Back to results
US 11,694,288 B2Granted
Method of optimizing rider satisfaction
AssigneeStrong Force TP Portfolio 2022, LLC
InventorCharles Howard Cella
Life of this patent
Estimating the term…
Abstract
A method of optimizing rider satisfaction includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. The method still further includes optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.
Claims30
A method of optimizing rider satisfaction, the method comprising:
- classifying, using a first neural network of a hybrid neural network, keywords or phrases in social media data sourced from a plurality of social media sources as indicative of an effect on traffic congestion in a transportation system, wherein the transportation system includes a set of roadways and at least one vehicle;
- predicting, using a second neural network of the hybrid neural network, a rider emotional state affected by the effect on the traffic congestion in the transportation system derived from the keywords or phrases in the social media data classified as indicative of an effect on the traffic congestion in the transportation system;
- optimizing, using a third neural network of the hybrid neural network, the rider emotional state for at least one rider occupying the at least one vehicle in the transportation system, wherein the optimizing the rider emotional state for the at least one rider occupying the at least one vehicle in the transportation system includes optimizing a planned route for the at least one vehicle occupied by the at least one rider to achieve a reduced traffic congestion objective;
- and
- communicating the planned route via an in-vehicle navigation interface to facilitate following the planned route.
2 more elements
A method of optimizing rider satisfaction, the method comprising:
- classifying, using a first neural network of a hybrid neural network, keywords or phrases in social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system, wherein the effect on the transportation system includes an effect on traffic congestion, and wherein the transportation system includes a set of roadways and at least one vehicle;
- predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by the effect on the transportation system derived from the keywords or phrases in the social media data classified as indicative of the effect on the transportation system, wherein the at least one aspect of rider satisfaction includes a rider emotional state;
- optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying the at least one vehicle in the transportation system, wherein the optimizing the at least one aspect of rider satisfaction includes optimizing the rider emotional state for the at least one rider occupying the at least one vehicle in the transportation system, wherein the optimizing the rider emotional state for the at least one rider occupying the at least one vehicle in the transportation system includes optimizing a planned route for the at least one vehicle occupied by the at least one rider to achieve a reduced traffic congestion objective;
- and
- autonomously following the planned route, wherein the at least one vehicle is a semi-autonomous vehicle that is automatically routed.
2 more elements
A method of optimizing rider satisfaction, the method comprising:
- classifying, using a first neural network of a hybrid neural network, keywords or phrases in social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system, wherein the effect on the transportation system includes an effect on traffic congestion, and wherein the transportation system includes:
- a set of roadways, traffic control lights, and at least one vehicle;
- predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by the effect on the transportation system derived from the keywords or phrases in the social media data classified as indicative of the effect on the transportation system, wherein the at least one aspect of rider satisfaction includes a rider emotional state;
- and
- optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying the at least one vehicle in the transportation system, wherein the optimizing the at least one aspect of rider satisfaction includes optimizing the rider emotional state for the at least one rider occupying the at least one vehicle in the transportation system, wherein the optimizing the rider emotional state for the at least one rider occupying the at least one vehicle in the transportation system includes optimizing traffic flow in the transportation system by adjusting operation of the traffic control lights to achieve a reduced traffic congestion objective.
2 more elements
Dependencies are inferred from the claim text.