A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector Regression Article Swipe
Jawad-ur-Rehman Chughtai
,
Irfan Ul Haq
,
Saif ul Islam
,
Abdullah Gani
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22249735
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22249735
Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages–initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.
Related Topics
Concepts
Feature (linguistics)
Support vector machine
Ensemble learning
Feature vector
Artificial intelligence
Computer science
Baseline (sea)
Mean squared error
Deep learning
Ensemble forecasting
Regression
Machine learning
Big data
Pattern recognition (psychology)
Data mining
Mathematics
Statistics
Geology
Philosophy
Oceanography
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22249735
- https://www.mdpi.com/1424-8220/22/24/9735/pdf?version=1670840998
- OA Status
- gold
- Cited By
- 5
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312127035
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4312127035Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22249735Digital Object Identifier
- Title
-
A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector RegressionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-12Full publication date if available
- Authors
-
Jawad-ur-Rehman Chughtai, Irfan Ul Haq, Saif ul Islam, Abdullah GaniList of authors in order
- Landing page
-
https://doi.org/10.3390/s22249735Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/24/9735/pdf?version=1670840998Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/24/9735/pdf?version=1670840998Direct OA link when available
- Concepts
-
Feature (linguistics), Support vector machine, Ensemble learning, Feature vector, Artificial intelligence, Computer science, Baseline (sea), Mean squared error, Deep learning, Ensemble forecasting, Regression, Machine learning, Big data, Pattern recognition (psychology), Data mining, Mathematics, Statistics, Geology, Philosophy, Oceanography, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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