Wu Jheng-Long, Lu Mingying, Wang Chia-Yun
Department of Data Science, Soochow University, Taipei City, Taiwan.
School of Big Data Management, Soochow University, Taipei City, Taiwan.
Appl Intell (Dordr). 2023 Feb 2:1-16. doi: 10.1007/s10489-023-04483-x.
In the rapid development of public transportation led, the traffic flow prediction has become one of the most crucial issues, especially estimating the number of passengers using the Mass Rapid Transit (MRT) system. In general, predicting the passenger flow of traffic is a time-series problem that requires external information to improve accuracy. Because many MRT passengers take cars or buses to MRT stations, this study used external information from vehicle detection (VD) devices to improve the prediction of passenger flow. This study proposed a deep learning architecture, called a multiple-attention deep neural network (MADNN) model, based on historical MRT passenger flow and the flow from surrounding VD devices that estimates the weights of the vehicle detection devices. The model consists of (1) an MRT attention layer (MRT-AL) that generate hidden features for MRT stations, (2) a surrounding VD (SVD) attention layer (SVD-AL) that generate hidden features for SVD devices, and (3) an MRT-SVD attention layer (MRT-SVD-AL) that generate attention weights for each VD device in an MRT station. The results of the investigation indicated that the MADNN model outperformed the models without multiple-attention mechanisms in predicting the passenger flow of MRT traffic.
在公共交通快速发展的带动下，交通流量预测已成为最为关键的问题之一，尤其是对使用地铁系统的乘客数量进行估计。一般来说，预测交通客流量是一个时间序列问题，需要外部信息来提高准确性。由于许多地铁乘客会乘坐汽车或公交车前往地铁站，本研究利用车辆检测（VD）设备的外部信息来改进客流量预测。本研究基于历史地铁客流量以及来自周边VD设备的流量，提出了一种深度学习架构，称为多注意力深度神经网络（MADNN）模型，该模型可估计车辆检测设备的权重。该模型由（1）一个为地铁站生成隐藏特征的地铁注意力层（MRT-AL）、（2）一个为VD设备生成隐藏特征的周边VD（SVD）注意力层（SVD-AL）以及（3）一个为地铁站中的每个VD设备生成注意力权重的MRT-SVD注意力层（MRT-SVD-AL）组成。调查结果表明，在预测地铁交通客流量方面，MADNN模型优于没有多注意力机制的模型。
