CHENNAI: It’s a theory doing the rounds on social media, but is it true? Some commuters have noticed strange discrepancies in taxi fares displayed on Android devices and iPhones simultaneously for the same trip. , wondering if the ride-hailing app’s pricing algorithm is programmed to charge Apple users higher prices.
TOI used iPhone and Android devices simultaneously to search for rides from three locations in Chennai to the same destination. In each case, the displayed fare was higher on iOS (see figure).
Please note – this is by no means conclusive evidence. The same search on different days may show different results. This pattern was also limited to solo passengers and seemed to be more pronounced over relatively short distances. For the record, Uber said it does not have a policy of personalizing trip prices based on a prospective ride’s cell phone. If there is a difference, the difference may be due to factors such as estimated time, distance, or real-time demand for taxis in a particular area. Ola did not respond to queries from TOI.
Companies increase fares when they identify regular passengers: expert
Experts suggest the disparity stems from the way ride-hailing apps access hardware data that users must consent to when installing the app.
C. Ambigapathy, managing director of Chennai-based ride-hailing platform Fastrac, said a central server can easily generate fare quotes tailored to a user’s device. “It’s child’s play for companies to hide their “dynamic pricing algorithms” while adjusting fares based on hardware details,” he said.
P. Ravikumar, former senior director of the Center for Advanced Computing Development (C-DAC) in Thiruvananthapuram, said aggregators are using machine learning frameworks (Google Cloud AI and Azure ML) to improve their pricing algorithms. Said is known to use rapid development tools such as. These tools can incorporate variables such as device type, app usage frequency, and search patterns to dynamically adjust fares.
TOI could not independently verify whether this was indeed the case.
An expert on intelligent transportation systems who helped develop the federal government’s aggregator policy said rising fares are not limited to differences between cell phone models. He noted that this also applies to users who use the app frequently or 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 use historical data to measure user loyalty and trust. “Once they identify a regular user, they can lower the price with confidence that that user will eventually book, even if they wait for the price to drop, even if they don’t actually lower the price. I will lift it up.”
Ravikumar said it’s time for companies to be transparent about their pricing models. “If factors such as estimated time, distance, and ride mode are consistent, users should not face discrimination based on their device.”
