CHENNAI: It’s a theory that has been doing the rounds on social media, but is it true? Some travelers have noticed a curious disparity in taxi fares displayed simultaneously on Android devices and iPhones for identical trips, leaving them wondering whether the pricing algorithms on ride-hailing apps are programmed to charge users more. from Apple.
TOI used an iPhone and Android device simultaneously to search for rides to the same destinations from three locations in Chennai. In each case, the displayed rate was higher on iOS (see chart).
A warning: this is by no means conclusive evidence. The same searches on another day may return different results. Furthermore, the pattern appeared to be limited to single runs and more pronounced over relatively shorter distances. For the record, Uber said it did not have a policy of customizing the price of rides based on a potential user’s phone. It attributed disparities, if any, to factors such as estimated time, distance and real-time demand for taxis in a specific area. Ola did not respond to TOI’s queries.
Once companies identify the regular user, they inflate rates: expert
Experts suggest the disparities are due to how transportation apps access hardware data that users must consent to when installing an app.
C Ambigapathy, CEO of transportation platform Fastrack in Chennai, said the central server could easily generate fare estimates tailored to the user’s device. “It’s child’s play for companies to modify rates based on hardware details while hiding behind the ‘dynamic pricing algorithm’ explanation,” he said.
P Ravikumar, former senior director at the Center for Development of Advanced Computing (C-DAC) in Thiruvananthapuram, said aggregators were known to use rapid development tools such as machine learning frameworks (Google Cloud AI and Azure ML) to refine pricing algorithms. These tools can incorporate variables such as device type, app usage frequency, and search patterns to adjust rates dynamically.
TOI could not independently verify whether that is indeed the case.
An Intelligent Transport Systems expert involved in formulating the Union government’s aggregation policy said the fare increases were not limited to differences between phone models. He noted that this also applies to frequent users of the app and those who check rates repeatedly on the same device. “These platforms rely on user behavior patterns to adjust prices dynamically,” said the expert.
Ambigapathy noted that companies leverage past data to measure user loyalty and trust. “Once they identify a repeat user, they inflate the rates, trusting that the user will eventually book, even if they wait for the prices to drop, although they never do.”
Ravikumar said it was time for companies to be transparent about their pricing models. “If factors such as estimated time, distance, and travel modes are consistent, users should not face discrimination based on their device.”