Rideshare Feature Engineering Questions

Designing and implementing predictive features for mobility and rideshare domains by transforming raw trip, geolocation, and contextual data into robust signals. Core topics include temporal features such as time of day and day of week, geospatial features such as distance, zone demand aggregates, and route patterns, as well as user and driver behavior features derived from historical interactions. Coverage also includes contextual signals such as weather and events, aggregation windows and time decay, encoding high cardinality categorical variables, handling missing and sparse data, feature importance and stability analysis, privacy and fairness considerations, and trade offs between precomputed and online feature computation including feature store usage and latency constraints.