Time-Aware Multivariate Nearest Neighbor Regression Methods for Traffic Flow Prediction

Traffic flow prediction is a fundamental functionality of intelligent transportation systems. After presenting the state of the art, we focus on nearest neighbor regression methods, which are data-driven algorithms that are effective yet simple to implement. We try to strengthen their efficacy in tw...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 16; no. 6; pp. 3393 - 3402
Main Authors: Dell'Acqua, Pietro, Bellotti, Francesco, Berta, Riccardo, De Gloria, Alessandro
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Bayes methods
Bayesian networks
Context awareness
multivariate analysis
nearest neighbor regression
Nearest neighbor searches
Pattern recognition
pattern recognition algorithm
Prediction algorithms
Regression analysis
Traffic flow
Traffic flow prediction
Traffic flow prediction
nearest neighbor regression
context awareness
Bayesian networks
multivariate analysis
pattern recognition algorithm
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:Traffic flow prediction is a fundamental functionality of intelligent transportation systems. After presenting the state of the art, we focus on nearest neighbor regression methods, which are data-driven algorithms that are effective yet simple to implement. We try to strengthen their efficacy in two ways that are little explored in literature, i.e., by adopting a multivariate approach and by adding awareness of the time of the day. The combination of these two refinements, which represents a novelty, leads to the definition of a new class of methods that we call time-aware multivariate nearest neighbor regression (TaM-NNR) algorithms. To assess this class, we have used publicly available traffic data from a California highway. Computational results show the effectiveness of such algorithms in comparison with state-of-the-art parametric and non-parametric methods. In particular, they consistently perform better than their corresponding standard univariate versions. These facts highlight the importance of context elements in traffic prediction. The ideas presented here may be further investigated considering more context elements (e.g., weather conditions), more complex road topologies (e.g., urban networks), and different types of prediction methods.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2015.2453116