Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique
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- Alisoltani, Negin & Leclercq, Ludovic & Zargayouna, Mahdi, 2021. "Can dynamic ride-sharing reduce traffic congestion?," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 212-246.
- Alireza Ermagun & David Levinson, 2018. "Spatiotemporal traffic forecasting: review and proposed directions," Transport Reviews, Taylor & Francis Journals, vol. 38(6), pages 786-814, November.
- Xiaolei Ma & Haiyang Yu & Yunpeng Wang & Yinhai Wang, 2015. "Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
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Keywords
traffic congestion; short-term traffic congestion; convolution neural network; xception classifier; sustainable transportation system; support vector machine classifier;All these keywords.
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