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Amir Mousavi, Jonathan Bunker and Jinwoo (Brian) Lee
This study investigated whether indices for socioeconomic, demographic and urban form characteristics can reflect the overall effect of each category in a demand forecasting model. Regression equations were developed for trip generation of the land use o...
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Dmitry Pavlyuk
Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: vi...
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Zhenbo Lu, Jingxin Xia, Man Wang, Qinghui Nie and Jishun Ou
Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regi...
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Chi Han, Wei Xiong and Ronghuan Yu
Mega-constellation network traffic forecasting provides key information for routing and resource allocation, which is of great significance to the performance of satellite networks. However, due to the self-similarity and long-range dependence (LRD) of m...
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Sorin Zoican, Roxana Zoican, Dan Galatchi and Marius Vochin
This paper illustrates a general framework in which a neural network application can be easily integrated and proposes a traffic forecasting approach that uses neural networks based on graphs. Neural networks based on graphs have the advantage of capturi...
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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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Weiwei Jiang, Jiayun Luo, Miao He and Weixi Gu
Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the...
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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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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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Irina Kochetkova, Anna Kushchazli, Sofia Burtseva and Andrey Gorshenin
Fifth-generation (5G) networks require efficient radio resource management (RRM) which should dynamically adapt to the current network load and user needs. Monitoring and forecasting network performance requirements and metrics helps with this task. One ...
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Mohammed Akallouch, Oussama Akallouch, Khalid Fardousse, Afaf Bouhoute and Ismail Berrada
Accurate and timely traffic information is a vital element in intelligent transportation systems and urban management, which is vitally important for road users and government agencies. However, existing traffic prediction approaches are primarily based ...
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Song Xin, Ziyi Wang, Huifeng Su, Liuhong Shang, Kun Meng, Xiang Wang, Zhiyong Zhou, Zhongxiao Zhao and Pengfei Zhang
In order to study the safety state of the structure of a cross-sea cable-stayed bridge during its operation period, this paper proposes a combined long-term traffic prediction model based on the XGBoost (eXtreme Gradient Boosting) model and LSTM (Long Sh...
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Yi Wang and Changfeng Jing
Benefiting from the rapid development of geospatial big data-related technologies, intelligent transportation systems (ITS) have become a part of people?s daily life. Traffic volume forecasting is one of the indispensable tasks in ITS. The spatiotemporal...
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Stavroula P. Kitsou, Nikitas Spiros Koutsoukis, Panos Chountalas and Nikolaos P. Rachaniotis
The aviation industry in the COVID-19 pandemic era (caused by the SARS-CoV-2 pathogen) is practically starting from scratch as significant losses have been recorded since the outbreak in December 2019. This paper focuses on the study of the available his...
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Kaiqi Chen, Min Deng and Yan Shi
Traffic forecasting plays a vital role in intelligent transportation systems and is of great significance for traffic management. The main issue of traffic forecasting is how to model spatial and temporal dependence. Current state-of-the-art methods tend...
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Jiandong Bai, Jiawei Zhu, Yujiao Song, Ling Zhao, Zhixiang Hou, Ronghua Du and Haifeng Li
Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In ...
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Yan Du, Xizhong Qin, Zhenhong Jia, Kun Yu and Mengmeng Lin
Accurate and timely traffic forecasting is an important task for the realization of urban smart traffic. The random occurrence of social events such as traffic accidents will make traffic prediction particularly difficult. At the same time, most of the e...
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Xiaohua Tong, Runjie Wang, Wenzhong Shi and Zhiyuan Li
Mathematically describing the physical process of a sequential data assimilation system perfectly is difficult and inevitably results in errors in the assimilation model. Filter divergence is a common phenomenon because of model inaccuracies and affects ...
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