System, method, and computer program product for generating real-time predictions for vehicle transportation requests using machine learning

A system comprising at least one processor programmed and/or configured to:

• generate a transportation categorization for each consumer of a plurality of consumers based at least partially on historic transaction data comprising a plurality of transactions by each customer of the plurality of consumers;
• process a plurality of new transactions by the plurality of consumers, each new transaction of the plurality of new transactions associated with a point-of-sale terminal having a location;
• generate a plurality of clusters of transaction activity based on a plurality of point-of-sale terminals associated with the plurality of new transactions;
• generate, with a machine-learning classification model, at least one vehicle transportation prediction based on the transportation categorization for at least a portion of consumers of the plurality of consumers and the plurality of clusters of transaction activity, the at least one vehicle transportation prediction representing a likelihood that at least one consumer will request vehicle transportation subsequent to the new transaction;
• and
• generate a supply map interface comprising at least one visual identification based on the at least one vehicle transportation prediction.

A computer program product comprising at least one non-transitory computer-readable medium including program instructions, which, when executed by at least one processer, cause the at least one processor to:

• generate a transportation categorization for each consumer of a plurality of consumers based at least partially on historic transaction data comprising a plurality of transactions by each customer of the plurality of consumers;
• process a plurality of new transactions by the plurality of consumers, each new transaction of the plurality of new transactions associated with a point-of-sale terminal having a location;
• generate a plurality of clusters of transaction activity based on a plurality of point-of-sale terminals associated with the plurality of new transactions;
• generate, with a machine-learning classification model, at least one vehicle transportation prediction based on the transportation categorization for at least a portion of consumers of the plurality of consumers and the plurality of clusters of transaction activity, the at least one vehicle transportation prediction representing a likelihood that at least one consumer will request vehicle transportation subsequent to the new transaction;
• and
• generate a supply map interface comprising at least one visual identification based on the at least one vehicle transportation prediction.