Intelligent transportation systems

A computer-implemented method of optimizing a set of values for in-vehicle entertainment parameters of at least one vehicle of a transportation system, the method comprising:

• classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting the transportation system, wherein an artificial intelligence system is in communication with the at least one vehicle, the artificial intelligence system operative on at least one processor having access to a non-transitory storage medium that stores computer executable instructions to be executed by the at least one processor, the artificial intelligence system including the hybrid neural network;
• predicting, using a second neural network of the hybrid neural network, a conflict between a first set of values of the in-vehicle entertainment parameters that correspond to a first rider state of a first rider in the at least one vehicle, and a second set of values of the in-vehicle entertainment parameters that correspond to a second rider state of a second rider in the at least one vehicle based on the classified social media data, wherein the second rider is exclusive of the first rider,
• wherein each of the first rider state and the second rider state includes an entertainment state;
• and
• optimally resolving, by a third neural network of the hybrid neural network, the conflict between the first set of values of the in-vehicle entertainment parameters and the second set of values of the in-vehicle entertainment parameters by at least one of a model, a rule, or machine learning to:
• i) generate an optimized set of values of the in-vehicle entertainment parameters to achieve an optimized combination of the first rider state and the second rider state, wherein the optimized set of values of the in-vehicle entertainment parameters affects entertainment content that is to be provided via audio or video in the at least one vehicle and includes content relating to at least one of:
• music, news, advertising, movies, entertainment games, or sports, and
• ii) adjust the first and second sets of values of the in-vehicle entertainment parameters to achieve the optimized set of values of the in-vehicle entertainment parameters and thereby achieve the generated optimized combination of the first rider state and the second rider state.

A machine readable non-transitory storage medium having machine readable instructions stored thereon, the instructions executable by an artificial intelligence system operative on at least one processor having access to the non-transitory storage medium, the instructions to execute a method of optimizing an entertainment state of at least one vehicle of a transportation system, the method comprising:

• classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting the transportation system, wherein the artificial intelligence system is in communication with the at least one vehicle, the artificial intelligence system including the hybrid neural network;
• predicting, using a second neural network of the hybrid neural network, a conflict between a first set of values of in-vehicle entertainment parameters that correspond to a first rider state of a first rider in the at least one vehicle, and a second set of values of in-vehicle entertainment parameters that correspond to a second rider state of a second rider in the at least one vehicle that is exclusive of the first rider based on the classified social media data,
• wherein each of the first rider state and the second rider state includes an entertainment state, and
• wherein the social media data is classified based on at least one of interests of the first rider and the second rider or transportation objectives of the first rider and the second rider;
• and
• optimally resolving, by a third neural network of the hybrid neural network, the conflict between the first set of values and the second set of values of the in-vehicle entertainment parameters by at least one of a model, a rule, or machine learning to generate an optimized set of values of the in-vehicle entertainment parameters to achieve an optimized combination of the first rider state and the second rider state, and adjusting the set of values of the in-vehicle entertainment parameters to achieve the generated optimized set of values of the in-vehicle entertainment parameters and thereby achieve the optimized combination of the first rider state and the second rider state, wherein the optimized set of values of the in-vehicle entertainment parameters affects entertainment content that is to be provided via audio or video in the at least one vehicle and includes content relating to at least one of music, news, advertising, movies, entertainment games, or sports.