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.