Abstract
With the increase of the types of urban management objects, the intelligent management of the whole city has become a matter of concern in various countries, and it is also one of the indispensable links in urban development. In the construction of cities all over the world, the intelligent and scientific management system has been used innovatively. We provide excellent facilities for transportation development, information exchange, and resource progress. The research on urban fine management based on multisource spatial data fusion is proposed. Aiming at the traffic problems in urban fine management, this paper proposes a deep network architecture based on multisource data fusion. Multisource spatial data fusion technology is used to analyze urban traffic data. Deep network architecture is used to improve the precision management status of a smart city and the accuracy of traffic condition prediction. Then, the convolution neural network technology is explored in the data fusion technology strategy. The research results show that the framework has the ability to deal with heterogeneous data and urban big data and can effectively improve the traffic management state in the construction of a smart city and effectively solve the complexity of urban fine management and processing efficiency in the construction of a smart city.
Highlights
Urbanization construction is one of the means for each country to realize the development of the whole transportation industry and information industry [1]
We propose to use multisource spatial data fusion technology to analyze traffic conditions. e architecture of multisource data fusion is shown in Figure 2. e structure mainly includes two branches and a fusion module. e first branch is the graph convolution neural network, which is composed of several spatiotemporal convolution modules
Based on multisource data fusion technology, this paper adopts the deep learning method. is paper proposes a deep multisource data fusion architecture based on the strategy of fusion in branch transformation to improve the accuracy of real-time traffic prediction
Summary
Urbanization construction is one of the means for each country to realize the development of the whole transportation industry and information industry [1]. En, according to the multisource spatial fusion technology, the influencing factors of traffic roads in urban management are analyzed, and the data definition and planning simulation of the whole experimental process are carried out. 2. Research on Traffic Road Technology in Urban Fine Management Based on Multisource Spatial Data Fusion. E traffic network structure represents the road changes in the whole urban planning and belongs to the information data in the network structure [23]. Real-time traffic data is the representation of the traffic state of the urban road system, which belongs to network-based data. Traffic prediction based on multisource data fusion: the observed value formula of traffic conditions in the first several periods of a given number of nodes is as follows:. A representation of time-domain features can be obtained by time-domain embedding. e fusion of space-time features is to treat the representation of time-domain features as convolution kernel and convolute the spatial representation to obtain the representation of low-level spatial and temporal features
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