Emergence of 4-D System Fundamental Diagram in Urban Air Mobility Traffic Flow

Abstract

Urban air mobility (UAM) is an emerging mode that promises to provide relief to congested urban streets. UAM relies on airspace, however, which is an exhaustible resource considering minimum aircraft separation requirements. In light of these requirements and UAM vehicle attributes, a simulation is developed to explore UAM traffic flows and congestion development. A decentralized conflict resolution scheme is employed in the form of a non-linear program (NLP) to offer improved flexibility in detours relative to past aircraft simulations. An expansion of Edie’s definitions of density and flow rate are used in conjunction with average speed to explore the relationships between traffic flow characteristics. The results find that UAM traffic flows emulate those of other modes, by following the familiar traffic patterns of build-up and breakdown captured in the macroscopic fundamental diagram. These findings also suggest the presence of a capacity of airspace that should be carefully managed by operators to achieve optimal system performance. The relationships established in this study highlight issues that UAM operators and aviation planners may face and could be used to improve the vehicle traffic modeling of other UAM models.

Measuring the Impact of Airspace Restrictions on Air Traffic Flow Using Four-Dimensional System Fundamental Diagrams for Urban Air Mobility

“…The air traffic flow results from each scenario are analyzed graphically in four relationships, with each using aircraft density as the independent variable. Density was used because it has been identified in previous research ( 8 , 9 , 23 ) as a driving factor in airspace congestion and flow breakdown. The other four air traffic flow measures (conflicts, average actual speed, average effective speed, and effective flow rate) are used as the dependent variables.…”

“…There is also a limited set of work in studying macroscopic air traffic flow patterns. Cummings and Mahmassani ( 8 ) developed and used a simulator of AAM vehicles to measure air traffic flow with aircraft densities, speeds, and flow rates. With these measurements the authors identified familiar patterns of traffic congestion such as declining vehicle speeds because of density and a system fundamental diagram pattern relating density and flow rate.…”

“…The paper did not, however, directly account for the effects of conflicts on aircraft speeds. Cummings and Mahmassani ( 18 ) developed air traffic flow models that went further by directly linking conflict resolution between vehicles with declines in both effective and actual aircraft speeds. That study also explored how vehicle parameters affected air traffic flow.…”

“…In the future these patterns can be validated and studied more closely with real-world experiments once AAM vehicles become more prevalent. Previous work in aviation simulation has been used for the AAM context in lieu of access to extensive experimental data ( 2 , 5 , 8 , 9 ). These works include estimates of airspace conflict rates ( 5 ), capacity ( 2 , 12 ), and air traffic flow ( 8 , 9 ).…”

“…Previous work in aviation simulation has been used for the AAM context in lieu of access to extensive experimental data ( 2 , 5 , 8 , 9 ). These works include estimates of airspace conflict rates ( 5 ), capacity ( 2 , 12 ), and air traffic flow ( 8 , 9 ). The simulator used in this paper was previously developed by Cummings and Mahmassani ( 8 ) and tracks the trajectory of AAM vehicles moving through airspace, with decentralized conflict detection and resolution along the way.…”