Evaluating eVTOL Network Performance and Fleet Dynamics through Simulation-Based Analysis

TL;DR

This paper introduces VertiSim, an open-source, event-driven simulator that jointly models passenger, aircraft, and energy flows to evaluate e-VTOL networks for Urban Air Mobility. By applying VertiSim to 19 two-vertiport scenarios with varying fleet sizes and vertiport distances, the authors quantify how fleet size and infrastructure configuration influence passenger delay, energy consumption, and fleet utilization under a heuristic dispatch and charge policy. Key contributions include a detailed software architecture with nine modules, a battery sizing and charging model informed by existing eVTOL and EV data, and a theoretical framework for fleet sizing that accounts for operational time and demand-driven repositioning. The findings highlight the critical role of charging dynamics and fleet repositioning in planning UAM networks, offering actionable insights for fleet sizing, vertiport design, and policy development to balance service quality with operating costs. VertiSim thus provides a versatile tool for exploring complex, energy-aware UAM operations and supports future work on more complex networks, scheduled operations, and weather effects to enhance planning realism.

Abstract

Urban Air Mobility (UAM) represents a promising solution for future transportation. In this study, we introduce VertiSim, an advanced event-driven simulator developed to evaluate e-VTOL transportation networks. Uniquely, VertiSim simultaneously models passenger, aircraft, and energy flows, reflecting the interrelated complexities of UAM systems. We utilized VertiSim to assess 19 operational scenarios serving a daily demand for 2,834 passengers with varying fleet sizes and vertiport distances. The study aims to support stakeholders in making informed decisions about fleet size, network design, and infrastructure development by understanding tradeoffs in passenger delay time, operational costs, and fleet utilization. Our simulations, guided by a heuristic dispatch and charge policy, indicate that fleet size significantly influences passenger delay and energy consumption within UAM networks. We find that increasing the fleet size can reduce average passenger delays, but this comes at the cost of higher operational expenses due to an increase in the number of repositioning flights. Additionally, our analysis highlights how vertiport distances impact fleet utilization: longer distances result in reduced total idle time and increased cruise and charge times, leading to more efficient fleet utilization but also longer passenger delays. These findings are important for UAM network planning, especially in balancing fleet size with vertiport capacity and operational costs. Simulator demo is available at: https://tinyurl.com/vertisim-vis

Figures (16)

• Figure 1: Building blocks of VertiSim
• Figure 2: VertiSim Software Architecture
• Figure 3: Clover type vertiport layout (FSA: Final Safety Area, FATO: Final Approach and Takeoff Area)
• Figure 4: Vertiport operations
• Figure 5: Scaled-down 24-hour demand between Eastbound and Westbound at the San Francisco Bay Bridge