Beyond Algorithms: When Riders and Drivers Set the Price

Over the past two decades, ride-hailing has redefined urban mobility. Platforms that connect riders and drivers in real time have transformed city transport into a more efficient and predictable experience. But innovation is reshaping on-demand mobility again—not through more complex algorithms, but through new approaches to how prices are set.

How fare negotiation expands the market for trips

In traditional ride-hailing systems, dynamic pricing balances supply and demand through data. Yet these algorithmic models typically optimise for the “average” journey, which can leave gaps at the margins—such as riders priced out during surge periods, or drivers unwilling to accept low-fare trips. Fare negotiation addresses this by allowing riders and drivers to reach an agreement directly, unlocking trips that might otherwise not take place. In fact, around half of all surveyed riders and drivers in the study agreed that fare negotiation helped them get a ride when fares elsewhere were prohibitively high for riders, or when fares offered to drivers elsewhere were too low.

Oxford Economics’ research also suggests that by enabling drivers and riders at the lower end of the price range to match directly, fare negotiation reduces reliance on fare subsidies and promotional discounts often used by algorithmic platforms to stimulate demand. Lower dependence on these incentives may reduce platform costs, which can in turn, affect fee structures and the prices faced by riders.

The data illustrate the scale of adoption. Across Colombia, Egypt, Mexico, Morocco, Nepal, Pakistan and Peru, more than 60 per cent of trips on inDrive involve negotiated fares. When factoring in only the markets in Latin America and the Middle East, the share approaches 80 per cent. The data suggest riders associate the option of fare negotiation with greater control over travel costs, while drivers report more discretion over trip selection and earnings.

Bridging gaps in urban mobility

Beyond usage patterns, the research highlights implications for mobility in markets with uneven transport provision. In areas where public transport coverage is limited or inconsistent, fare negotiation appears to facilitate trips to peripheral or less well-served locations. Nearly half of surveyed riders and drivers across the above markets reported that it helped them access destinations that are otherwise difficult to reach. For lower-income households, this can affect access to employment, education and services; for drivers, it can provide greater flexibility in managing income under volatile economic conditions.

Critics argue that negotiation introduces friction into a service designed for speed and simplicity. However, the findings suggest that in heterogeneous markets—with wide variation in incomes, geographies and travel needs—some degree of user interaction may help platforms accommodate local conditions more effectively. Rather than replacing automation, fare negotiation complements it by allowing price-setting to respond to circumstances that algorithms may not fully capture.

Combining algorithmic precision with human choice

By enabling bilateral agreement, the model retains digital matching while incorporating human judgement into pricing decisions. It also changes the nature of price transparency: fares are determined through explicit choice and mutual agreement between rider and driver, rather than through opaque algorithmic adjustments.

Ultimately, fare negotiation reflects a broader shift in ride-hailing design—from fully automated pricing towards hybrid approaches that blend algorithms with user choice and enable social inclusion in on-demand transport. Whether this model scales across all markets remains an open question, but the evidence suggests it can play a role in expanding how on-demand transport functions in diverse economic and social contexts.

To learn more about how fare negotiation improves the efficiency and accessibility of on-demand transport, please download the report here.