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http://dx.doi.org/10.15207/JKCS.2022.13.01.051

Speed Prediction and Analysis of Nearby Road Causality Using Explainable Deep Graph Neural Network  

Kim, Yoo Jin (Department of Computer Engineering, Hongik University)
Yoon, Young (Department of Computer Engineering, Hongik University)
Publication Information
Journal of the Korea Convergence Society / v.13, no.1, 2022 , pp. 51-62 More about this Journal
Abstract
AI-based speed prediction studies have been conducted quite actively. However, while the importance of explainable AI is emerging, the study of interpreting and reasoning the AI-based speed predictions has not been carried out much. Therefore, in this paper, 'Explainable Deep Graph Neural Network (GNN)' is devised to analyze the speed prediction and assess the nearby road influence for reasoning the critical contributions to a given road situation. The model's output was explained by comparing the differences in output before and after masking the input values of the GNN model. Using TOPIS traffic speed data, we applied our GNN models for the major congested roads in Seoul. We verified our approach through a traffic flow simulation by adjusting the most influential nearby roads' speed and observing the congestion's relief on the road of interest accordingly. This is meaningful in that our approach can be applied to the transportation network and traffic flow can be improved by controlling specific nearby roads based on the inference results.
Keywords
Explainable AI; Graph Neural Network; Speed Prediction; Causality Analysis; Deep Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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