AAE5301 Chap.2 Vertiport Location and Coverage Models
Vertiport Location and Coverage Models
Location as a service promise
Vertiport siting affects access, route geometry, charging demand, network cost and safety. The course workflow begins with scenario and data, screens infeasible candidates, selects a model, and tests the result. Set covering, maximal covering, p-median and p-center models encode different promises.
Hub location adds origin-destination flows and consolidation, so it cannot be reduced to selecting a facility with a different label.
Match the objective to the promise
Set covering asks for the fewest sites that bring every demand point within a stated threshold. Maximal covering accepts a site budget and captures as much weighted demand as possible.
P-median reduces total weighted access, whereas p-center protects the longest assigned distance. These families can choose different sites from the same data because they value different distributions of inconvenience.
State the service promise first, then select the family; do not choose a familiar formula and retrofit the narrative.
Screen, solve and stress-test
Candidate screening should remove sites that violate documented airspace, land, safety or policy conditions, not merely sites that seem unattractive. After optimisation, map assignments and inspect isolated demand, capacity pressure and concentration risk.
Change the distance threshold, facility budget and demand weights to find breakpoints. A location remains credible when a small input change alters performance gradually; a solution that collapses after one modest change needs contingency capacity or an alternative site.
What this chapter covers
- 01
Scenario and candidate screening
- 02
Demand, safety, noise and access
- 03
Set and maximal covering
- 04
P-median and p-center objectives
- 05
Opening and assignment variables
- 06
Single and multiple hub flows
Worked example · free
Compare two demand-weighted sites
- 3Multiply each district weight by distance to Site A.
- 3Repeat for Site B and compare totals.
- 2Inspect the worst individual assignment before recommending.
Key terms
- Set Covering
- A location model minimising facilities while covering all demand within a stated radius.
- Maximal Covering
- A model maximising covered demand for a fixed facility budget and service radius.
- Candidate Site
- A location left in the model after physical, policy and safety screening.
- Demand Weight
- A value scaling an assignment distance or cost by the amount of service associated with a demand point.
- Single Allocation
- A hub rule assigning each non-hub node to one selected hub.
Vertiport Location and Coverage Models FAQ
When is p-median the right model?
Use p-median when a fixed number of facilities is given and the service goal is to minimise total demand-weighted access distance. It favours average performance and does not automatically protect the worst-served demand point.
What makes p-center different?
P-center minimises the maximum distance between any demand point and its assigned facility. It encodes a worst-case access promise and can accept a higher total distance to protect remote users.
Why screen sites before optimisation?
Safety, airspace, terrain, policy or land availability can make a location impossible. Screening removes false choices and reduces model scale, but the rule must be documented because screening can also remove resilience value.
How should capacity be added to a location model?
Link assigned demand to the capacity of each opened facility. The assignment total at a site cannot exceed its available throughput, and demand can be assigned only when the site is open. If capacity varies by time, vehicle type or charger, preserve that index rather than hiding all resources in one annual number.
What makes a hub-location result different from ordinary facility placement?
Hub models assign origin-destination flows through consolidation points and may reward inter-hub economies. Performance depends on paths and flow pairs, not only each demand point's distance to its nearest facility. Validate the routed flow, hub capacity and transfer assumption before interpreting a selected hub as a complete service network.
How can I test whether a location result is robust?
Vary demand weights, candidate availability, the facility budget and the coverage or distance threshold one at a time. Record when the selected sites change and whether performance deteriorates smoothly. Then remove each chosen site in turn to expose dependence on a single location.
A robust recommendation reports stable sites, plausible alternatives and the assumptions that trigger a switch; it does not claim certainty from one deterministic data set.
Exam move
Write one sentence for the service promise behind each location family, then sketch the resulting objective. Practise explaining why a p-median and p-center answer can disagree without either being mathematically wrong. Add one siting factor as a hard constraint in every exercise.
For each result, draw candidate sites, opened sites and assignments with different symbols; then mark the worst-served demand and the busiest facility. Repeat after changing the site budget or coverage threshold. The comparison reveals whether the answer is driven by averages, extremes, capacity or a screening decision. Record the first parameter value that changes the chosen sites.
