Data Analysis, Modelling and Optimisation of Carpooling Service in a Ridesharing App

This detailed study interrogates the paradigm of shared rides within the context of existing ride-hailing services, offering both a historical evolution and a systematic approach to optimizing this emerging transportation model. Beginning with a historical overview, the first chapter delineates the development of shared rides and substantiates their importance due to environmental benefits and market efficiency gains. Various real-world applications by ride-hailing companies, prominently featuring a case study from Russia, highlight industry practices and user experiences, delivering a comprehensive portrayal of the current landscape. The second chapter transitions into a methodological exploration aimed at refining shared ride efficiencies. Focusing sharply on data analysis tools and methods, this segment meticulously details the use and selection processes of data querying languages like YQL and Clickhouse, combined with data visualization tools such as Datalens and Python's libraries, underscoring their roles in enhancing data interpretability. The author further demarcates the realms of exploratory and predictive data analysis while elaborating on model-building techniques, spanning from conceptual definitions to verification methods through A/B and Switchback experiments. Overall, this work not only expounds on the theoretical underpinnings and practical implementations of shared rides within the taxi marketplace but also extends a granular analysis toolkit for optimizing these services.