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Accurate short-term traffic prediction plays a pivotal role in various smartmobility operation and management systems. Currently, most of thestate-of-the-art prediction models are based on graph neural networks (GNNs),and the required training samples are proportional to the size of the trafficnetwork. In many cities, the available amount of traffic data is substantiallybelow the minimum requirement due to the data collection expense. It is stillan open question to develop traffic prediction models with a small size oftraining data on large-scale networks. We notice that the traffic states of anode for the near future only depend on the traffic states of its localizedneighborhoods, which can be represented using the graph relational inductivebiases. In view of this, this paper develops a graph network (GN)-based deeplearning model LocaleGN that depicts the traffic dynamics using localized dataaggregating and updating functions, as well as the node-wise recurrent neuralnetworks. LocaleGN is a light-weighted model designed for training on fewsamples without over-fitting, and hence it can solve the problem of few-sampletraffic prediction. The proposed model is examined on predicting both trafficspeed and flow with six datasets, and the experimental results demonstrate thatLocaleGN outperforms existing state-of-the-art baseline models. It is alsodemonstrated that the learned knowledge from LocaleGN can be transferred acrosscities. The research outcomes can help to develop light-weighted trafficprediction systems, especially for cities lacking historically archived trafficdata.

Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases