Shared mobility service passenger matching based on passenger attributes

A computing platform comprising at least one processor, memory, and a communication interface, wherein the memory stores instructions that, when executed by the at least one processor, cause the computing platform to:

• train an initial score machine-learning model using training data that correlates behavior data with indicators of particular personality traits to output a continuous value indicating an estimated strength of a personality trait for an occupant of a vehicle , wherein the training data includes training driving data;
• receive, by the at least one processor, via the communication interface, information about a user of a shared mobility service, wherein the user is a driver;
• generate, based on the information about the user, a user profile comprising user driving data and one or more user attributes based on appended data from third-party sources including at least one of surveys, historic purchase habits, observed travel patterns, or online browsing activity, wherein the one or more user attributes include at least one of a personality factor, an interest, a demographic factor, a preferred temperature, a preferred vehicle condition, or preference of genre of media, and wherein the one or more user attributes are numerical indicating a degree to which the user has a corresponding attribute and are determined based on rules that indicate a threshold number of instances determined from the appended data;
• extract features from the user attributes and using the extracted features as inputs to the initial score machine-learning model, wherein the initial score machine-learning model outputs a continuous value indicating an estimated strength of a personality trait for the user:
• determine a plurality of rides associated with the shared mobility service that are available to a passenger including a ride with the user;
• run a trained machine-learning model to determine a propensity score between the user and each of the plurality of rides, wherein a lookup is performed via accessing an application programming interface (API) and the trained machine-learning model matches attributes using a multi-dimensional propensity matrix indicating propensities between groups of attributes to determine propensity sub-scores for each pair of attributes corresponding to the user and each attribute of each ride, and wherein each propensity score is a combination of respective propensity sub-scores between the user and each ride;
• rank the plurality of rides based on the respective plurality of propensity scores;
• send a ride opportunity to pick up the user based on a highest rated ride;
• and
• train the initial score machine-learning model with the extracted features.

One or more non-transitory computer readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to:

• train an initial score machine-learning model using training data that correlates behavior data with indicators of particular personality traits to output a continuous value indicating an estimated strength of a personality trait for a driver or passenger, wherein the training data includes training driving data;
• receive, by the at least one processor, via the communication interface, information about a user of a shared mobility service, wherein the user is a driver;
• generate, based on the information about the user, a user profile comprising user driving data and one or more user attributes based on appended data from third-party sources including at least one of surveys, historic purchase habits, observed travel patterns, or online browsing activity, wherein the one or more user attributes include at least one of a personality factor, an interest, a demographic factor, a preferred temperature, a preferred vehicle condition, or preference of genre of media, and wherein the one or more user attributes are numerical indicating a degree to which the user has a corresponding attribute and are determined based on rules that indicate a threshold number of instances determined from the appended data;
• extract features from the user attributes and using the extracted features as inputs to the initial score machine-learning model, wherein the initial score machine-learning model outputs a continuous value indicating an estimated strength of a personality trait for the user;
• determine a plurality of rides associated with the shared mobility service that are available to a passenger including a ride with the user;
• run a trained machine-learning model to determine a propensity score between the user and each of the plurality of rides, wherein a lookup is performed via accessing an application programming interface (API) and the trained machine-learning model matches attributes using a multi-dimensional propensity matrix indicating propensities between groups of attributes to determine propensity sub-scores for each pair of attributes corresponding to the user and each attribute of each ride, and wherein each propensity score is a combination of respective propensity sub-scores between the user and each ride;
• rank the plurality of rides based on the respective plurality of propensity scores;
• send a ride opportunity to pick up the user based on a highest rated ride;
• and
• train the initial score machine-learning model with the extracted features.