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Prognose von Parkplatzdaten

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Management digitaler Plattformen

Part of the book series: Informationsmanagement und digitale Transformation ((INDITR))

Zusammenfassung

Die Suche nach freien Parkplätzen am Straßenrand ist aufwändig und verursacht einen signifikanten Teil des innerstädtischen Verkehrs. Existierende Lösungsansätze haben einige Nachteile, da sie zum Beispiel mit hohen Kosten verbunden sind oder eine aktive Nutzerbasis voraussetzen. Dieses Kapitel befasst sich mit der Vorhersage von freien Parkplätzen basierend auf öffentlich zugänglichen Daten, auf die kosteneffizient zugegriffen werden kann. Geeignete Datenkategorien werden basierend auf einer Literaturstudie identifiziert. Anschließend wird ein solcher Service beispielhaft mittels eines neuronalen Netzes umgesetzt. Die Relevanz der einzelnen Datenkategorien wird basierend auf 2779 Datensätzen evaluiert. Die Ergebnisse zeigen, dass Informationen zu Wochentag, Standort, Temperatur und Uhrzeit die Vorhersage stark verbessern, wohingegen Informationen zu Events, Verkehr, Feiertagen und Regenmenge nur von untergeordneter Wichtigkeit sind. Dieses Kapitel kategorisiert bestehende Lösungen zur Unterstützung der Parkplatzsuche und zeigt, dass einfach zugängliche Daten ausreichen, um mithilfe eines Service die Verfügbarkeit von Parkmöglichkeiten vorherzusagen.

The search for curb-side parking spaces is a time-consuming effort and responsible for a significant part of urban traffic. Existing solutions have several disadvantages as they are for example costly to operate or do require an active user base. This chapter deals with the prediction of free parking spaces using publicly available data sources that can be accessed in a cost-efficient way. Suitable categories of data sources are identified by performing a literature-research. Subsequently, an exemplary service based on an artificial neural network is developed. The particular relevance of each category of data sources is evaluated using a training data set with 2779 entries. Results show that weekday, location, temperature and time are improving the prediction result, while events, traffic, public holidays and the amount of rainfall have little influence on the prediction model. This chapter furthermore categorizes existing solutions and shows that easily accessible data sources are sufficient to predict the availability of curb-side parking spaces.

Aufbauend auf der bereits von Pflügler et al. (2016a) durchgeführten Studie zum Thema Prognose von Parkplatzdaten.

Das Forschungsprojekt ExCELL wurde mit Mitteln des Bundesministeriums für Wirtschaft und Energie (BMWi) gefördert (Förderkennzeichen: 01MD15001D und 01MD15001E).

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Author information

Authors and Affiliations

  1. fortiss GmbH, München, Deutschland

    Marcel Altendeitering

  2. Lehrstuhl für Wirtschaftsinformatik, Technische Universität München, Garching, Deutschland

    Christoph Pflügler, Maximilian Schreieck, Manuel Wiesche & Helmut Krcmar

  3. Institut für Verkehrstelematik, Technische Universität Dresden, Dresden, Deutschland

    Sven Fröhlich

Authors
  1. Marcel Altendeitering
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  2. Christoph Pflügler
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  3. Maximilian Schreieck
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  4. Sven Fröhlich
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  5. Manuel Wiesche
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  6. Helmut Krcmar
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Corresponding author

Correspondence to Marcel Altendeitering .

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Editors and Affiliations

  1. Lehrstuhl für Wirtschaftsinformatik I 17, Technische Univ München, Garching bei München, Germany

    Manuel Wiesche

  2. Germany, Berlin, Germany

    Petra Sauer

  3. Dresden, Dresden, Germany

    Jürgen Krimmling

  4. Garching, Garching, Germany

    Helmut Krcmar

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Altendeitering, M., Pflügler, C., Schreieck, M., Fröhlich, S., Wiesche, M., Krcmar, H. (2018). Prognose von Parkplatzdaten. In: Wiesche, M., Sauer, P., Krimmling, J., Krcmar, H. (eds) Management digitaler Plattformen. Informationsmanagement und digitale Transformation. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-21214-8_13

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  • DOI: https://doi.org/10.1007/978-3-658-21214-8_13

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