The transportation system, particularly the street community, is the spine of any fashionable financial system. However, with speedy urbanization, the congestion degree has surged drastically, inflicting a direct impact on the standard of city life, the surroundings, and the financial system. In this paper, we suggest (i) a cheap and environment friendly Traffic Congestion Pattern Analysis algorithm primarily based on Image Processing, which identifies the group of roads in a community that suffers from reoccurring congestion; (ii) deep neural community structure, fashioned from Convolutional Autoencoder, which learns each spatial and temporal relationships from the sequence of picture knowledge to foretell the city-wide grid congestion index. Our experiment exhibits that each algorithms are environment friendly as a result of the sample evaluation is predicated on the essential operations of arithmetic, whereas the prediction algorithm outperforms two different deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) when it comes to large-scale visitors community prediction efficiency. A case examine was performed on the dataset from Seoul metropolis.
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#LargeScale #Road #Network #Congestion #Pattern #Analysis #Prediction #Deep #Convolutional #Autoencoder