Replication Data for: Bridging the Public-Private Transport Discomfort Gap

This repository strictly provides the codebase and the optimization models)._ **Contents of the Repository** The codebase is structured to handle the following analytical steps: - **Differential Generalized Discomfort Metric:** Python scripts designed to compute the generalized cost gap between private trips and PT alternatives based on travel phases. - **Propensity Model Calibration:** Code implementing the non-linear regression to fit an empirical Sigmoid mode-shift propensity curve based on Random Utility Theory. - **Priority Corridors Extraction (H-index):** Algorithms to calculate the _H-index_, a spatial-temporal metric used to prioritize critical urban corridors where transport supply fails to meet demand. - **What-If Optimization Framework (Genetic Algorithm):** The core Python codebase implementing a modular decision model.

The Genetic Algorithm simulates the application of four operational levers: stop optimization, frequency adjustments, commercial speed upgrades, and schedule synchronization. **Interpretation and Usage** Researchers and transport planners can utilize this modular Python framework to deploy the proposed Decision Support System (DSS) in other urban contexts. By inputting their own local GTFS feeds and mobility demand data, users can replicate the methodology to evaluate transit competitiveness and simulate targeted optimization scenarios.