What counts as a good acceptance rate on Uber and taxi apps, and should it shape job access?
- Perry Richardson
- Aug 17
- 3 min read
Most taxi and private hire drivers use a booking app to snare differing levels of work throughout their working days. Private hire vehicle drivers rely on such apps like Uber or Bolt, but more traditional taxi drivers can pick and choose from street and rank work too.
When it comes to acceptance rates, there is no single benchmark across the industry. Acceptance rate targets vary by platform, country and by feature. In the United States, Lyft set 85% as the threshold to keep seeing full upfront trip details in New York, which shows how high some platforms pitch it.
Uber’s current approach is mixed. In Great Britain, Uber Pro tiers are built around points, driver rating and cancellation rate, not acceptance rate, according to the UK programme terms.
In some markets, Uber also runs an “Advantage Mode”, where acceptance rate forms part of eligibility alongside cancellation rate and rating, with a published 25% minimum.
On Uber in the UK, the help pages focus driver performance management on cancellation rate mechanics rather than acceptance. The app shows both figures in the profile, and explains how each is calculated over the last 100 relevant requests.
Other apps take a firmer line on acceptance as a service quality signal. FREENOW explains why low acceptance harms wait times and introduces a warning system, and its UK Priority Programme asks drivers to keep acceptance high and cancellations low to earn more visibility. Bolt tracks acceptance too, although it notes some ignored orders outside a driver’s set radius do not count against the metric.
A practical view for UK taxi and PHV drivers using multiple apps is that “good” depends on the operator. If a platform ties features or priority to acceptance, a target near that threshold becomes the meaningful number. Where only cancellation rate is enforced, acceptance becomes a softer signal of reliability rather than a trigger for sanctions.
Should higher acceptance be rewarded?
Rewarding higher acceptance with queue priority, surge access or pre-book visibility can stabilise pick-up times and reduce rider drop-offs. Apps already do this in different ways, from Lyft’s upfront-details rule to FREENOW’s priority scoring.
The balance is important. Over-weighting acceptance can push drivers to take poor-value jobs, which increases dead mileage and decreases morale. Schemes that blend acceptance with cancellation rate and rider ratings, as Uber’s Advantage Mode does in some regions, tend to be more workable.
Should lower acceptance be restricted?
Blanket restrictions based only on acceptance risk excluding part-time or off-peak drivers and those operating in thin markets where cherry-picking is often rational. A measured approach is to gate premium features rather than block work, and to rely on cancellation rate and reliability for any corrective action.
Benefits and negatives for drivers, operators and passengers
For drivers, higher acceptance can unlock priority queues and steadier work where the platform uses acceptance in its scoring. It can also reduce idle time if dispatch is efficient. The downside is reduced control over trip selection and potential earnings dilution if too many low-value jobs are accepted. Driver acceptance varies by experience and preference to working, which supports keeping policies flexible.
For passengers, higher acceptance reduces waiting times and cancellations, improving reliability at peaks. If drivers feel compelled to take marginal trips, service quality can slip, so policies that reward consistency without forcing lower-value or less rewarding work usually perform best. FREENOW’s guidance links low acceptance to longer waits and cancellations, which aligns with this outcome.
For operators, acceptance is a useful health metric for dispatch and ETA stability. Using it with cancellation rate and ratings offers a cleaner picture of reliability. Over-penalising low acceptance can shrink supply and encourage gaming or app-multi-tasking, while feature-based incentives tend to keep supply engaged. Lyft’s 85% rule and FREENOW’s priority model illustrate two versions of that incentive approach.
A good acceptance rate is the one that meets the rule set of the app you are on. Where a platform ties features to acceptance, aim for that stated level. Where it does not, keep acceptance sensible but focus on a low cancellation rate and strong ratings. Operators are most likely to reward consistent acceptance without making it the only gate to work, and should publish clear thresholds so drivers can plan their shifts against them.
