mymetro
  • Introduction
  • Technical section
    • Architecture and Mechanism of mymetro
    • Simulations Module: AI Training and Route Optimization
    • X/Twitter integration
  • Vision
    • Roadmap
    • Tokemomics
    • Official links
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  1. Technical section

Architecture and Mechanism of mymetro

PreviousIntroductionNextSimulations Module: AI Training and Route Optimization

Last updated 2 months ago

Data Collection and Processing:

  • Data Sources: Integration with APIs of transportation companies, IoT sensors, weather services, and other external sources.

  • ETL Processes: Extraction, Transformation, and Loading of data into a centralized repository for further analysis.

AI and Machine Learning:

  • Predictive Models: Utilization of regression models and neural networks to forecast congestion and delays.

  • Dynamic Route Optimization: Application of machine learning algorithms to update routes in real-time based on current data.

Route Optimization:

  • Routing Algorithms: Implementation of algorithms to find optimal routes considering multiple factors (travel time, congestion, weather conditions).

  • Personalization: Customization of routes based on individual user preferences, such as minimizing transfers or selecting less congested routes.