Murilo Pinto Alves Apparecido
Research Mentor: Taehoon Han
Mentor Department: Mechanical Engineering, Engineering
Author(s): Taehoon Han
Session: Session 5 (2:00 PM – 2:50 PM)
Presentation Type: Poster 62
Abstract
Urban Air Mobility (UAM) systems are expected to operate over densely populated metropolitan regions, yet existing routing frameworks prioritize distance and energy efficiency without accounting for ground casualty risk in the event of aircraft failure. This study develops a high-resolution spatial risk model for drone taxi operations over Chicago and integrates it into a risk-aware path planning framework. A 100m × 100m geospatial casualty risk grid covering Chicago’s full municipal boundary (~50,000+ cells) was constructed by combining LandScan USA 2021 daytime population estimates, building coverage ratios derived from municipal footprint data, and a Monte Carlo ballistic descent simulation drawing 2,000 samples to model debris scatter distributions. Each grid cell encodes an expected casualty value N_casualty(k) representing predicted ground fatalities from a catastrophic failure event. The resulting risk surface was embedded into an A* path planning algorithm with a tunable trade-off parameter ? balancing flight distance against casualty exposure. Results show that highest-risk zones concentrate in Chicago’s downtown Loop and Near North Side, with N_casualty(k) values of 1–10 across most of the city. Pareto frontier analysis demonstrates that a balanced weighting of ? = 0.5 achieves 60–80% of the maximum possible risk reduction while increasing flight distance by only 10–20% over the shortest path. These findings suggest that moderate risk-weighting is practically favorable for real UAM operations and provide a scalable framework for safety-conscious urban flight corridor design.
