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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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Ennia Mariapaola Acerra, Murad Shoman, Hocine Imine, Claudia Brasile, Claudio Lantieri and Valeria Vignali
Cyclists are one of the main categories of road users particularly exposed to accident risk. The increasing use of this ecological means of transport requires a specific assessment of cyclist safety in terms of traffic flow and human factors. In this stu...
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Harri Koivusalo, Maria Dubovik, Laura Wendling, Eero Assmuth, Nora Sillanpää and Teemu Kokkonen
Nature-based solutions and similar natural water retention measures to manage urban runoff are often implemented by cities in order to reduce runoff peaks, catch pollutants, and improve sustainability. However, the performance of these stormwater managem...
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Xinjian Xiang, Haibin Hu, Yi Ding, Yongping Zheng and Shanbao Wu
This study proposes a GC-YOLOv5s crack-detection network of UAVs to work out several issues, such as the low efficiency, low detection accuracy caused by shadows, occlusions and low contrast, and influences due to road noise in the classic crack-detectio...
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Yulong Bai, Guolian Li, Tianxiu Lu, Yadong Wu, Weihan Zhang and Yidan Feng
Most existing road network matching algorithms are designed based on previous rules and do not fully utilize the potential of big data and historical tracks. To solve this problem, we introduce a new road network matching algorithm based on deep learning...
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