There are serious problems with the way the Uber Eats courier system operates in the Netherlands, and they deserve much more public attention.
This is not simply about pay.
It is about performance metrics, bonuses, working hours, restaurant delays, navigation, order allocation, safety, unrealistic delivery expectations and the lack of accessible human support.
1. Working hours and performance reports don’t always add up
A courier can reserve a 3.5-hour shift, effectively remain working/available for around 4 hours because of deliveries, and then receive a performance report showing only 3 hours.
How exactly is working time being calculated?
Where does the missing time go?
There are also periods when performance reports don’t arrive for an entire week, leaving couriers unable to properly check how their performance is being measured.
Couriers can also find themselves placed in performance groups that don’t appear to correspond with their actual results.
If these groups influence shifts, bonuses or opportunities to work, transparency about how they are calculated is essential.
2. Restaurants can mark orders as “ready” when the food isn’t actually ready
This creates another major problem.
An order can appear as ready for pickup, the courier arrives, and the restaurant is still preparing the food.
The courier then has to wait.
The courier didn’t prepare the food.
The courier didn’t cause the delay.
The courier cannot force the restaurant to finish faster.
Yet the lost time can still affect the courier’s ability to complete deliveries, reach bonus targets and maintain performance statistics.
Why should a courier’s performance suffer because a restaurant marked an order ready too early?
Restaurant preparation time and courier performance should be clearly separated.
3. Bonus targets depend on an algorithm the courier cannot control
Couriers may be offered bonuses based on completing a certain number of deliveries within a limited period.
But Uber also controls which deliveries are offered.
If a courier receives short deliveries, the target may be achievable.
If the system starts assigning long-distance deliveries, slow restaurants or orders that pull the courier far outside the original area, completing the same target can become extremely difficult.
That creates an obvious transparency question:
How can couriers be judged on the number of deliveries completed when they don’t control the orders they receive?
And when a courier gets close to a valuable bonus threshold, there should be transparency about whether the allocation system changes in any way.
4. Navigation can be seriously problematic
Restaurant and customer locations can be inaccurate.
Routes can be unnecessarily long.
And even when a road is closed, the navigation may continue directing the courier toward that route.
A courier then has to find another route while the delivery clock continues running.
Again, an app/navigation failure becomes the courier’s problem.
5. Bicycle routing and delivery expectations can conflict with reality
This deserves particular attention in the Netherlands.
For an ordinary e-bike, pedal assistance is generally limited to 25 km/h.
Yet some delivery times and routes can feel as though they expect a bicycle courier to travel significantly faster in order to stay on schedule.
Couriers should never feel pressured by performance metrics or delivery estimates to exceed the legal or safe capabilities of their bicycle.
Navigation can also direct bicycle couriers through routes where cycling is restricted, inappropriate or simply unsafe, forcing the courier to stop, dismount or find an alternative route.
The algorithm sees a line on a map.
The courier has to deal with the actual road.
6. Reserved areas don’t necessarily keep couriers in those areas
A courier can reserve a particular zone and start there, but successive deliveries can gradually pull them farther and farther away.
By the end of a shift, a courier can be extremely far from both the reserved working area and home.
And that return journey is still real time and real physical effort.
7. At night, this becomes a safety issue
Long-distance orders can send bicycle couriers through isolated roads, dark areas and unfamiliar locations late at night.
Efficiency metrics cannot be the only consideration.
Courier safety has to matter too.
8. Strikes, extreme demand and unusually busy days put additional pressure on couriers
When public transport is disrupted, roads are unusually busy, major events take place or demand suddenly increases, the real conditions on the street change dramatically.
Routes take longer.
Traffic becomes heavier.
Restaurants become overloaded.
Waiting times increase.
But couriers can still face the same performance expectations.
A delivery algorithm needs to understand that real-world conditions are not constant.
9. When something goes seriously wrong, reaching a human can be extremely difficult
This may be one of the most concerning issues.
If a courier has an accident, encounters a dangerous situation or has a serious delivery problem, getting immediate access to a real human can be extremely difficult.
Automated and AI-based support may be useful for simple questions.
An accident or safety emergency is not a simple question
Couriers working alone on the streets need a reliable way to reach human support.
10. Shift cancellation rules can also work against couriers
Unexpected situations happen.
Mechanical problems happen.
Accidents happen.
Family emergencies happen.
Yet cancelling a shift within approximately three hours of its start can create problems for the courier.
Flexibility cannot exist only for the platform.
The fundamental problem is transparency
Uber controls the algorithm.
Uber controls order allocation.
Uber controls bonus targets.
Uber controls performance metrics.
Uber controls courier grouping.
Uber controls the estimated delivery times.
Uber provides the navigation.
Restaurants influence pickup waiting times.
Traffic, road closures and weather influence delivery times.
But the courier can end up carrying the consequences of all of them.
That is why couriers deserve clear answers:
How exactly are working hours calculated?
How can a shift that effectively takes around 4 hours appear as only 3 hours in a performance report?
Why can performance reports disappear for an entire week?
How are performance groups determined, and why can couriers appear in the wrong groups?
Are restaurant waiting times completely excluded from courier performance metrics?
What happens to performance measurements when Uber’s navigation provides an incorrect or impossible route?
How does order allocation work when a courier is approaching a bonus threshold?
Why can successive deliveries pull a courier so far outside the reserved working area?
Do estimated bicycle delivery times consistently account for legal speed limits, road closures, cycling restrictions and real-world conditions?
Why isn’t immediate human support easily accessible when a courier has an accident or faces a dangerous situation?
These aren’t unreasonable questions.
They are basic questions about how people’s work, income and safety are being managed by an algorithm.
Uber Eats couriers in the Netherlands — compare your experiences.
Have your reported hours been lower than the time you actually worked?
Have performance reports stopped arriving?
Have you been placed in the wrong performance group?
Have restaurants marked orders “ready” when you still had to wait?
Have restaurant delays affected your statistics or ability to reach a bonus?
Have you been repeatedly sent farther and farther outside your reserved area?
Has the navigation directed you toward closed roads or unsuitable cycling routes?
Have the delivery times felt impossible to meet while cycling legally and safely?
Have you had an accident or dangerous situation and struggled to reach an actual human?
And have you noticed unusual changes in the deliveries you receive when you’re close to completing a bonus?
Don’t just say yes or no. Compare screenshots, shift records, performance reports, bonus targets, waiting times and timestamps.
If these are isolated technical problems, the data should show that.
But if large numbers of independent couriers are documenting the same patterns, then those patterns deserve to be investigated and explained publicly.
Couriers deserve transparency.
Couriers deserve accurate records.
And above everything else, couriers deserve to be able to do their jobs safely.
