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Throughput Maximizing Takeoff Scheduling for eVTOL Vehicles in On-Demand Urban Air Mobility Systems

Milad Pooladsanj, Ketan Savla, Petros A. Ioannou

TL;DR

The paper addresses efficient, conflict-free takeoff scheduling for on-demand urban air mobility with eVTOLs by introducing VertiSync, a cycle-based centralized policy that jointly handles trip servicing and vehicle rebalancing under safety and energy constraints. It develops a service-vector framework to characterize system throughput, proving that VertiSync achieves near-optimal throughput for large fleets and symmetric networks, and it provides both inner and outer bounds on throughput. Through a Los Angeles case study and larger-scale simulations, the authors show substantial reductions in passenger waiting times compared to FCFS, and they demonstrate computational feasibility for large problem sizes. The work advances urban air traffic management by integrating rebalancing, safety margins, and energy considerations into a unified, throughput-focused scheduling framework, with clear directions for scalability enhancements and high-fidelity validation.

Abstract

Urban Air Mobility (UAM) offers a solution to current traffic congestion by using electric Vertical Takeoff and Landing (eVTOL) vehicles to provide on-demand air mobility in urban areas. Effective traffic management is crucial for efficient operation of UAM systems, especially for high-demand scenarios. In this paper, we present a centralized framework for conflict-free takeoff scheduling of eVTOLs in on-demand UAM systems. Specifically, we provide a scheduling policy, called VertiSync, which jointly schedules UAM vehicles for servicing trip requests and rebalancing, subject to safety margins and energy requirements. We characterize the system-level throughput of VertiSync, which determines the demand threshold at which the average waiting time transitions from being stable to being increasing over time. We show that the proposed policy maximizes throughput for sufficiently large fleet size and if the UAM network has a certain symmetry property. We demonstrate the performance of VertiSync through a case study for the city of Los Angeles, and show that it significantly reduces average passenger waiting time compared to a first-come first-serve scheduling policy.

Throughput Maximizing Takeoff Scheduling for eVTOL Vehicles in On-Demand Urban Air Mobility Systems

TL;DR

The paper addresses efficient, conflict-free takeoff scheduling for on-demand urban air mobility with eVTOLs by introducing VertiSync, a cycle-based centralized policy that jointly handles trip servicing and vehicle rebalancing under safety and energy constraints. It develops a service-vector framework to characterize system throughput, proving that VertiSync achieves near-optimal throughput for large fleets and symmetric networks, and it provides both inner and outer bounds on throughput. Through a Los Angeles case study and larger-scale simulations, the authors show substantial reductions in passenger waiting times compared to FCFS, and they demonstrate computational feasibility for large problem sizes. The work advances urban air traffic management by integrating rebalancing, safety margins, and energy considerations into a unified, throughput-focused scheduling framework, with clear directions for scalability enhancements and high-fidelity validation.

Abstract

Urban Air Mobility (UAM) offers a solution to current traffic congestion by using electric Vertical Takeoff and Landing (eVTOL) vehicles to provide on-demand air mobility in urban areas. Effective traffic management is crucial for efficient operation of UAM systems, especially for high-demand scenarios. In this paper, we present a centralized framework for conflict-free takeoff scheduling of eVTOLs in on-demand UAM systems. Specifically, we provide a scheduling policy, called VertiSync, which jointly schedules UAM vehicles for servicing trip requests and rebalancing, subject to safety margins and energy requirements. We characterize the system-level throughput of VertiSync, which determines the demand threshold at which the average waiting time transitions from being stable to being increasing over time. We show that the proposed policy maximizes throughput for sufficiently large fleet size and if the UAM network has a certain symmetry property. We demonstrate the performance of VertiSync through a case study for the city of Los Angeles, and show that it significantly reduces average passenger waiting time compared to a first-come first-serve scheduling policy.

Paper Structure

This paper contains 17 sections, 3 theorems, 32 equations, 12 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. UAM Network Structure
  4. Operational Constraints
  5. Demand and Performance Metric
  6. Network-Wide Scheduling
  7. VertiSync Policy
  8. Size of VertiSync Formulation
  9. VertiSync Throughput
  10. Fundamental Limit on Throughput
  11. Simulation Results
  12. Comparison of Theoretical Bounds
  13. Comparison with First-Come First-Serve Policy
  14. Computation Results
  15. Conclusion
  16. ...and 2 more sections

Key Result

Theorem 1

If the UAM network satisfies the reversibility Assumption assumption:reversibility, and the number of UAM vehicles satisfies then the VertiSync policy can keep the network under-saturated for demands belonging to the set where and the vector inequality $\lambda_{} < \sum_{i = 1}^{|R|}r_{i}^{} x_i/(1+c_i)$ is considered component-wise.

Figures (12)

  • Figure 1: A top-view sketch of a UAM network with three modes of UAM vehicle operation: idle vehicle (red), in-service vehicle that transports passengers (black), and rebalancing vehicle that flies without passengers to high-demand areas (purple).
  • Figure 2: A top-view sketch of a UAM network with $|V_{}|=4$ vertiports (blue circles) and $|P|=8$ O-D pairs $P = \{(1,3), (1,4), (2,3), (2,4), (3,1), (4,2), (1,2), (2,1)\}$.
  • Figure 3: Sector configuration for a UAM network, with sector capacity of $1$ vehicle, i.e., at most $1$ UAM vehicle can occupy any sector at any time. Moreover, if a UAM vehicle occupies sector A, then it moves to sector B after one time step.
  • Figure 4: An illustration of the under-saturation region of some policy $\pi'$ (dark grey area) and a throughput maximizing policy $\pi$ (dark + light grey areas).
  • Figure 5: Sector configuration for a UAM network. The green sector belongs to routes $(1,3), (1,4), (2,3), (2,4)$.
  • ...and 7 more figures

Theorems & Definitions (18)

  • Remark 1
  • Example 1
  • Definition 1
  • Remark 2
  • Remark 3
  • Remark 4
  • Example 2
  • Example 3
  • Example 4
  • Theorem 1
  • ...and 8 more