Mobility is not only a technical problem.
It is a question of values and human needs.

Every transport system optimizes something: speed, cost, energy, comfort, safety.
As soon as we compare these dimensions, we make a choice about what matters more, and that choice is never neutral.

Some variables are relatively objective: travel time, distance, energy use, cost, injury per km.
Others are deeply subjective: perceived time, comfort, effort, stress, sense of safety, dignity, autonomy.

The difficulty is not that these dimensions exist; it is that they cannot be cleanly reduced to a single metric.

How many minutes of travel time are worth less stress?
How much physical effort compensates for lower cost?
How much discomfort is acceptable in exchange for efficiency?

There is no universal answer.
Assigning weights to these dimensions is a value judgment, not a scientific fact.
Pretending otherwise is a form of dishonesty often hidden behind “optimization.”

The problem is made harder by measurement.
Many of the most important variables like mental load, perceived safety, familiarity, fatigue, or the ability to focus on other tasks cannot be reliably quantified, even though they strongly shape behavior.

Ignoring them because they are hard to measure does not make them disappear.
It only guarantees poor design.

Human mobility is multi-dimensional.
People choose modes of transport not by maximizing a single variable, but by navigating trade-offs between time and attention, effort and comfort, flexibility and predictability, exposure and protection, individual needs and social norms.

Several frameworks attempt to explain how these trade-offs are navigated in practice.
One of the most useful is motility, proposed by sociologist Vincent Kaufmann. (accessible freely via sci-hub)

Motility describes mobility not as movement itself, but as a form of capacity.
It is shaped by three dimensions: access to transport options, competence to use them confidently, and appropriation, the extent to which a mode feels legitimate, acceptable, or “for me.”

Modal choice emerges from the interaction of these three factors, not from abstract efficiency.
A mode can be objectively fast or cheap and still remain unused if it feels unsafe, unfamiliar, or socially misaligned.

Car dominance is therefore not only the result of infrastructure.
It is reinforced by learned competence, cultural norms, and decades of design that have made the car the default solution for everyday mobility.

Within this context, the persistence of car use becomes easier to understand.

Cars minimize mental effort per kilometer, even when they are energetically inefficient.
They offer habit and familiarity, requiring little conscious planning and anticipation.
They provide an enclosed and predictable environment that increases perceived safety.
They function as a privacy bubble, allowing control over sound, temperature, pace, and social exposure.
They offer flexibility, enabling trip chaining, detours, and unplanned tasks without additional planning.
They also shape time perception: driving often feels shorter than waiting, transferring, or coordinating schedules.

This is why faster vehicles do not always save time in practice: urban sprawl and longer trip distances often erase speed gains.
Efficiency is not just about energy demand but also total effort, convenience, and real-world experience. To push this furter, we can use the notion of generalized speed of Ivan Illich which look at all the time (notably the working time to finance a vehicle) to cover a given distance. The result is generally painful with generalized speed rarely above 30 kmh with lower income class being more penalized.

From a justice perspective, this matters.
A system optimized for an “average” user often shifts costs onto others: non-drivers, children, the elderly, or those who don’t match the average.
What appears efficient on paper may be unfair in practice.

We do not claim to know the “correct” weights for these dimensions.
We do not assume that energy efficiency alone defines what is better.

Instead, we acknowledge the existence of these variables and treat them as real design constraints.
Our goal is not to eliminate trade-offs, but to design with them consciously, addressing true human needs without indulging in excess.

By iterating on real vehicles with real users, we observe how changes in design affect access, competence, and appropriation. Not just performance metrics.

Designing for humans means accepting uncertainty, resisting false optimization, and refusing to treat subjective experience as secondary.

Efficiency matters.
So does dignity.
So does time well spent.
So does mobility that serves people, not just machines.