CHENNAI: It’s a theory that has been doing the rounds on social media, but is it true? Some commuters have noticed a curious disparity in taxi fares displayed simultaneously on Android devices and iPhones for identical trips, leading them to wonder if ride-hailing apps’ pricing algorithms are programmed to charge more for Apple users.
TOI used an iPhone and an Android device simultaneously to search for rides to the same destinations from three locations in Chennai. In each case, the price displayed was higher on iOS (see graph).
A word of caution: this is by no means conclusive evidence. The same searches performed on a different day may yield different results. Furthermore, the trend appears to be limited to single journeys and to be more pronounced over relatively short distances. For the record, Uber said it does not have a policy of customizing trip prices based on a potential rider’s phone. He attributed the 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 a regular user, they inflate prices: expert
Experts suggest the disparities stem from how ride-hailing apps access hardware data that users must give consent to when installing an app.
C Ambigapathy, managing director of ride-hailing platform Fastrack in Chennai, said the central server could easily generate fare estimates tailored to the user’s device. “It’s a breeze for companies to change their prices based on material details while hiding behind the ‘dynamic pricing algorithm’ explanation,” he said.
P Ravikumar, former senior director of 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 like device type, app usage frequency, and search patterns to dynamically adjust pricing.
TOI was not able to independently verify whether this was indeed the case.
An intelligent transport systems expert involved in shaping the Union government’s aggregation policy said fare hikes are not limited to differences between phone models. He emphasized that this also applies to frequent users of the app and those who check fares repeatedly on the same device. “These platforms rely on user behavior patterns to dynamically adjust prices,” the expert said.
Ambigapathy pointed out that companies leverage past data to gauge user loyalty and trust. “Once they identify a repeat user, they inflate prices, confident that the user will eventually book, even as they wait for prices to drop, even if 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 driving patterns are consistent, users should not be discriminated against based on their device.”
