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- W4387349153 abstract "The previous decades have witnessed the remarkable growth of information technology and the emergence of novel algorithms in identifying and predicting future situations. Accordingly, many different methods were proposed in this field. The current paper focuses on two issues. The first detects the movement modes of moving objects in the future based on the current movement route of moving objects. The second calculates the movement dependence degree of moving objects. As a result, the impact of the increase in moving objects is analyzed according to the amount of traffic on the routes. In order to achieve more accurate results, Deep Learning (DL) was used to predict the movement states of moving objects for the future. For this purpose, the raw motion data of moving objects are computed in the Global Positioning System (GPS) format. The extent of route interactions is evaluated by applying new properties to the volume of moving objects and calculating the correlation coefficient and distance criterion, creating a distance matrix for both current and future states. This paper's findings benefit the experts in urban traffic management that can analyze and evaluate the impact of new decisions in advance without spending much time and money." @default.
- W4387349153 created "2023-10-05" @default.
- W4387349153 creator A5026832191 @default.
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- W4387349153 date "2023-01-01" @default.
- W4387349153 modified "2023-10-06" @default.
- W4387349153 title "Routes Analysis and Dependency Detection Based on Traffic Volume: A Deep Learning Approach" @default.
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- W4387349153 doi "https://doi.org/10.1007/978-3-031-43763-2_2" @default.
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