关键词: 客流量预测, 时空数据, 编码器解码器, 图卷积网络, 门控循环单元

Abstract:

Passenger flow prediction is an important part of urban intelligent transportation system, which has significant guiding meaning for people’s travel and traffic control. In view of the spatial dimension and temporal dimension attributes of subway passenger flow data, this paper proposes a prediction model that can capture the spatial and temporal characteristics of the data simultaneously. The model is based on the encoder-decoder architecture design, in which both decoder and encoder are composed of spatio-temporal prediction module. In this module, graph convolution is used to learn the station’s spatial structure and the gating cycle unit to capture temporal characteristics. In addition, the model uses the dual time series composed of entrance and exit passenger flow in unit time as input to cooperatively predict the entrance and exit passenger flow of each station. The experimental on the data collected from Shanghai metro cards show that the proposed model achieves a better result in the entrance and exit passenger flow prediction, which indicate taking the spatial dependence into consideration can improve the model prediction accuracy.

Key words: passenger flow prediction, spatio-temporal data, encoder-decoder, graph convolutional network, gated cycle unit