Apparatus and methods for predicting slipping events for micromobility vehicles

A non-transitory computer-readable storage medium having computer program code instructions stored therein, the computer program code instructions, when executed by at least one processor, cause the at least one processor to:

• receive input data indicating a target location and including contextual data associated with the target location;
• and
• cause a machine learning model to generate output data as a function of the input data, wherein the output data indicate a likelihood in which a target micromobility vehicle will slip at the target location, wherein the machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which micromobility vehicles have slipped, wherein the historical data indicate slip-inducing objects within locations of the events, proximity of sources of the slip-inducing objects relative to the locations, and one or more factors that cause the slip-inducing objects to be disposed within the locations, wherein the slip-inducing objects are leaves, garbage, or a combination thereof, wherein the sources are plants, waste containers, or a combination thereof, and wherein the one or more factors is defined by a season in which each of the events has occurred, timings in which the waste containers are emptied, or a combination thereof.