Integrating mobile location data into vertiport siting optimization for UAM airport access: A case study of the Chicago multi-airport region

This study presents a case analysis focused on identifying optimal vertiport locations for airport shuttle services serving two major international hub airports in the Chicago metropolitan region. Anonymized mobile location data, derived from revealed-preference tract-to-airport flows in the Chicago multi-airport region, are used to calibrate airport-access demand at census-tract resolution. To simulate realistic market uptake, the study integrates a behaviorally informed multinomial logit choice model that places Urban Air Mobility (UAM) alongside private vehicles, public metro transit, and TNC ridesharing services in a shared airport-access choice set into a mixed-integer linear programming framework that maximizes expected generalized travel cost savings under infrastructure, throughput, and operational constraints. The results indicate that optimal vertiport locations function primarily as “intermodal interceptors†situated near major surface bottlenecks rather than simply maximizing door-to-door coverage of dense residential areas. Sensitivity analyses reveal that network viability is driven by cost efficiency rather than aircraft performance. Per-mile fare remains the dominant network-geometry lever: mean dispersion has a large negative elasticity with respect to fare (E=−2.47, Ï =−0.99), indicating a consistent contraction of the optimal network as airborne distance becomes more expensive. By contrast, cruise speed produces only a weak and non-monotone topology response, even though it modestly improves total time savings among the trips that remain served. Furthermore, capacity constraints in early deployment phases force the network to prioritize long-distance, high-value trips, creating an inherent tension between efficient utilization and broad accessibility. These findings are synthesized into a probabilistic siting heatmap that distinguishes robust corridors from contingent sites, supporting scenario-based planning that prioritizes cost efficiency, reduces controllable ground-side frictions, and stages infrastructure expansion as operating assumptions improve during the early stages of UAM implementation.