Abstract: In some implementations, a simulation platform may receive a request to perform a simulation of operations of a plurality of components. The simulation platform may receive first fidelity information identifying a first level of detail and functionality to be simulated for a first component of the plurality of components, wherein the first level of detail and functionality is part of a first plurality of levels of details and functionalities to be simulated for the first component. The simulation platform may receive second fidelity information identifying a second level of detail and functionality to be simulated for a second component of the plurality of components, wherein the second level of detail and functionality is part of a second plurality of levels of details and functionalities to be simulated for the second component.

Abstract: A vehicle location system may execute a model to simulate a movement of a passenger vehicle along a ride path. The vehicle location system may determine, based on executing the model, that a current ride location of the passenger vehicle is a first ride location. The vehicle location system may update a map application to indicate that the current ride location is the first ride location. The vehicle location system may receive sensor data generated by a sensor device located at a second ride location. The sensor data indicates that the passenger vehicle has been detected at the second ride location. The vehicle location system may provide the sensor data as an input to the model to update the current ride location to the second ride location. The vehicle location system may update, based on the sensor data, the map application to indicate the second ride location.

Abstract: A system includes a processor and a memory storing software code and a machine learning (ML) model. The software code is executed to receive contextual data samples each including raw data and a descriptive label, for each contextual data sample: search a database for a data pattern matching the raw data, determine, when the data pattern is detected, whether the data pattern is correlated with an anomalous event, and generate, when the correlation is determined, training data including a label identifying the anomalous event, and the raw data, the data pattern, or both, to provide one of multiple training data samples, wherein the training data samples describe anomalous events corresponding respectively to the raw data, the data pattern, or both. The software code is further executed to train the ML model, using the training data samples, to provide a trained predictive ML model configured to predict the anomalous events.