Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Purpose – Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration p...

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Bibliographic Details
Main Authors: Weiwei Zhu, Jinglin Wu, Ting Fu, Junhua Wang, Jie Zhang, Qiangqiang Shangguan
Format: Article
Language:English
Published: Tsinghua University Press 2021-09-01
Series:Journal of Intelligent and Connected Vehicles
Subjects:
prediction of traffic incident duration
long short-term memory
multi-layer perception
deep learning
Online Access:https://www.emerald.com/insight/content/doi/10.1108/JICV-03-2021-0004/full/pdf?title=dynamic-prediction-of-traffic-incident-duration-on-urban-expressways-a-deep-learning-approach-based-on-lstm-and-mlp

Internet

https://www.emerald.com/insight/content/doi/10.1108/JICV-03-2021-0004/full/pdf?title=dynamic-prediction-of-traffic-incident-duration-on-urban-expressways-a-deep-learning-approach-based-on-lstm-and-mlp

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