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Titel:

Predicting the Availability of Parking Spaces with Publicly Available Data

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Pflügler, Christoph; Köhn, Thomas; Schreieck, Maximilian; Wiesche, Manuel; Krcmar, Helmut
Seitenangaben Beitrag:
361-373
Abstract:
Searching for parking spaces on the street causes a significant part of the urban traffic and results in extra costs for the drivers in terms of time and fuel consumption. Existing approaches to predict the availability of parking spaces have significant drawbacks as they are either expensive or rely on the users´ information. This article deals with the prediction of the parking situation based on publicly available data that can be accessed cost-efficiently. Suitable categories of data are ide...     »
Stichworte:
Smart Parking, Public Data, Smart City, Parking Prediction, Parking, Neuronal Network
Kongress- / Buchtitel:
Lecture Notes in Informatics (LNI)
Kongress / Zusatzinformationen:
INFORMATIK 2016
Jahr:
2016
Leitbild:
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