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Real Time Traffic Flow Monitoring and Congestion Prediction Driven by Deep Learning

Published: 29 April 2024 Publication History

Abstract

Spatiotemporal data, such that collected from road traffic monitoring and congestion prediction, exhibits temporal and geographical relationships. This investigation employs a two-pronged strategy, first investigating spatiotemporal characteristics and then creating a model for traffic flow monitoring and congestion prediction based on a deep neural network. With the use of a graph convolutional neural network and an attention mechanism, this research is the first to propose a method for learning spatial features of traffic flows. To improve our expression and the capacity to extract relevant spatial attributes, we introduce node adaptive learning and apply different weights to the degree of mutual influence across different nodes. Furthermore, we present a temporal convolutional network-based learning approach for temporal features of traffic flow, which uses causal convolution to guarantee that input and output data dimensions are consistent. Long-length spatiotemporal sequence data benefit greatly from the dilated convolution's ability to dynamically regulate the receptive field by adjusting the sampling interval. Using spatiotemporal graphs, a system is developed to monitor and foresee traffic congestion. In order to learn feature information, mode-specific parameter values, and overall model performance, this model combines a graph convolutional neural network with an attention mechanism.

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ICEITSA '23: Proceedings of the 3rd International Conference on Electronic Information Technology and Smart Agriculture
December 2023
541 pages
ISBN:9798400716775
DOI:10.1145/3641343
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 29 April 2024

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Author Tags

  1. Congestion prediction
  2. Deep learning
  3. Neural network
  4. Traffic flow monitoring

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