This paper presents a demand-driven framework for on-demand Urban Air Mobility (UAM) network design that links vertiport siting, fleet simulation, and door-to-door travel-time feasibility. Demand is estimated from commuter and passenger activity data, converted into spatial trip-end points, and clustered using K-means to generate candidate vertiport locations. Candidate networks are screened using range and minimum station-spacing constraints, then evaluated with a discrete-event simulation that models multi-vehicle dispatch, deadhead relocation, battery swaps, and service regularity. Flight time and energy consumption are computed using a point-mass eVTOL performance model. In a Greater Los Angeles case study, the preferred design expands from four stations and four eVTOLs at low demand to sixteen stations and twelve eVTOLs at the highest tested demand level. Results show that larger fleets improve completion time and vehicle-arrival regularity but do not eliminate deadhead flights, indicating that spatial demand imbalance remains an operational burden. The travel-time savings analysis further suggests that UAM is most defensible for longer or congestion-heavy trips where sufficient non-flight time remains after accounting for flight time.
Figures & tables
Fig. 1 : Door-to-door UAM trip schematic and ground alternative
Fig. 2 : UAM network design and evaluation pipeline
Fig. 3 : DES Dispatch and Relocation Logic
Parameter (unit)
Description
Value
Δmax(km)
Max inter-station distance
81
Δmin(km)
Min station spacing
10
O
Number of runs
250
Ebat(MJ)
Battery usable energy
339
{TCB,Tboard,Tdeboard,Tclr}(s)
Swap, board, deboard, clearance times
{90,240,150,45}
v(km/h)
Average ground speed
{50,80}
TABLE I : Case-study parameters and operational constants
# Stations
Avg. ss(k)
Infeasible missions
Min dist. (km)
Max dist. (km)
4
0.6297
0
20
55
8
0.6359
0
14
70
12
0.6713
0
12
76
16
0.6949
0
11
78
18
0.6915
2
8
78
TABLE II : Clustering results
Fig. 4 : Centroids of the 16-clusters case used as candidate station locations; station latitudes and longitudes are annotated
Fig. 5 : Stations mapping over the study area
D
(K∗,F∗)
ET (h)
MVAI (min)
DHR (%)
CB
100
(4, 4)
2
18
22
15
500
(8, 8)
6
26
28
77
1000
(8, 8)
11
34
30
145
2000
(16, 12)
15
37
29
337
TABLE III : Preferred candidate design and performance across demand scenarios
K
F
ET (h)
MVAI (min)
DHR (%)
CB
4
16
10
30
24
418
8
32
7
15
28
400
12
48
4
13
23
490
16
64
3
10
24
520
TABLE IV : Fleet-abundant extreme case for D=2000 with F=4K
Fig. 6 : Objective landscape for the weighted cost function J as a function of station count K and fleet size F under four demand scenarios: (a) D=100 , (b) D=500 , (c) D=1000 , and (d) D=2000
Fig. 7 : Station–fleet tradeoffs for D=2000 passengers. Panels report DES outcomes across tested station counts K and fleet sizes F : (a) completion/evacuation time, (b) service regularity proxy, (c) flight frequency, and (d) battery swaps
Fig. 8 : Deadhead burden for D=2000 : total number of flights versus percentage of deadhead flights
Fig. 9 : Maximum allowable non-flight time budget vs. station-to-station range for multiple travel-time savings targets at v=80km/h