NDLI: Towards an uncertainty aware short-term travel time prediction using GPS bus data: Case study in Dublin

Content Provider	IEEE Xplore Digital Library
Author	Baptista, A.T. Bouillet, E.P. Pompey, P.
Copyright Year	2012
Description	Author affiliation: KTH Royal Institute of Technology, Sweden (Baptista, A.T.) \|\| IBM Dublin Research Laboratory, Ireland (Bouillet, E.P.; Pompey, P.)
Abstract	In this paper we propose and study the performances of a bus travel times prediction model using real bus location data from the city of Dublin. The proposed prediction model uses a modified version of the K-Nearest Neighbors algorithm, KNN, algorithm and exhibits a significant improvement over the baseline KNN. We also investigate the benefits of decomposing travel times in three components: running time, dwell time at bus stops and time stopped at traffic lights. We discuss that most of the uncertainty on the travel times comes from time spent at bus stops and traffic lights, and prediction of running time only is much improved due to the reduced uncertainty at bus stops and traffic lights. Finally we show the need of a prediction algorithm for time spent at bus stops and traffic lights that added to the prediction of running time would allow for an uncertainty aware travel time predictor.
Starting Page	1620
Ending Page	1625
File Size	735077
Page Count	6
File Format	PDF
ISBN	9781467330640
ISSN	21530009
e-ISBN	9781467330633
e-ISBN	9781467330626
DOI	10.1109/ITSC.2012.6338633
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prediction algorithms Uncertainty Global Positioning System Predictive models Time series analysis Vehicles Cities and towns
Content Type	Text
Resource Type	Article

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