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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 
            
52
Z\u00e4hlschleifen\r\nAn einem Gro\u00dfteil der Ampeln im Stadtgebiet 
52
Z\u00e4hlschleifen\r\nAn einem Gro\u00dfteil der Ampeln im Stadtgebiet 
            
53
von Ingolstadt sind Z\u00e4hlschleifen in die Fahrbahn eingelassen, 
53
von Ingolstadt sind Z\u00e4hlschleifen in die Fahrbahn eingelassen, 
            
54
die dar\u00fcberfahende Fahrzeuge erkennen und z\u00e4hlen. Die Daten 
54
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, 
56
abgelegt. Diese werden vom Forschrungsprojekt SAVeNoW abgerufen, 
            
57
verarbeitet und in eine performante, standardisierten Sensordatenbank 
57
verarbeitet und in eine performante, standardisierten Sensordatenbank 
            
58
\u00fcbertragen. Zur Anwendung kommen die Verkehrsz\u00e4hlungen zur 
58
\u00fcbertragen. Zur Anwendung kommen die Verkehrsz\u00e4hlungen zur 
            
59
Verbesserung der Genauigkeit der Verkehrssimulation. Mittels 
59
Verbesserung der Genauigkeit der Verkehrssimulation. Mittels 
            
60
minutengenauen Daten \u00fcber das Verkehrsaufkommens an den einzelnen 
60
minutengenauen Daten \u00fcber das Verkehrsaufkommens an den einzelnen 
            
61
Kreuzungen, kann ein akuraltes Abbild des Realverkehrs innerhalb der 
61
Kreuzungen, kann ein akuraltes Abbild des Realverkehrs innerhalb der 
            
62
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 
            
65
validiert werden k\u00f6nnen.\r\n\r\nIm Dashboard ist es m\u00f6glich, 
65
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 
            
67
der Sensoren resultieren.\r\n\r\n<img 
67
der Sensoren resultieren.\r\n\r\n<img 
            
68
src=\"https://savenow.github.io/sta-docs/_images/dashboard-hm.jpg\" 
68
src=\"https://savenow.github.io/sta-docs/_images/dashboard-hm.jpg\" 
            
69
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 
            
70
Verkehrsst\u00e4rke in Ingolstadt.\r\n\r\n<img 
70
Verkehrsst\u00e4rke in Ingolstadt.\r\n\r\n<img 
            
71
src=\"https://savenow.github.io/sta-docs/_images/dashboard.jpg\" 
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src=\"https://savenow.github.io/sta-docs/_images/dashboard.jpg\" 
            
72
alt=\"B3\" width=\"800px\">\r\n\r\nAbbildung 2: Vorschau Dashboard zur 
72
alt=\"B3\" width=\"800px\">\r\n\r\nAbbildung 2: Vorschau Dashboard zur 
            
73
Verkehrsst\u00e4rke an der Kreuzung 
73
Verkehrsst\u00e4rke an der Kreuzung 
            
74
N\u00fcrnberger-/Theodor-Heuss-Stra\u00dfe",
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N\u00fcrnberger-/Theodor-Heuss-Stra\u00dfe",
            
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Messung des Verkehrsaufkommens im gesamten Stadtgebiet durch 
139
Messung des Verkehrsaufkommens im gesamten Stadtgebiet durch 
            
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Detektorschleifen, Echtzeitanalyse der Kreuzung 
140
Detektorschleifen, Echtzeitanalyse der Kreuzung 
            
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Hindenburg-/Ringlerstra\u00dfe, Wetterdatenerfassung.",
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