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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 
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  "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 
            
55
werden in nahezu Echtzeit auf einem zentralen Rechner der Stadt 
55
werden in nahezu Echtzeit auf einem zentralen Rechner der Stadt 
            
56
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 
57
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 
59
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 
62
Simulation geschaffen werden. Somit bilden Verkehrsdaten das Fundament 
            
63
f\u00fcr eine realistische Simulation. Mithilfe der Konzepte und Ideen 
63
f\u00fcr eine realistische Simulation. Mithilfe der Konzepte und Ideen 
            
64
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, 
            
66
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\" 
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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 
            
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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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}