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Team: Kexin (Sally) Chen, Ali Shamshiripour, Daniel Feldman, Md Sami Hasnine, Andre Romano Alho, Ravi Seshadri, Moshe Ben-Akiva

  • Develop an agent-based simulation framework to model urban air mobility (UAM) services including demand and supply, and integrate this framework within SimMobility

  • Quantify the demand for UAM and the impacts on the transportation system across several prototypical north American cities

The urban mobility landscape is evolving rapidly due to technological developments including communication, automation, and electrification. This has led to the proliferation of new modes and ‘smart’ mobility services such as one-way and two-way car sharing services, dockless and dock-based bike sharing/PMD (Personal Mobility Devices) systems, ride-hailing services, and automated and electric vehicles. A more recent and intriguing addition is urban air mobility, which refers to aerial vehicle concepts for short-haul urban passenger transportation. For instance, Uber Elevate, already offers urban air services thorough helicopters (Uber Copter) and – along with several other companies- is working towards deploying aerial ride-sharing at scale in the near future. The concept of urban air mobility is facilitated by technological developments in battery storage, electrical power transmission and distributed propulsion systems, and it is completely plausible that these services will be deployed by automated aircrafts or drones in the future.  Given these developments and the likely deployment of such services in the near future, there are several important and pressing research questions that remain to be addressed in the context of urban air mobility:

  • Can aerial ride-sharing offer a feasible and practical alternative to ground-based shared mobility services?

  • What types of trips are likely to be served by such a service (e.g commuting or non-commuting, long or moderate distances, etc.)?

  • What impact will these services have on existing modes?

  • How will UAM affect the transportation system, and in particular, what are the potential implications on congestion, energy and emissions?

  • What are the infrastructure requirements (e.g. vertiports, access/egress) and land-use implications?

  • How is the demand for UAM likely to vary with operational configuration and service pricing across different city types?

In order to do so, we employ agent- and activity-based simulation of mobility outcomes in prototype cities.

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