Data-Driven Urban Air Mobility Flight Energy Consumption Prediction and Risk Assessment

The current technological advancements revolutionizing the concept of Urban Air Mobility (UAM), has a concurrent need to quantify the operational safety of these vehicles in terms of their associated risk. Providing safety certification of flight operations of UAM vehicles is critical as the concept relies on battery powered electrically Vertical Takeoff and Landing (eVTOL) vehicles, to operate in the current Air traffic control. In this paper, a data-driven method for UAM vehicle energy consumption prediction and risk quantification with conditional value-at-risk based on energy consumption distribution is presented. Significant factors affecting energy consumption, such as density altitude, aircraft design, airspeed, and collision avoidance algorithms, are considered in the data-driven based energy consumption prediction of multiple eVTOL flights. Additionally, a risk metric was deployed to evaluate the risk associated with worst case energy dissipating flights. Our result shows that the proposed approach provides a generalized method to quantify operational safety of UAM network over a given region.