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Exploring Bus Stop Mobility Pattern: A Multi-Pattern Deep Learning Prediction Framework
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DOI：10.1109/TITS.2023.3345872.png)
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The spatio-temporal prediction task in the transportation network is the core of the solutions for various traffic problems. On one hand, the mobility pattern in traffic can be reflected in the travel behavior of the crowd. In most traffic prediction tasks, the importance of the mobility pattern is often overlooked. On the other hand, traffic prediction also has a variety of predicting scenarios, including short-term and long-term prediction, and relevant research cannot solve the problems under the two scenarios at the same time. In view of the problem of existing work, we propose a multi-pattern traffic prediction framework, MPGNNFormer. First, we construct a new bus stop distance network to model the relationships between stops. Then, we use a graph neural network-based deep clustering method to extract the bus stop mobility pattern. Finally, we design a transformer-based spatio-temporal prediction model (STGNNFormer) to predict bus stop flow by taking full advantage of time dependency and space dependency. After that, we conduct a series of experiments to evaluate and test them on the real bus dataset, including analyzing mobility patterns and comparing prediction results. The experimental results prove that MPGNNFormer can improve the calculation efficiency in the prediction scene while ensuring prediction accuracy in the stop-flow prediction of the transportation network.
Keyword:
Spatio-temporal data mining
short and long-term traffic prediction
multi-pattern
deep learning
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