大型活动散场期间地铁车站短时进站客流预测

大型活动散场期间的地铁车站客流属于可预知的非常规客流,采用常规客流的统计预测方法难以准确预测其客流需求.基于深度学习,将历史客流规律、大型活动数据与实时自动售检票系统数据相结合,提出了一种适用于大型活动散场期间地铁车站的短时客流预测模型.首先对历史客流数据进行了拆分及降噪处理,并分析了活动客流特征.之后,基于深度学习框架构建多层结构的卷积神经网络,拟合活动客流特征与客流时空分布的映射关系,并选取Adam(adaptive moment estimation)算法优化训练过程,以适用于活动散场时客流集中进站的情况.最后,以北京地铁奥林匹克公园站为例,利用实测数据验证了模型的准确性.预测结果表明:建立的Adam-CNN(convolution neural network)模型相对于常用时间序列方法自回归滑动平均和传统神经网络SGD-CNN模型具有更高的精度,能够为大型活动的组织提供更为有力的支持.

Prediction of short-term passenger flow of metro station in the period of planned special events

The passenger flow of subway station during the period of large-scale activities is a kind of predictable unconventional passenger flow. This paper proposed a short-term passenger flow prediction model suitable for metro stations during the period of planned special events (PSEs) based on the deep learning framework, combining the real-time AFC passenger flow data, PSE data and historical passenger flow rules. Firstly, the historical passenger flow data is split and noise reduction is processed, and the characteristics of active passenger flow are analyzed. Then, based on the deep learning framework, a multi-layer convolution neural network (CNN) is constructed to fit the mapping relationship between the characteristics of active passenger flow and the spatial-temporal distribution of passenger flow, and the adaptive moment estimation algorithm (Adam) is selected to optimize the training process, so as to apply to the situation that the passenger flow is concentrated in the terminal when the special event is over. Finally, taking Beijing Olympic Park subway station as an example, the accuracy of the model is verified by the measured data. The prediction results show that the Adam-CNN model established in this paper has higher accuracy than the general time series method ARMA and the traditional neural network SGD-CNN model, and can provide more favorable support for the organization of planned special events.