An Hybridization of LSTM and Random Forest Model to Predict Road Situation-Reference-Cited by-同舟云学术

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An Hybridization of LSTM and Random Forest Model to Predict Road Situation

Published:2023-12-14 Issue: Volume:90 Page:1-7
ISSN:
Container-title:2023 IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)
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Author:

Souki Olfa¹,Djemaa Raoudha Ben²,Amous Ikram³,Sèdes Florence⁴

Affiliation:

1. University of Sfax,Faculty of Sciences Economics and Management of Sfax, Miracl laboratory,Tunisia

2. Higher Institute of Computer Science and Communication Technology, University of Sousse,Miracl laboratory,Tunisia

3. National School of Electronics and Telecommunications of Sfax, University of sfax,Miracl laboratory,Tunisia

4. Toulouse Institute of Computer Science Research, University of Paul Sabatier,IRIT laboratory,Toulouse,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10477677/10477776/10477806.pdf?arnumber=10477806

Reference14 articles.

1. Traffic Flow Prediction With Big Data: A Deep Learning Approach

2. Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction

3. Real-time traffic signal control using deep reinforcement learning;Low;Transportation Research Part C: Emerging Technologies,2018

4. Monitor and Analyze Sensor Data from a Connected Vehicle Thanks to Cloud Edge Computing

5. A Survey of Middlewares for self-adaptation and context-aware in Cloud of Things environment

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