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This content will become publicly available on August 6, 2024

Title: Reaction-Diffusion Graph Ordinary Differential Equation Networks: Traffic-Law-Informed Speed Prediction under Mismatched Data
Accurate traffic speed prediction is critical to many applications, from routing and urban planning to infrastructure management. With sufficient training data where all spatio-temporal patterns are well- represented, machine learning models such as Spatial-Temporal Graph Convolutional Networks (STGCN), can make reasonably accurate predictions. However, existing methods fail when the training data distribution (e.g., traffic patterns on regular days) is different from test distribution (e.g., traffic patterns on special days). We address this challenge by proposing a traffic-law-informed network called Reaction-Diffusion Graph Ordinary Differential Equation (RDGODE) network, which incorporates a physical model of traffic speed evolution based on a reliable and interpretable reaction- diffusion equation that allows the RDGODE to adapt to unseen traffic patterns. We show that with mismatched training data, RDGODE is more robust than the state-of-the-art machine learning methods in the following cases. (1) When the test dataset exhibits spatio-temporal patterns not represented in the training dataset, the performance of RDGODE is more consistent and reliable. (2) When the test dataset has missing data, RDGODE can maintain its accuracy by intrinsically imputing the missing values.  more » « less
Award ID(s):
2008155
NSF-PAR ID:
10466683
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
The 12th International Workshop on Urban Computing, held in conjunction with the 29th ACM SIGKDD 2023
Date Published:
Subject(s) / Keyword(s):
Traffic speed prediction graph neural networks spatial-temporal time series prediction
Format(s):
Medium: X
Location:
Long Beach, CA, USA
Sponsoring Org:
National Science Foundation
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