Intersection traffic prediction using decision tree models

Alajali, Walaa, Zhou, Wei, Wen, Sheng and Wang, Yu 2018, Intersection traffic prediction using decision tree models, Symmetry, vol. 10, no. 9, Special Issue Symmetry and Asymmetry Applications for Internet of Things Security and Privacy, pp. 1-16, doi: 10.3390/sym10090386.

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Title Intersection traffic prediction using decision tree models
Author(s) Alajali, Walaa
Zhou, Wei
Wen, Sheng
Wang, Yu
Journal name Symmetry
Volume number 10
Issue number 9
Season Special Issue Symmetry and Asymmetry Applications for Internet of Things Security and Privacy
Article ID 386
Start page 1
End page 16
Total pages 16
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2018-09
ISSN 2073-8994
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
traffic prediction
batch learning
online learning
decision tree
Fast Incremental Model Trees with Drift Detection (FIMT-DD)
TARGET TRACKING
FLOW PREDICTION
INFORMATION
PROPAGATION
ALGORITHM
SECURE
Summary Traffic prediction is a critical task for intelligent transportation systems (ITS). Prediction at intersections is challenging as it involves various participants, such as vehicles, cyclists, and pedestrians. In this paper, we propose a novel approach for the accurate intersection traffic prediction by introducing extra data sources other than road traffic volume data into the prediction model. In particular, we take advantage of the data collected from the reports of road accidents and roadworks happening near the intersections. In addition, we investigate two types of learning schemes, namely batch learning and online learning. Three popular ensemble decision tree models are used in the batch learning scheme, including Gradient Boosting Regression Trees (GBRT), Random Forest (RF) and Extreme Gradient Boosting Trees (XGBoost), while the Fast Incremental Model Trees with Drift Detection (FIMT-DD) model is adopted for the online learning scheme. The proposed approach is evaluated using public data sets released by the Victorian Government of Australia. The results indicate that the accuracy of intersection traffic prediction can be improved by incorporating nearby accidents and roadworks information.
Language eng
DOI 10.3390/sym10090386
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018 by the authors.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30115727

Document type: Journal Article
Collections: School of Information Technology
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