Intelligent Transportation Simulation Model

I am working on my PhD research in Intelligent Transportation Systems, and I need to develop a simulation model based on the clustered dataset I will provide. The goal is to evaluate the impact of targeted financial incentives on vanpooling matching success and user satisfaction. Please develop the simulation to execute the following tasks: 1. Sensitivity Analysis: Simulate the system across a range of incentive percentages: (0%, 10%, 20%, 30%, 40%, 50%). Objective: Identify the Optimal Tipping Point—the incentive percentage that maximizes the matching ratio without unnecessary financial overspending. Output: Generate a sensitivity curve (Incentive % vs. Matching Ratio) to visualize this impact. 2. KPI Comparison (Baseline vs. Optimal Policy): Run the simulation for two scenarios: Baseline: 0% incentive. Optimal Policy: Use the 'optimal percentage' identified in the sensitivity analysis. Required KPIs for both scenarios: Matching Ratio: Total number of riders successfully matched. Average Satisfaction: Calculated per rider profile (5 profiles in total). Equity Metric (Range Gap): The difference between the highest and lowest average satisfaction scores among profiles (to ensure fairness). Total Subsidy Cost: The total financial outlay required to implement the incentive. 3. Required Deliverables: Comparison Table: A summary table presenting all the KPIs for the Baseline vs. the Optimal scenario. Visualizations: A sensitivity curve plot. A bar chart comparing satisfaction levels per profile before and after the incentive. Code Documentation: The code should be written in Python (using optimization libraries like PuLP, Pyomo, or SciPy). Please ensure the code is well-commented so I can explain the logic to my supervisor. Technical Note: My dataset includes 5 distinct user clusters.

Project ID: 40547327