Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms
Joint Authors
Sridharan, Mohan
Weerasekera, Rivindu
Ranjitkar, Prakash
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-15
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
Short-term traffic prediction is a key component of Intelligent Transportation Systems.
It uses historical data to construct models for reliably predicting traffic state at specific locations in road networks in the near future.
Despite being a mature field, short-term traffic prediction still poses some open problems related to the choice of optimal data resolution, prediction of nonrecurring congestion, and the modelling of relevant spatiotemporal dependencies.
As a step towards addressing these problems, this paper investigates the ability of Artificial Neural Networks, Random Forests, and Support Vector Regression algorithms to reliably model traffic flow at different data resolutions and respond to unexpected traffic incidents.
We also explore different feature selection methods to identify and better understand the spatiotemporal attributes that most influence the reliability of these models.
Experimental results indicate that data aggregation does not necessarily achieve good performance for multivariate spatiotemporal machine learning models.
The models learned using high-resolution 30-second input data outperformed the corresponding baseline ARIMA models by 8%.
Furthermore, feature selection based on Recursive Feature Elimination resulted in models that outperformed those based on linear correlation-based feature selection.
American Psychological Association (APA)
Weerasekera, Rivindu& Sridharan, Mohan& Ranjitkar, Prakash. 2020. Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1175945
Modern Language Association (MLA)
Weerasekera, Rivindu…[et al.]. Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms. Journal of Advanced Transportation No. 2020 (2020), pp.1-21.
https://search.emarefa.net/detail/BIM-1175945
American Medical Association (AMA)
Weerasekera, Rivindu& Sridharan, Mohan& Ranjitkar, Prakash. Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1175945
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175945