Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
On Predicting the Taxi-Passenger Demand: A Real-Time Approach
verfasst von : Luis Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luis Damas
Erschienen in: Progress in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Informed driving
is becoming a key feature to increase the sustainability of taxi companies. Some recent works are exploring the data broadcasted by each vehicle to provide live information for decision making. In this paper, we propose a method to employ a learning model based on historical GPS data in a real-time environment. Our goal is to predict the spatiotemporal distribution of the Taxi-Passenger demand in a short time horizon. We did so by using learning concepts originally proposed to a well-known online algorithm: the
perceptron
[1]. The results were promising: we accomplished a satisfactory performance to output the next prediction using a short amount of resources.