Artificial intelligence-based pricing, mapping, and emissions control system and method for optimized inner-city mobility

A method for dynamic optimization of a transportation network, comprising the steps of:

• acquiring and processing real-time and historical data from a plurality of sensors and external data sources to generate a multi-dimensional representation of the transportation network state;
• generating a dynamic pricing model for segments of the transportation network based on the multi-dimensional network state representation and a set of pricing policies;
• tracking and predicting the behavior of users and vehicles in the transportation network using real-time data and machine learning models;
• generating a set of optimized route and pricing options for users by applying a multi-objective optimization algorithm to the dynamic pricing model and the predicted user and vehicle behavior, while satisfying a set of feasibility constraints;
• presenting the optimized route and pricing options to users via an interactive user interface and receiving user feedback;
• adapting the dynamic pricing model, the user and vehicle behavior models, and the optimization algorithm based on the received user feedback and the evolving transportation network state;
• and
• managing and orchestrating the data flows and interactions between the system components using a centralized or decentralized architecture.

Non-transitory, computer-readable storage media having computer-executable instructions embodied thereon that, when executed by one or more processors of a computing system employing an artificial intelligence based platform for dynamic optimization of a transportation network, cause the computing system to:

• acquire and process real-time and historical data from a plurality of sensors and external data sources to generate a multi-dimensional representation of the transportation network state;
• generate a dynamic pricing model for segments of the transportation network based on the multi-dimensional network state representation and a set of pricing policies;
• track and predict the behavior of users and vehicles in the transportation network using real-time data and machine learning models;
• generate a set of optimized route and pricing options for users by applying a multi-objective optimization algorithm to the dynamic pricing model and the predicted user and vehicle behavior, while satisfying a set of feasibility constraints;
• present the optimized route and pricing options to users via an interactive user interface and receive user feedback;
• adapt the dynamic pricing model, the user and vehicle behavior models, and the optimization algorithm based on the received user feedback and the evolving transportation network state;
• and
• manage and orchestrate the data flows and interactions between the system components using a centralized or decentralized architecture.