Method of optimizing rider satisfaction
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.
1. 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. The method of claim 1 wherein the rider emotional state is further optimized by predicting an entertainment option for presenting to the at least one rider.
3. The method of claim 1 wherein social media data specific to the at least one rider is analyzed to determine at least one optimizing action likely to further optimize the rider emotional state.
4. The method of claim 3 wherein the at least one optimizing action includes at least one of: adjusting a routing plan to include passing points of interest that are of interest to the at least one rider, or presenting entertainment options.
5. The method of claim 1 wherein the social media data includes at least one of: social media posts or social media feeds.
6. The method of claim 1 wherein the social media data includes at least one of: discussion threads or chats.
7. The method of claim 1 wherein the keywords or phrases in the social media data include at least one of: an indication of a specific individual at a location, or an indication of a celebrity at a location.
8. The method of claim 1 wherein the social media data includes presence of a rare or transient phenomena at a location.
9. The method of claim 1 wherein the keywords or phrases in the social media data include an indication of at least one of: a commerce-related event, or an entertainment event at a location.
10. The method of claim 1 wherein the social media data includes weather conditions.
11. The method of claim 1 wherein the social media data includes a prediction of a crowd gathering for a potentially dangerous reason.
12. The method of claim 1 wherein the social media data includes at least one of: predictions of attendance at an event, or estimates of attendance at an event.
13. The method of claim 1 wherein the keywords or phrases in the social media data include an indication of modes of transportation used with an event.
14. The method of claim 1 wherein the reduced traffic congestion objective includes at least one of: an objective to reduce the traffic congestion, or an objective to avoid the traffic congestion.
15. The method of claim 1 wherein a characterization of an outcome of the optimizing the rider emotional state in the social media data is used as feedback to improve the optimizing.
16. The method of claim 15 wherein the feedback includes likes or dislikes of the outcome.
17. The method of claim 15 wherein the feedback includes social medial activity referencing the outcome.
18. The method of claim 15 wherein the feedback includes trending of social media activity referencing the outcome.
19. The method of claim 15 wherein the feedback includes hash tags associated with the outcome.
20. The method of claim 15 wherein the feedback includes ratings of the outcome or requests for the outcome.
21. 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.
22. The method of claim 21 wherein the reduced traffic congestion objective includes at least one of: an objective to reduce the traffic congestion, or an objective to avoid the traffic congestion.
23. The method of claim 21 wherein social media data specific to the at least one rider is analyzed to determine at least one optimizing action likely to optimize the at least one aspect of rider satisfaction.
24. The method of claim 23 wherein the at least one optimizing action includes at least one of: adjusting a routing plan to include passing points of interest that are of interest to the at least one rider, avoiding the traffic congestion predicted from the social media data, or presenting entertainment options.
25. The method of claim 21 wherein the social media data includes social media posts or social media feeds.
26. 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.
27. The method of claim 26 wherein the reduced traffic congestion objective includes at least one of: an objective to reduce the traffic congestion, or an objective to avoid the traffic congestion.
28. The method of claim 26 wherein social media data specific to the at least one rider is analyzed to determine at least one optimizing action likely to optimize the at least one aspect of rider satisfaction.
29. The method of claim 28 wherein the at least one optimizing action includes at least one of: adjusting a routing plan to include passing points of interest to the at least one rider, avoiding the traffic congestion predicted from the social media data, or presenting entertainment options.
30. The method of claim 26 wherein the social media data includes social media posts or social media feeds.