Results 1 to 10 of about 215,061 (165)
Remote sensing image road network detection based on channel attention mechanism [PDF]
HeliyonExtracting and detecting road network consistency from high-resolution remote sensing images has been a hot and difficult problem in the computer vision. Although it has made significant progress, there is still a phenomenon of high training accuracy but
Chuanhui Shan, Xinlong Geng, Chao Han
doaj +2 more sources
Estonian Road Network and Road Management
The Baltic Journal of Road and Bridge Engineering, 2006 Estonian public road network is one of the densest in the Baltic and Nordic countries with the 52,8 % of paved state roads. Also it is specific in the sense of the organisation of road management the maintenance of which is implemented partly by private ...
Andrus Aavik
doaj +1 more source
Measuring Road Network Vulnerability with Sensitivity Analysis. [PDF]
PLoS ONE, 2017 This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole
Leng Jun-Qiang +3 more
doaj +2 more sources
Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks
Remote Sensing, 2019 Road network extraction from remote sensing images has played an important role in various areas. However, due to complex imaging conditions and terrain factors, such as occlusion and shades, it is very challenging to extract road networks with complete ...
Yang Zhang +5 more
doaj +3 more sources
Research on digital flow control model of urban rail transit under the situation of epidemic prevention and control [PDF]
Smart and Resilient Transportation, 2021 Purpose – Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to
Qi Sun +4 more
doaj +1 more source
International Journal of Industry and Sustainable Development, 2022 The system is a network of stations deployed along the roads in a smart city. Every station is a standalone self-powered system working as an element in a huge network which is responsible for controlling the traffic on the road and improve the quality ...
Hassan Kasem +6 more
doaj +1 more source
Road network partitioning method based on Canopy-Kmeans clustering algorithm [PDF]
Archives of Transport, 2020 With the increasing scope of traffic signal control, in order to improve the stability and flexibility of the traffic control system, it is necessary to rationally divide the road network according to the structure of the road network and the ...
Xiaohui Lin, Jianmin Xu
doaj +1 more source
Urban Road Transport Network Analysis: Machine Learning and Social Network Approaches
Communications, 2022 Traffic congestion is one of the most significant problems in urban transportation. It has been increasing, especially in regions close to intersections. Several methods have been developed to reduce the traffic congestion. One of the analysis methods is
Emre Kuşkapan +4 more
doaj +1 more source
Applied Sciences, 2022 Accurately identifying the key nodes of the road network and focusing on its management and control is an important means to improve the robustness and invulnerability of the road network.
Chaofeng Liu +4 more
doaj +1 more source
Graph Convolutional Networks for Road Networks [PDF]
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019 Machine learning techniques for road networks hold the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a road network by utilizing information of, e.g., adjacent road segments.
Tobias Skovgaard Jepsen +2 more
openaire +2 more sources