Next Article in Journal
Smart Doll: Emotion Recognition Using Embedded Deep Learning
Next Article in Special Issue
Authentication with What You See and Remember in the Internet of Things
Previous Article in Journal
Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Frontal Face Images
Previous Article in Special Issue
False Data Injection Attack Based on Hyperplane Migration of Support Vector Machine in Transmission Network of the Smart Grid
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(9), 386;

Intersection Traffic Prediction Using Decision Tree Models

School of Computer Science, Guangzhou University, Guangzhou 510006, China
School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China
School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Current address: Guangzhou Higher Education Mega Center, 230 Wai Huan Xi Road, Guangzhou 510006, China.
Author to whom correspondence should be addressed.
Received: 19 August 2018 / Revised: 2 September 2018 / Accepted: 3 September 2018 / Published: 7 September 2018
Full-Text   |   PDF [1630 KB, uploaded 7 September 2018]   |  


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. View Full-Text
Keywords: traffic prediction; batch learning; online learning; decision tree; Fast Incremental Model Trees with Drift Detection (FIMT-DD) traffic prediction; batch learning; online learning; decision tree; Fast Incremental Model Trees with Drift Detection (FIMT-DD)

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Alajali, W.; Zhou, W.; Wen, S.; Wang, Y. Intersection Traffic Prediction Using Decision Tree Models. Symmetry 2018, 10, 386.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top