Intelligent transportation method and system

Abstract

The present disclosure relates to transportation and related methods and systems, including intelligent digital twin systems that create, manage and provide digital twins of transportation systems using vehicle operating conditions, identity management systems, sensor data and other data, quantum computing methods and systems including a set of quantum computing services, and biology-based systems and methods for communicating and / or processing data.

Claims

1. 1. A method for prioritizing predictive model data streams, comprising: receiving, by a first device, social media data sourced from a plurality of social media sources as affecting a transportation system; classifying, by the first device, the social media data based on a set of model parameters for each of a plurality of predictive models, each predictive model being trained to predict future data values ​​for the transportation system; selecting, by the first device, at least one predictive model data stream from the categorized social media data; parameterizing, by the first device, a predictive model using a set of model parameters included in the selected at least one predictive model stream; and and predicting, by the first device, at least one future data value of the transportation system using the parameterized predictive model.

2. 10. The method of claim 1, wherein selecting at least one assignment of a priority by the first device to each of a plurality of predictive model data streams included in the social media data and selecting the at least one predictive model stream are performed based on the priority assigned to each of the plurality of predictive model data streams.

3. 3. The method of claim 2, wherein the selected at least one predictive model data stream is associated with a highest priority among the plurality of predictive model data streams.

4. 3. The method of claim 2, wherein the selecting includes suppressing at least one of the non-selected predictive model data streams based on the priority assigned to each of the at least one non-selected predictive model data stream.

5. The method of claim 1 , further comprising adjusting an operational state of the transportation system based on the future data value of the transportation system.

6. 1. A system for transportation, comprising: an expert system for selecting a configuration of a vehicle, the configuration including at least one parameter selected from the group consisting of a vehicle parameter, a user experience parameter, and a combination thereof; the expert system includes a first data system and a second data system; The first data system receiving a plurality of data values ​​of a data stream, the data values ​​comprising sensor data collected from one or more sensor devices; generating a predictive model for predicting the at least one parameter based on the received plurality of data values, wherein generating the predictive model includes determining a plurality of model parameters; and transmitting the plurality of model parameters; The second data system receiving the plurality of model parameters; parameterizing a predictive model based on the plurality of model parameters; and selecting the at least one parameter based on the parameterized predictive model.

7. 7. The system of claim 6, wherein the predictive model includes a behavioral analysis model, and the at least one parameter is based on predicted behavior of a rider of the vehicle.

8. 7. The system of claim 6, wherein the predictive model comprises a classification model, and the at least one parameter comprises a predicted future state of the vehicle based on classification data received from one or more sensor devices associated with the vehicle.

9. 7. The system of claim 6, wherein the data stream comprises a video stream received from a camera associated with the vehicle, and the plurality of data values ​​comprises one or more motion vectors extracted from the video stream received from the camera.

10. The expert system comprises: a first neural network configured to operate to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet of Things device while the vehicle is being operated; and a second neural network configured to optimize the at least one parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider aboard the vehicle, and information relating vehicle operation to an effect on the rider's state.

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