Benchmarking dataset for the Optimal Stop Location and Spacing for a Public Transport Corridor

The repository contains three main types of data: processed data (P), real data (R), and simulated data (S). Each dataset follows a structured naming convention that indicates its type, the experiment it belongs to, and the specific scenario within that experiment. Real data (R) consists of actual operational data provided by Transmetro S.A., reflecting the conditions of the public transport system in Barranquilla. These data are essential for accurately modeling the constraints and performance of the transport system. Processed data (P) includes structured datasets generated from the case study, used to construct key sets and parameters in the optimization model. Simulated data (S) includes artificially generated datasets designed to test different demand scenarios and model sensitivities. These simulated datasets allow for the evaluation of various hypothetical scenarios to assess the robustness of the optimization model under different operational conditions. Each file in the repository follows a specific naming format: [Type]-[Experiment]_[Scenario], where Type refers to P (Processed), R (Real), or S (Simulated), followed by the experiment (E1, E2, E3, etc.) and the scenario (S1, S2, S3, etc.). For example, P-E1_S1 corresponds to processed data for scenario 1 of experiment 1, R-E2_S3 refers to real data for scenario 3 of experiment 2, and S-E3_S5 denotes simulated data for scenario 5 of experiment 3. Specifically, for Scenario 4 in Experiment 3, the instance name also indicates the increase in utility value used in the simulation. In these cases, the utility value is scaled by a factor of x20, x100, or x10,000, allowing for sensitivity analysis on the impact of marginal utility changes.