A Data-Driven Simulation Approach to Taxi Pricing, Profitability, and Operational Performance

This research presents a data-driven simulation framework to evaluate taxi pricing, profitability, and operational performance. Unlike traditional statistical methods, it captures idle time, idle distance, and real-time request matching, providing more accurate profitability estimates. Using the New York City Taxi and Limousine Commission (TLC) dataset, the framework simulates various pricing models and fleet sizes, demonstrating its effectiveness through a comparative analysis of ICE and EV taxis. Results highlight the advantages of simulation-based analysis, offering actionable insights for fleet operators and policymakers to improve urban mobility, efficiency, and financial sustainability.