Michael Kilbourne | Search NASA

NASA NTRSDate not supplied

Exploring Ridesharing in Passenger Urban Air Mobility: A Comparative Analysis

There is growing interest in urban air mobility (UAM) as an alternative for passenger and cargo transport around metropolitan areas in a multimodal transportation system that leverages small, electric aircraft. Ridesharing has been proposed as a means of making UAM passenger trips more affordable and environmentally friendly. We present a UAM ridesharing model integrated into an existing computational framework for analyzing daily work commute trips within a metropolitan area. We leverage this model to estimate the potential demand for ridesharing-enabled UAM trips within six metropolitan areas across the United States: Chicago, IL; Cleveland, OH; Dallas, TX; Denver, CO; New York City, NY; and Orlando, FL. We compare results for each metropolitan area with and without ridesharing. Results indicate that ridesharing enables at least an order of magnitude more UAM-preferring passengers than without ridesharing, though specifics vary across metropolitan areas and network sizes. Enabling ridesharing in UAM also considerably lowers the mean and mode value of time for passengers that select the UAM mode, indicating that ridesharing can help make UAM more economically accessible to a larger set of the population. An important caveat is that the UAM ridesharing model does not account for operational constraints, such as aerodrome capacity and aircraft availability, and relies on a perfect knowledge of passenger movements and mode preferences. This leads to high UAM ridesharing volumes that are unlikely to reflect real-world UAM operations and thus serves as an upper bound estimate.

advanced air mobility↗

NASA NTRSDate not supplied

Passenger Aggregation Network with Very Efficient Listing (PANVEL) Ridesharing Model for Urban Air Mobility

This paper introduces a ridesharing model useful for examining the role of ridesharing in advancing Urban Air Mobility (UAM) operations. Although the success of UAM will likely require a host of new technologies in aircraft design, airspace management, autonomy, and more, operational innovations like ridesharing may also be key, especially to lower operating costs and attract a broader market. The ridesharing algorithm introduced, termed the Passenger Aggregation Network with Very Efficient Listing (PANVEL) model, estimates trip numbers and passenger occupancy aboard particular aircraft within a UAM network in a metropolitan area. This algorithm is applied within a transportation computational framework to determine the impact of ridesharing on mode choice, passenger travel patterns, and transportation costs. The algorithm aggregates passengers traveling between identical origin-destination pairs, factoring in individual-specific metrics, such as value of time and alternative mode options, to generate high load factors on UAM aircraft. The computational framework simulates passenger mode-choice selections using an effective-cost approach to determine the number of ridesharing UAM-preferred trips, and results with ridesharing are representative of a rough upper bound on UAM ridership potential for a given metro area. A case study in the Cleveland metro area is presented to demonstrate the algorithm and assess ridesharing potential.

advanced air mobility↗

NASA NTRSDate not supplied

Refined Analysis of CO2 Emissions in Urban Air Mobility Networks

In this study, we examine the environmental impact of Urban Air Mobility (UAM) operations as measured via carbon dioxide (CO2) emissions by leveraging a computational model from previous studies that has been refined and expanded. Several scenarios are examined in which total transportation emissions for a representative day in a metro area are distributed differently among vehicle types and transportation modes. Specifically, we investigate the use of electric air vehicles in a UAM network; the increasing presence of electric ground vehicles for ground transportation; the impacts from implementing ridesharing in the UAM mode; and the effects of varying the electricity grid emissions. Results for a case study in the Chicago, IL metro area indicate that incorporating electric ground vehicles and UAM ridesharing can both lead to reduced CO2 emissions. These preliminary results indicate that carbon emissions from a UAM system leveraging ridesharing and all-electric aircraft may not be a practical operational limit on UAM, though further improvements to the modeling are warranted.

advanced air mobility↗

NASA NTRSDate not supplied

Passenger Aggregation Network with Very Efficient Listing (PANVEL) Ridesharing Model for Urban Air Mobility

This presentation introduces a ridesharing model useful for examining the role of ridesharing in advancing Urban Air Mobility (UAM) operations. Although the success of UAM will likely require a host of new technologies in aircraft design, airspace management, autonomy, and more, operational innovations like ridesharing may also be key, especially to lower operating costs and attract a broader market. The ridesharing algorithm introduced, termed the Passenger Aggregation Network with Very Efficient Listing (PANVEL) model, estimates trip numbers and passenger occupancy aboard particular aircraft within a UAM network in a metropolitan area. This algorithm is applied within a transportation computational framework to determine the impact of ridesharing on mode choice, passenger travel patterns, and transportation costs. The algorithm aggregates passengers traveling between identical origin-destination pairs, factoring in individual-specific metrics, such as value of time and alternative mode options, to generate high load factors on UAM aircraft. The computational framework simulates passenger mode-choice selections using an effective-cost approach to determine the number of ridesharing UAM-preferred trips, and results with ridesharing are representative of a rough upper bound on UAM ridership potential for a given metro area. A case study in the Cleveland metro area is presented to demonstrate the algorithm and assess ridesharing potential.

advanced air mobility↗

NASA NTRSDate not supplied

Refined Analysis of CO2 Emissions in Urban Air Mobility Networks

In this study, we examine the environmental impact of Urban Air Mobility (UAM) operations as measured via carbon dioxide (CO2) emissions by leveraging a computational model from previous studies that has been refined and expanded. Several scenarios are examined in which total transportation emissions for a representative day in a metro area are distributed differently among vehicle types and transportation modes. Specifically, we investigate the use of electric air vehicles in a UAM network; the increasing presence of electric ground vehicles for ground transportation; the impacts from implementing ridesharing in the UAM mode; and the effects of varying the electricity grid emissions. Results for a case study in the Chicago, IL metro area indicate that incorporating electric ground vehicles and UAM ridesharing can both lead to reduced CO2 emissions. These preliminary results indicate that carbon emissions from a UAM system leveraging ridesharing and all-electric aircraft may not be a practical operational limit on UAM, though further improvements to the modeling are warranted.

advanced air mobility↗

NASA NTRSDate not supplied

Exploring Ridesharing in Passenger Urban Air Mobility: A Comparative Analysis

There is growing interest in urban air mobility (UAM) as an alternative for passenger and cargo transport around metropolitan areas in a multimodal transportation system that leverages small, electric aircraft. Ridesharing has been proposed as a means of making UAM passenger trips more affordable and environmentally friendly. We present a UAM ridesharing model integrated into an existing computational framework for analyzing daily work commute trips within a metropolitan area. We leverage this model to estimate the potential demand for ridesharing-enabled UAM trips within six metropolitan areas across the United States: Chicago, IL; Cleveland, OH; Dallas, TX; Denver, CO; New York City, NY; and Orlando, FL. We compare results for each metropolitan area with and without ridesharing. Results indicate that ridesharing enables at least an order of magnitude more UAM-preferring passengers than without ridesharing, though specifics vary across metropolitan areas and network sizes. Enabling ridesharing in UAM also considerably lowers the mean and mode value of time for passengers that select the UAM mode, indicating that ridesharing can help make UAM more economically accessible to a larger set of the population. An important caveat is that the UAM ridesharing model does not account for operational constraints, such as aerodrome capacity and aircraft availability, and relies on a perfect knowledge of passenger movements and mode preferences. This leads to high UAM ridesharing volumes that are unlikely to reflect real-world UAM operations and thus serves as an upper bound estimate.

advanced air mobility↗