Message Passing Neural Networks for Traffic Forecasting

• URL: http://arxiv.org/abs/2305.05740v1
• Date: Tue, 9 May 2023 19:33:52 GMT
• Title: Message Passing Neural Networks for Traffic Forecasting
• Authors: Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz, Flora D. Salim
• Abstract summary: Traffic forecasting is interesting because it is complex as the future speed of a road is dependent on a number of different factors. Existing works fail to capture the inter-node interactions because none are using the message-passing flavor of GNN. Results from real-world data show the superiority of the message-passing flavor for traffic forecasting.
• Score: 32.354251863295424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location. Traffic forecasting is interesting because it is complex as the future speed of a road is dependent on a number of different factors. Therefore, to properly forecast traffic, we need a model that is capable of capturing all these different factors. A factor that is missing from the existing works is the node interactions factor. Existing works fail to capture the inter-node interactions because none are using the message-passing flavor of GNN, which is the one best suited to capture the node interactions This paper presents a plausible scenario in road traffic where node interactions are important and argued that the most appropriate GNN flavor to capture node interactions is message-passing. Results from real-world data show the superiority of the message-passing flavor for traffic forecasting. An additional experiment using synthetic data shows that the message-passing flavor can capture inter-node interaction better than other flavors.

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