Urban spatial data-driven planning and operations for eVTOL city logistics under airspace, energy and charging constraints
Year: 2026
Type: article
Abstract: Urban Air Mobility (UAM) logistics planning is still often carried out on abstract Euclidean instances, in which demand generation, low-altitude airspace restrictions, payload-dependent energy use, and en-route charging are modeled separately. This paper proposes a city-data-driven planning and operations framework for eVTOL city logistics that connects these elements within a single workflow. Building heights, points of interest, vertiport locations, and airport-related no-fly-zone boundaries are used to construct proxy delivery-demand scenarios, feasible operating areas, and candidate charging resources for comparative network planning, rather than to estimate observed delivery demand. A payload-sensitive energy model and a hybrid routing heuristic then jointly account for airspace feasibility, payload variation, route planning, and en-route charging decisions. The computational behavior of the heuristic is first examined on synthetic instances with 20, 50, and 100 customers, after which the framework is applied to a Chengdu case study built from real urban spatial inputs and simulated 50-customer task batches. The heuristic solves all instances with up to 50 customers at lower cost than ant-colony and particle-swarm benchmarks; in the Chengdu case, paired comparisons on 30 shared task batches show that the proposed charging layout attains costs statistically indistinguishable from six demand-weighted facility-location baselines under Holm-corrected Wilcoxon tests, with paired differences within about ± 1.2 % , while explicitly incorporating the airspace, endurance, and charging feasibility that such benchmarks do not model. The framework is therefore intended as a comparative planning and network-screening tool under limited demand data, supporting early deployment decisions for eVTOL city logistics.
Source: Journal of Urban Mobility
Authors: Zilong Chen, Shiyu Li, Liang Guo, Binqi Chen
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7225754557