A Traffic Flow Dependency and Dynamics based Deep Learning Aided Approach for Network-Wide Traffic Speed Propagation Prediction
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DOI: 10.1016/j.trb.2022.11.009
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- Gu, Bingmei & Liu, Jiaguo & Ye, Xiaoheng & Gong, Yu & Chen, Jihong, 2024. "Data-driven approach for port resilience evaluation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
- Juan Francisco Sánchez-Pérez & Santiago Oviedo-Casado & Gonzalo García-Ros & Manuel Conesa & Enrique Castro, 2024. "Understanding Complex Traffic Dynamics with the Nondimensionalisation Technique," Mathematics, MDPI, vol. 12(4), pages 1-14, February.
- Yadav, Sunita & Redhu, Poonam, 2024. "Impact of driving prediction on headway and velocity in car-following model under V2X environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
- Zhao, Mingming & Yu, Hongxin & Wang, Yibing & Song, Bin & Xu, Liang & Zhu, Dianchen, 2024. "Real-time freeway traffic state estimation for inhomogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
- Kai Zhang & Zixuan Chu & Jiping Xing & Honggang Zhang & Qixiu Cheng, 2023. "Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model," Mathematics, MDPI, vol. 11(19), pages 1-20, September.
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Keywords
Traffic Speed Propagation; Deep Learning; LSTM; v-CTM; Flow Dependency; Dynamics;All these keywords.
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