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Shuai Lu, Haibo Chen and Yilong Teng
Traffic flow prediction is a crucial research area in traffic management. Accurately predicting traffic flow in each area of the city over the long term can enable city managers to make informed decisions regarding the allocation of urban transportation ...
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Juan Nunez-Portillo, Alfonso Valenzuela, Antonio Franco and Damián Rivas
This paper presents an approach for integrating uncertainty information in air traffic flow management at the tactical phase. In particular, probabilistic methodologies to predict sector demand and sector congestion under adverse weather in a time horizo...
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Wen Tian, Yining Zhang, Ying Zhang, Haiyan Chen and Weidong Liu
To fully leverage the spatiotemporal dynamic correlations in air traffic flow and enhance the accuracy of traffic flow prediction models, thereby providing a more precise basis for perceiving congestion situations in the air route network, a study was co...
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Vienna N. Katambire, Richard Musabe, Alfred Uwitonze and Didacienne Mukanyiligira
Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general,...
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Petros Brimos, Areti Karamanou, Evangelos Kalampokis and Konstantinos Tarabanis
Traffic forecasting has been an important area of research for several decades, with significant implications for urban traffic planning, management, and control. In recent years, deep-learning models, such as graph neural networks (GNN), have shown grea...
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Xianlei Fu, Maozhi Wu, Sasthikapreeya Ponnarasu and Limao Zhang
This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep ...
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Min Yue and Shuhong Ma
A crucial component of multimodal transportation networks and long-distance travel chains is the forecasting of transfer passenger flow between integrated hubs in urban agglomerations, particularly during periods of high passenger flow or unusual weather...
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Zhuhua Liao, Haokai Huang, Yijiang Zhao, Yizhi Liu and Guoqiang Zhang
Urban planning and function layout have important implications for the journeys of a large percentage of commuters, which often make up the majority of daily traffic in many cities. Therefore, the analysis and forecast of traffic flow among urban functio...
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Dimitrios K. Nasiopoulos, Dimitrios A. Arvanitidis, Dimitrios M. Mastrakoulis, Nikos Kanellos, Thomas Fotiadis and Dimitrios E. Koulouriotis
Globalization has gotten increasingly intense in recent years, necessitating accurate forecasting. Traditional supply chains have evolved into transnational networks that grow with time, becoming more vulnerable. These dangers have the potential to disru...
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Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a...
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