关键词: 智能交通, 短时公交客流预测方法, Attention机制, Multi-headed机制, 站点实时关联度, 站点信息编码

Abstract: In order to explore the relevance between bus stations and make real-time predictions of bus passenger volume more accurate, this paper proposes a core traffic prediction algorithm based on Attention, referred to as TFMA, which combines data preprocessing and station information coding. A short-term public traffic passenger volume forecasting method based on the real-time relevance of stations is proposed. This method firstly proposes the real-time relevance of stations in an innovative way, which can achieve a more accurate prediction of the passenger volume of the target station. Secondly, this paper integrates the relevant factors(e.g., line station information, passenger volume rate of change, weather) and date into the coding information of the bus station. Then, the method relies on the Attention mechanism to calculate the real-time relevance of the station; the core algorithm also uses the multi-headed mechanism, adding channels and residual connections to further improve the prediction ability. Finally, this paper uses the data of Suzhou city bus for verification. The results show that: in terms of accuracy, compared with 53.8% of multiple linear regression, 66.9% of GRU and 81.2% of LightGBM, the accuracy of the forecasting method proposed in this paper is above 90%, indicating that the method has excellent short-term bus passenger flow prediction capabilities.

Key words: intelligent transportation, short-term public traffic passenger volume forecasting method, Attention mechanism, Multi-headed mechanism, real-time relevance of the station, station information coding