Demand - supply interactions
- Supply information can be readily grokked from vehicle manufacturers
- Demand, however, is complex to understand and glean because humans are often irrational and not easily predictable
- How many people will use UAM? How often will they use it?
- Which UAM features are useful and attractive?
- What’s the cost and speed of UAM services?
- How does gender, age, income, health, tech-savviness, and education levels change how people use UAM?
- How safe is UAM?
- Can people trust autonomous UAM operations?
- How okay are people with the noise and visual occlusion issues that UAM may present?
- People have biases and prejudices.
What makes measuring demand all the more complex is the prevalence of biases and prejudices in surveys
There are two types of commonly noted preferences in surveys
- Revealed Preferences (RP) - These are based on observational data. Revealed preferences are those that people indirectly reveal from their behaviour and interactions with a product or service
- Stated Preferences (SP) - Stated preferences are more explicit as they are directly obtained from survey participants by means of pointed questions and estimates. Survey creators also create scenarios and question people about their preferences in such scenarios
Note
UAM demand modelling relies on SP because there’s little to no observational data. UAM is a new and emerging field
How can UAM be explained to the public when it is still a largely rare and unavailable mode of transport? Explanations about the dynamics of UAM can be carried out with the general public using scenario development. Develop scenarios and sample environments where UAM systems operate
- If modelling demand is a complex task, how can it be estimated?
- Understanding and modelling user acceptance
Demand and Acceptance
I think both user demand and user acceptance go hand-in-hand but there are some subtle differences that must be known. Customers create demand for a service because they believe that said service can address some issues they’re facing. However, will they accept using such a service? Even if it solves their issues, are they comfortable using it? That’s acceptance
Putting everything together
- Transport modelling is complex, especially for non-existent modes
- Human factor is the biggest unknown for UAM
- Methodological tools for demand forecasting for UAM. Model user acceptance, and anticipate societal acceptance challenges
- Data collection is huge and important (Garb in, garb out)
- Sensitivity analysis and scenario analysis are a must