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  "metadata_modified": "2023-08-09T09:42:49.680351",
            
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  "name": "grafana-dashboard-for-detector-loop",
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  "name": "grafana-dashboard-for-detector-loop",
            
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  "notes": "## Messung des Verkehrsaufkommens durch 
51
  "notes": "## Messung des Verkehrsaufkommens durch 
            
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Z\u00e4hlschleifen\r\nAn einem Gro\u00dfteil der Ampeln im Stadtgebiet 
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Z\u00e4hlschleifen\r\nAn einem Gro\u00dfteil der Ampeln im Stadtgebiet 
            
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von Ingolstadt sind Z\u00e4hlschleifen in die Fahrbahn eingelassen, 
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von Ingolstadt sind Z\u00e4hlschleifen in die Fahrbahn eingelassen, 
            
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die dar\u00fcberfahende Fahrzeuge erkennen und z\u00e4hlen. Die Daten 
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die dar\u00fcberfahende Fahrzeuge erkennen und z\u00e4hlen. Die Daten 
            
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werden in nahezu Echtzeit auf einem zentralen Rechner der Stadt 
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werden in nahezu Echtzeit auf einem zentralen Rechner der Stadt 
            
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abgelegt. Diese werden vom Forschrungsprojekt SAVeNoW abgerufen, 
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abgelegt. Diese werden vom Forschrungsprojekt SAVeNoW abgerufen, 
            
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verarbeitet und in eine performante, standardisierten Sensordatenbank 
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verarbeitet und in eine performante, standardisierten Sensordatenbank 
            
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\u00fcbertragen. Zur Anwendung kommen die Verkehrsz\u00e4hlungen zur 
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\u00fcbertragen. Zur Anwendung kommen die Verkehrsz\u00e4hlungen zur 
            
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Verbesserung der Genauigkeit der Verkehrssimulation. Mittels 
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Verbesserung der Genauigkeit der Verkehrssimulation. Mittels 
            
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minutengenauen Daten \u00fcber das Verkehrsaufkommens an den einzelnen 
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minutengenauen Daten \u00fcber das Verkehrsaufkommens an den einzelnen 
            
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Kreuzungen, kann ein akuraltes Abbild des Realverkehrs innerhalb der 
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Kreuzungen, kann ein akuraltes Abbild des Realverkehrs innerhalb der 
            
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Simulation geschaffen werden. Somit bilden Verkehrsdaten das Fundament 
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Simulation geschaffen werden. Somit bilden Verkehrsdaten das Fundament 
            
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f\u00fcr eine realistische Simulation. Mithilfe der Konzepte und Ideen 
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f\u00fcr eine realistische Simulation. Mithilfe der Konzepte und Ideen 
            
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zur Verbesserung des Verkehrs in Ingolstadt unkompliziert getestet und 
64
zur Verbesserung des Verkehrs in Ingolstadt unkompliziert getestet und 
            
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validiert werden k\u00f6nnen.\r\n\r\nIm Dashboard ist es m\u00f6glich, 
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validiert werden k\u00f6nnen.\r\n\r\nIm Dashboard ist es m\u00f6glich, 
            
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verschiedene Analysen und Dashboards zu sehen, die aus den Messungen 
66
verschiedene Analysen und Dashboards zu sehen, die aus den Messungen 
            
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der Sensoren resultieren.\r\n\r\n<img 
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der Sensoren resultieren.\r\n\r\n<img 
            
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src=\"https://savenow.github.io/sta-docs/_images/dashboard-hm.jpg\" 
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src=\"https://savenow.github.io/sta-docs/_images/dashboard-hm.jpg\" 
            
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alt=\"B3\" width=\"800px\">\r\n\r\nAbbildung 1: Vorschau Dashboard zur 
69
alt=\"B3\" width=\"800px\">\r\n\r\nAbbildung 1: Vorschau Dashboard zur 
            
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Verkehrsst\u00e4rke in Ingolstadt.\r\n\r\n<img 
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Verkehrsst\u00e4rke in Ingolstadt.\r\n\r\n<img 
            
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src=\"https://savenow.github.io/sta-docs/_images/dashboard.jpg\" 
71
src=\"https://savenow.github.io/sta-docs/_images/dashboard.jpg\" 
            
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alt=\"B3\" width=\"800px\">\r\n\r\nAbbildung 2: Vorschau Dashboard zur 
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alt=\"B3\" width=\"800px\">\r\n\r\nAbbildung 2: Vorschau Dashboard zur 
            
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Verkehrsst\u00e4rke an der Kreuzung 
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Verkehrsst\u00e4rke an der Kreuzung 
            
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N\u00fcrnberger-/Theodor-Heuss-Stra\u00dfe",
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N\u00fcrnberger-/Theodor-Heuss-Stra\u00dfe",
            
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dieMessung des Verkehrsaufkommens durch Z\u00e4hlschleifen zu 
120
dieMessung des Verkehrsaufkommens durch Z\u00e4hlschleifen zu 
            
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sehen.\r\nDas Dashboard zeigt: Aktueller Verkehr in den vergangenen 15 
121
sehen.\r\nDas Dashboard zeigt: Aktueller Verkehr in den vergangenen 15 
            
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min, Fahrzeuge je Kreuzung je 15 min gestapelt, Summe Fahrzeuge und 
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min, Fahrzeuge je Kreuzung je 15 min gestapelt, Summe Fahrzeuge und 
            
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contact Maintainer",
            
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  "state": "active",
            
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  "tags": [
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  "tags": [
            
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    {
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    {
            
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      "display_name": "DetectorLoop",
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      "display_name": "DetectorLoop",
            
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      "id": "ea8022dd-0463-4e76-b510-5d35a1544703",
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      "id": "ea8022dd-0463-4e76-b510-5d35a1544703",
            
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      "name": "DetectorLoop",
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      "name": "DetectorLoop",
            
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      "state": "active",
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "Grafana",
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      "display_name": "Grafana",
            
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      "id": "a3f2cab5-9e8a-4a04-89b6-82f44580ce4f",
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      "id": "a3f2cab5-9e8a-4a04-89b6-82f44580ce4f",
            
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      "name": "Grafana",
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      "name": "Grafana",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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    },
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    {
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    {
            
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      "display_name": "dashboard",
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      "display_name": "dashboard",
            
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      "id": "7125e404-6a43-4d28-9db5-b60a301ea501",
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      "id": "7125e404-6a43-4d28-9db5-b60a301ea501",
            
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      "name": "dashboard",
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      "name": "dashboard",
            
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      "state": "active",
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Grafana Dashboard for Detector Loop",
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  "title": "Grafana Dashboard for Detector Loop",
            
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  "type": "dataset",
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  "version": ""
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  "version": ""
            
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}
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}