most citedA deep reinforcement learning approach to assess the low-altitude airspace capacity for urban air mobility

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

stat.AP2024

Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Due to Covid-19

Saeed Saleh Namadi, Behnam Tahmasbi, Asal Mehditabrizi +2

COVID-19 has deeply affected daily life and travel behaviors. Understanding these changes is crucial, prompting an investigation into socio-demographic and socio-economic factors.…

cs.RO2023★ 2 cited

A deep reinforcement learning approach to assess the low-altitude airspace capacity for urban air mobility

Asal Mehditabrizi, Mahdi Samadzad, Sina Sabzekar

Urban air mobility is the new mode of transportation aiming to provide a fast and secure way of travel by utilizing the low-altitude airspace. This goal cannot be achieved without…