A new paper by UCL School of Management’s Dr Wei Miao examines a difficult design choice at the heart of taxi ride-sharing services, like Uber or Bolt. Who should decide the acceptance of a passenger’s ride request, the driver or the platform?
By studying Singapore’s taxi market, where the leading operators traditionally used a driver‑accept system, the researchers show that by switching to an auto‑assignment system driver earnings increase as well as the reliability of hailing a ride for passengers.
The research team compared two dispatch methods that are common across the industry. In a driver‑accept system, nearby drivers are offered a request and can accept or ignore it. In an auto‑accept system, the platform assigns the request to a specific nearby driver who generally cannot decline it.
To evaluate the two, Dr Miao and his co-authors build a dynamic equilibrium model that captures how forward‑looking drivers choose whether to take e‑hail jobs and where to position themselves, while passengers choose between e‑hail, street‑hail or public transport based on expected waits and prices. The model was estimated using detailed data from a major Singapore operator, between 2016 and 2017.
Its core finding is that automatic assignment increases average driver earnings by cutting idle time. E‑hail pickup distances also fall because the nearest available driver takes the job, so less time is spent empty between trips. In the researcher’s simulations, average e‑hail wait times fell slightly as well.
The research did find a caveat, however. Riders that hailed taxis on the street instead of electronically would see their wait times increase slightly as more drivers are absorbed into e‑hail. The paper highlights this effect as a design consideration for markets where app‑based dispatch continues to coexist with old-school roadside hailing.
The results speak to a wider debate about autonomy and efficiency in the platform economy. Driver discretion makes sense when workers have valuable private information, but at scale it can introduce frictions.
Speaking about the paper, Miao said:
“While “autonomy” is a powerful value proposition for the gig economy, it comes with hidden costs. Search frictions—the time spent looking for the perfect match—are a deadweight loss for everyone.
“By moving to an algorithmic assignment system, platforms can actually make drivers better off financially, even if it feels like they are losing control. Ideally, platforms should find ways to compensate for the psychological loss of autonomy while delivering the economic gains of efficiency.”
If platforms and drivers are worried about the perceived loss of control, Dr Miao suggests pairing auto‑accept with design features that preserve agency without reintroducing friction, for example scheduled breaks from auto‑assignment or limited destination preferences that operate outside peak periods.
The balance may vary by city, traffic pattern and the importance of street‑hail, but the Singapore evidence indicates that a well‑designed auto‑accept regime can grow the market and could benefit drivers financially.
