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A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis
Joint Authors
Shang, Qiang
Tan, Derong
Gao, Song
Feng, Linlin
Source
Journal of Advanced Transportation
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-20
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Predicting traffic incident duration is important for effective and real-time traffic incident management (TIM), which helps to minimize traffic congestion, environmental pollution, and secondary incident related to this incident.
Traffic incident duration prediction methods often use more input variables to obtain better prediction results.
However, the problems that available variables are limited at the beginning of an incident and how to select significant variables are ignored to some extent.
In this paper, a novel prediction method named NCA-BOA-RF is proposed using the Neighborhood Components Analysis (NCA) and the Bayesian Optimization Algorithm (BOA)-optimized Random Forest (RF) model.
Firstly, the NCA is applied to select feature variables for traffic incident duration.
Then, RF model is trained based on the training set constructed using feature variables, and the BOA is employed to optimize the RF parameters.
Finally, confusion matrix is introduced to measure the optimized RF model performance and compare with other methods.
In addition, the performance is also tested in the absence of some feature variables.
The results demonstrate that the proposed method not only has high accuracy, but also exhibits excellent reliability and robustness.
American Psychological Association (APA)
Shang, Qiang& Tan, Derong& Gao, Song& Feng, Linlin. 2019. A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1169866
Modern Language Association (MLA)
Shang, Qiang…[et al.]. A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis. Journal of Advanced Transportation No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1169866
American Medical Association (AMA)
Shang, Qiang& Tan, Derong& Gao, Song& Feng, Linlin. A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1169866
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169866