Bus arrival interval prediction model based on gated recurrent unit network

Abstract

By analyzing the bus operation environment and accounting for prediction uncertainties, a bus arrival interval prediction model was developed utilizing a gated recurrent unit (GRU) neural network. To reduce the impact of irrelevant data and boost prediction accuracy, an attention mechanism was integrated into the point model to concentrate on important input sequence information. Based on the point predictions, the lower upper bound estimation (LUBE) method was used, providing a range for the bus interval times predicted by the model. The model was validated using data from 169 bus routes in Nanchang, Jiangxi Province. The results indicated that the attention-GRU model outperformed neural network, long short-term memory and GRU models. Compared with the Bootstrap method, the LUBE method has a narrower average interval width. The coverage width-based criterion (CWC) was reduced by 8.1%, 2.2%, and 5.7% at confidence levels of 85%, 90%, and 95%, respectively, during the off-peak period, and by 23.2%, 26.9%, and 27.3% at confidence levels of 85%, 90%, and 95%, respectively, during the peak period. Therefore, it can accurately describe the fluctuation range in bus arrival times with higher accuracy and stability.

Keywords

public transportation / gated recurrent unit network / attention mechanism / lower upper bound estimation

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Funding

National Natural Science Foundation of China(52162042); General Science and Technology Project of Jiangxi Provincial Department of Transportation(2024YB039)