Inhaltszusammenfassung

This paper presents a platform that uses open urban data and machine learning to predict parking space occupancy in Mainz, Germany. Our goal is to support urban mobility by delivering real-time weather data and parking availability forecasts. We developed and evaluated several machine learning models based on their predictive accuracy. To complement the backend, we designed a visual analytics prototype that supports decision-making. The system provides a user-friendly interface that helps cit...This paper presents a platform that uses open urban data and machine learning to predict parking space occupancy in Mainz, Germany. Our goal is to support urban mobility by delivering real-time weather data and parking availability forecasts. We developed and evaluated several machine learning models based on their predictive accuracy. To complement the backend, we designed a visual analytics prototype that supports decision-making. The system provides a user-friendly interface that helps citizens locate available parking more efficiently and reduces traffic caused by parking searches. A user study demonstrates that the platform effectively integrates data-driven forecasts with intuitive visualizations of uncertainty, enhancing user understanding and trust. We designed the prototype to be scalable and adaptable for broader applications in intelligent urban infrastructure.» weiterlesen» einklappen

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Klassifikation

DDC Sachgruppe:
Informatik