Data-driven simulation-based planning for electric airport shuttle systems: A real-world case study

Many airports are adopting battery electric buses in their shuttle fleets due to concerns over air quality and regulations. This study proposes a simulation-based optimization modeling framework to help airport shuttle operators effectively deploy electric buses. We evaluated a planned airport electric shuttle system with an event -driven simulator. Empirical data collected from existing systems were used to drive the simulations. We then proposed a simulation-based optimization model to determine the battery capacity, charging power, and number of chargers so that predefined objective(s) (e.g., minimizing total capital cost, minimizing emissions) are opti-mized. Compared to existing studies, the primary contribution of the proposed method is that it can model the real-world stochastic nature of operations in an electric bus system with much higher fidelity. To demonstrate the proposed modeling framework, we study a real-world shuttle system at the Dallas-Fort Worth International Airport, and present extensive numerical studies. When considering partial fleet electrification, the model can provide a set of Pareto optimal solutions. When considering full fleet electrification, the optimal solution requires a 50-kWh battery capacity and four 210-kW chargers, resulting in a total capital cost of $26,744,000. The results demonstrate that the proposed modeling framework can effectively optimize the planning of electric airport shuttle systems with partial or full fleet electrification.

Automated summary

Many airports are adopting battery electric buses in their shuttle fleets to address concerns over air quality and regulations. This study presents a simulation-based optimization modeling framework to assist airport shuttle operators in deploying electric buses efficiently. By using empirical data and a simulation-driven approach, the model determines the optimal battery capacity, charging power, and number of chargers to minimize capital costs and emissions. The proposed framework demonstrates its effectiveness through extensive numerical studies at the Dallas-Fort Worth International Airport.