From trip metrics to corporate disclosures: predicting ride-hailing prices for longitudinal data using an integrated Cumulative Link Mixed Model and generative AI-based methodology

The ride-hailing platforms often attract severe criticisms for setting arbitrary ride prices, emphasising the importance of accurate fare prediction and key influencing factor identification. In this context, operational, financial, and temporal factors, along with semantic variables captured by Generative AI, can play an instrumental role in pricing. Motivated by this issue, we focus on the prediction of price categories for ride-hailing platforms Lyft and Uber between February 2019 and December 2024 with a dataset of 109,435 trips. We investigate the effect of operational, financial, and temporal attributes on the price classification. Further, we introduce two novel semantic variables, namely Price, Demand, and Tech Cooccurrence and Sentiment, captured from company annual reports using a leading GenAI tool, ChatGPT 4.0, and explore the impact of these variables on the price categories. We incorporate a Cumulative Link Mixed Model (CLMM)-based regression methodology to handle ordinal and non-continuous longitudinal data. We find that the financial attributes, sales tax and driver payment, and semantic variables always remain significant predictors for both, whereas the temporal attribute week of the year emerges as significant for Uber. Additionally, we explore how weather conditions, locations, and the COVID-19 pandemic influence the pricing. Finally, we propose several platform-specific recommendations. This work highlights the instrumental role of different variables in designing ride-hailing platforms’ price category classification. It suggests that the ride-hailing platforms should improve the semantic tone of their corporate disclosures and properly align their pricing strategies with corporate communications. It can result in a more transparent pricing strategy by reducing the consumer dissatisfaction caused by the arbitrariness of the surge price. Our work helps the policymakers understand the impact of important financial variables, such as sales tax, congestion surcharges, driver payments, etc., on the price category classification. It can be useful to devise more responsive and flexible regulatory measures. From the societal perspective, this work explains how GenAI can be useful to identify the semantic determinants influencing the ride-hailing platforms’ prices, along with operational and financial attributes. It can facilitate a more appropriate dynamic pricing strategy, which, in turn, can increase the consumer satisfaction and social welfare.