Taking Flight: Data-Driven eVTOL Fleet Optimization
My previous blog post, "Ideal Take-off and Landing Spots for eVTOLs," explored the crucial first step in realizing the potential of Urban Air Mobility (UAM): identifying optimal locations for vertiports. We delved into the factors influencing vertiport placement, considering aspects like proximity to demand centers, airspace regulations, and noise impact. This foundational work highlighted the importance of strategic infrastructure planning for the successful integration of electric vertical takeoff and landing (eVTOL) aircraft into our urban transportation ecosystems. This post builds upon that foundation, taking the next leap by leveraging crowdsourced mobility data processed by CITYFLOW to quantify terrestrial traffic demand and subsequently estimate the required eVTOL fleet size for a network of strategically positioned vertiports.
The promise of UAM is immense. Imagine a future where congested roadways are bypassed by efficient, quiet, and emission-free eVTOLs, drastically reducing commute times and improving urban mobility. This vision is rapidly moving closer to reality, with advancements in eVTOL technology, battery performance, and air traffic management systems. However, realizing this potential requires more than just technological innovation. It demands careful planning and optimization of the entire UAM ecosystem, from vertiport networks to fleet sizing and operational strategies.
Quantifying Terrestrial Demand with CITYFLOW:
The key to effective UAM integration lies in understanding existing travel patterns. CITYFLOW provides granular insights into urban mobility by processing anonymized crowdsourced GPS data. This allows us to quantify traffic counts for each road segment, effectively establishing the terrestrial demand for transport. By analyzing trip origins and destinations within CITYFLOW's mobility dataset, we can infer a network of potential vertiport locations, strategically positioned to intercept and augment existing ground transportation. This data-driven approach moves beyond theoretical placements and grounds our vertiport network in real-world travel patterns, maximizing the potential for UAM adoption.
Estimating Terrestrial to Aerial Demand Conversion:
A crucial, and complex, step is determining what percentage of terrestrial demand might realistically convert to UAM. This conversion rate is a key variable in our analysis and represents a significant area for future research. While the actual percentage will depend on a complex interplay of factors like cost, perceived convenience, public acceptance, regulatory frameworks, and integration with existing transportation modes, we can explore scenarios ranging from a conservative 1% to a more optimistic 15% conversion. This range allows for sensitivity analysis, providing a more comprehensive understanding of potential fleet requirements and acknowledging the inherent uncertainties in predicting future travel behavior. Research by Oliver Wyman, for example, has explored potential market sizes and adoption rates for UAM, highlighting the importance of factors like price point and perceived value proposition. Further studies, including surveys and pilot programs, will be essential to refine these estimates.
A Multi-faceted Fleet Sizing Methodology:
Once we have an estimate of aerial demand between vertiports, we can determine the necessary eVTOL fleet size. This requires considering several aircraft characteristics:
- Passenger Capacity: The number of passengers each eVTOL can carry.
- Average Speed: The typical cruise speed of the aircraft.
- Average Range: The maximum distance the aircraft can travel without recharging.
- Charging Time: The duration required to fully recharge the aircraft's batteries.
- Loading/Unloading Time: The time taken for passengers to board and disembark.
- Cycle Time: The total time for a complete trip, including flight, charging, and loading/unloading.
With these parameters, we can model the operational efficiency of each eVTOL and estimate the number of aircraft needed to meet the aerial demand between any two vertiports. This calculation considers factors like peak demand periods, aircraft utilization rates, and turnaround times. This process is similar to methodologies used in traditional airline fleet planning, but with the added complexity of battery charging and vertiport infrastructure constraints. Existing research, such as studies on dynamic fleet management for on-demand air mobility, provides valuable insights into optimization algorithms and dispatch strategies.
Sensitivity Analysis and Scenario Planning:
By varying the percentage of terrestrial demand converting to UAM, we can conduct fleet size sensitivity analyses. This helps us understand the impact of different adoption rates on the overall fleet requirements. For example, a 1% conversion might necessitate a significantly smaller fleet compared to a 15% conversion. This analysis is crucial for planning and investment decisions, allowing stakeholders to prepare for a range of potential market scenarios. Furthermore, we can explore scenarios with different vertiport network configurations and aircraft types to optimize the overall UAM system.
Interactive Tool for Fleet Scenario Exploration:
To further enhance this analysis, we have developed a dynamic interactive tool called "CITYFLOW for UAM". This tool allows our UAM mobility research firms, airports, vertiport infrastructure developers, and traditional airline companies aspiring to transition to UAM operators, to:
- Select eVTOL Aircraft: Choose from a range of manufacturers, including Archer, Joby, Volocopter, eHang, Vertical Aerospace, Airbus, Wisk, Beta Technologies, and Eve Air Mobility. Each aircraft would have its specific characteristics (passenger capacity, speed, range, etc.) pre-loaded in the tool.
- Define Vertiport Network: Define or import a network of known or inferred vertiport locations based on the CITYFLOW analysis, allowing for iterative refinement and optimization.
- Set Demand Conversion Rate: Adjust the percentage of terrestrial demand converting to UAM to explore different scenarios and sensitivities.
- Calculate Fleet Size: Calculate the estimated fleet size required to meet the aerial demand between the defined vertiports, considering the chosen aircraft type and demand conversion rate.
- Visualize and Analyze Results: Provide visualizations of fleet size requirements, aircraft utilization rates, and other key performance indicators, enabling users to understand the trade-offs between different scenarios.
This interactive tool provides a powerful platform for stakeholders – from city planners and UAM operators to investors and aircraft manufacturers – to explore different scenarios, optimize vertiport placement, and make informed decisions about eVTOL fleet deployment. It bridges the gap between data-driven analysis and practical application, paving the way for the successful and efficient integration of UAM into our urban transportation ecosystems. Future development of this tool could incorporate real-time data feeds, weather information, and air traffic management simulations to create an even more robust and dynamic planning platform.
And one more thing. CITYFLOW UAM is offered as a white-label, online-accessible, and fully managed solution for your mobility business. Reach out to us if you'd like to see your logo displayed at the top left within the tool. 😄
About CITYDATA.ai
CITYDATA.ai brings mobility big data + AI to make cities smarter, sustainable, and more resilient. We provide insights about people counts, density patterns, movement trends, economic impact, and community engagement.
Founded in 2020 in San Francisco, California, CITYDATA.ai provides fresh, accurate, daily insights that are essential for smart city programs, economic development, urban planning, mobility and transportation, tourism, parks and recreation, disaster mitigation, sustainability, and resilience.
You can reach us via email at business@citydata.ai if you’d like to discuss your data needs and use cases. You can also follow the company on Linkedin, and the UniverCity.ai blog to stay updated on the newest innovations in big data and AI for the public sector.