlit AHP questionnaire app
├── tests/ # Unit tests
├── notebooks/ # Analysis & exploration notebooks
├── docs/ # Documentation (incl. full_documentation/)
│
├── requirements.txt # Python dependencies
├── INSTALL.md # Installation guide
├── AHP_QUICKSTART.md # AHP feature quick-start
├── CLAUDE.md # Full framework specification
└── README.md # This file
- Load Transit Data: Import nodes, lines, and modes
- Create H3 Hexagons: Assign hexagonal spatial indices (~15m resolution)
- Group Hexagons: Merge nearby hexagons (120m threshold) into hub areas
- Filter Eligibility: Keep hubs with ≥1,000 passengers/day and ≥2 mass-transit modes
- Classify Hierarchy: Assign tiers (ארצי/מטרופוליני/עירוני) based on ridership
- Calculate Scores: Compute 5 scoring criteria normalized to 1-10 scale
- Aggregate Scores: Monte Carlo simulation (10,000 iterations) for final ranking
| Tier | Hebrew | Ridership | Description |
|---|---|---|---|
| National | ארצי | ≥50,000/day | Top-tier hubs connecting metropolitan regions |
| Metropolitan | מטרופוליני | 5,000-50,000/day | Mid-level nodes linking trunk lines to feeders |
| Local | עירוני | <5,000/day | Neighborhood gateways to the transit network |
| # | Criterion | Normalization |
|---|---|---|
| 1 | Passenger Activity (log₁₀ + min-max) | Per tier |
| 2 | Service & Modes (mode weights × √lines × diversity bonus) | Per tier |
| 3 | Location (region × metro position) | Global |
| 4 | Population & Jobs (2050 catchment with distance decay) | Per tier |
| 5 | Bus Terminal (200m proximity × terminal weight) | Global |
Important Notes:
- Per-tier normalization: All hubs of the same tier (ארצי/מטרופוליני/עירוני) are normalized together, regardless of geographic area
- Monte Carlo: Simulation runs on all hubs together (single simulation across entire dataset)
- Ranking: National hubs ranked globally; Metropolitan/Local hubs ranked within their geographic area
All parameters are centralized in src/config.py:
# Key thresholdsELIGIBILITY_MIN_PASSENGERS=1000# Minimum daily passengersELIGIBILITY_MIN_MODES=2# Minimum mass-transit modes# Hierarchy thresholdsNATIONAL_HUB_MIN_PASSENGERS=50000METRO_HUB_MIN_PASSENGERS=5000# Spatial parametersH3_RESOLUTION=10# ~15m hexagonsHUB_MERGE_THRESHOLD_M=120# Edge-to-edge grouping distance# ScoringMONTE_CARLO_ITERATIONS=10000# Simulation iterationsMAX_CRITERION_WEIGHT=0.5# Max weight per criterion (50%)Transit Nodes (
All_nodes+lines.csv)- Columns:
node,LINE_ID,X,Y(orgeometry) - Format: CSV with Israel TM Grid coordinates (EPSG:2039)
- Columns:
Lines and Modes (
Lines_and_Planned_Mode.csv)- Columns:
Line_ModelName,Mode_Planned,Area - Maps transit lines to their planned mode
- Columns:
Demand Data (optional, Excel file)
- 2050 passenger forecasts by station
- Sheets for each regional model (Haifa, TelAviv, Jerusalem, etc.)
Spatial Layers (optional shapefiles)
- Metro areas
- Administrative districts
- TAZ zones with POP_2050 and EMPL_2050
- Bus terminals
hub_prioritization_results_{timestamp}.csv- Full results with all scoreshub_prioritization_results_{timestamp}.geojson- Spatial data for mappinghub_map_{timestamp}.html- Interactive web map
fromscripts.run_pipelineimportHubPrioritizationPipelinepipeline=HubPrioritizationPipeline()
# Run individual stepspipeline.step_1_load_transit_data(nodes_csv, lines_csv)
pipeline.step_2_create_h3_hexagons()
pipeline.step_3_group_hexagons()
# ... continue as needed# Access intermediate resultshubs=pipeline.grouped_hubsfromsrc.scoringimportmonte_carlo# Calculate all scoresscored_hubs=monte_carlo.run_complete_scoring_pipeline(
hubs_gdf,
tier_column='tier'
)
# View top 20top_20=scored_hubs.nlargest(20, 'final_score')
print(top_20[['group', 'tier', 'final_score', 'rank']])fromsrc.visualizationimportmaps# Create interactive mapmaps.create_hub_map(
scored_hubs,
color_by='final_score',
output_file='results/my_map.html'
)# Run all tests
pytest tests/
# With coverage
pytest tests/ --cov=src --cov-report=htmlThis codebase has been reviewed for adherence to SOLID design principles and software engineering best practices. See docs/EXECUTIVE_SUMMARY.md for the complete assessment.
- ✅ Excellent module organization - Clear separation of concerns
- ✅ Well-documented methodology - Comprehensive technical documentation
- ✅ Centralized configuration - All parameters in one place
- ✅ Strong data validation - Early error detection
- Single Responsibility: Each module has one clear purpose
- Functional Design: Composable, testable functions
- Configuration-Driven: Behavior controlled by
config.py - Reproducible: Fixed random seeds, version-controlled parameters
The framework demonstrates strong engineering practices and is production-ready. See the SOLID review for detailed recommendations to enhance extensibility and testability
CLAUDE.md - Complete framework specification and methodology
- Domain context (What is a מתח"מ?)
- Hub hierarchy definitions
- Detailed scoring methodology
- Data requirements
- Development guidelines
- AI assistant instructions
INSTALL.md - Installation guide and troubleshooting
AHP_QUICKSTART.md - Quick-start for the optional AHP expert-weighting workflow
docs/full_documentation/ - End-to-end documentation (overview, inputs, pipeline, step-by-step, scoring, outputs)
- docs/PROJECT_EXECUTIVE_SUMMARY.md - Project-level executive summary (methodology depth and decision rationale)
- docs/AHP_SCORING_GUIDE.md - AHP scoring methodology
- docs/SCORING_CRITERIA_EXECUTIVE_SUMMARY.md - Scoring criteria overview
- docs/DEMO_SCORING_WALKTHROUGH.md - Worked scoring example
- docs/EXECUTIVE_SUMMARY.md - Code quality / SOLID review executive summary
- docs/SOLID_PRINCIPLES_REVIEW.md - Detailed SOLID principles review
- docs/DATA_CONFIGURATION.md - Data file configuration guide
- data/README_MANUAL_DEMAND_UPDATES.md - Demand data update procedures
- data/README_MODE_LINE_COLUMNS.md - Mode and line column descriptions
This is a proprietary planning framework for Israeli transport infrastructure. It is not open to external contribution. For access or collaboration requests, contact the author (see below).
PROPRIETARY AND CONFIDENTIAL — Copyright © 2026 Ohad Dahan. All Rights Reserved.
This software and all associated materials are proprietary and confidential. Unauthorized copying, distribution, modification, or use, in whole or in part, is strictly prohibited without prior express written permission. See the LICENSE file for full terms.
Author: Ohad Dahan Email:ohad@ayalonhw.co.il
For technical questions about the framework, see CLAUDE.md and the full documentation.