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Diffusion-based traffic flow algorithm for graph representation learning
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| Author:
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B.MALATHI , DR.S.CHELLIAH
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| Abstract:
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In recent years, the use of graph-based representations for modeling transportation systems has become increasingly popular due to their ability to capture the complex relationships between different parts of the system. However, one of the challenges of working with graph-based representations is that it can be difficult to analyze the flow of traffic through the network of roads and intersections. To address this challenge, diffusion can be applied to an algorithm for learning to represent graphs of traffic flow in a city. The basic idea behind the algorithm is to simulate particles representing vehicles moving through the network of roads and intersections, using diffusion to model the spread of traffic. Specifically, each particle is assigned a random starting location and a random velocity, and is then allowed to move through the network according to a set of predefined rules. As the particles move through the network, they leave behind a trail that represents the density of traffic at each point in the network. This trail can be analyzed to identify areas of congestion, which can then be targeted for optimization. For example, if a particular intersection is identified as a bottleneck, the algorithm can be used to determine the optimal timing for traffic signals at that intersection to reduce congestion and improve traffic flow. By analyzing this information, the algorithm can optimize traffic flow and reduce congestion, helping to create more efficient transportation systems. This approach has the potential to revolutionize the way that traffic flow is analyzed and optimized, making it possible to create more efficient and sustainable transportation systems that benefit both individuals and society as a whole
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| Keyword:
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Graph representation algorithm, graph embedding , diffusion model, Auto-encoder.
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.03.533
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| Download:
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