多尺度时序依赖的校园公共区域人流量预测

Pedestrian Volume Prediction for Campus Public Area Based on Multi-scale Temporal Dependency

校园公共区域人流量预测对于维护校园安全、提升校园管理水平有重大意义.尤其在疫情防控下,高校复学对公共区域的人流量预测和控制提出了更高的要求.以高校食堂为例,通过预测就餐人数,有助于食堂防疫人员合理调度和安排,既降低了人群聚集的潜在风险,也可以针对食堂人流量分布情况提供分时分批服务.然而,由于校园管理需求,如节假日和教学安排等因素,使得校园公共区域人流量预测问题颇具挑战性.为此,提出一种基于深度学习的多尺度时序卷积网络MSCNN(multi-scale temporal patterns convolution neural networks),实现人流量时序数据中短时依赖、长时周期模式的获取和多尺度时序模式特征的重标定,以对任意时段人流量进行预测.通过在真实校园环境数据集以及公开数据集上的实验,验证了MSCNN模型的有效性和执行效率.

Predicting pedestrian volume in campus public area is of significance for maintaining campus safety and improving campus management level.In particular,due to the outbreak of epidemic,the resumption of college education has put forward higher requirements for the prediction and control of the pedestrian volume in public area.Taking college canteens as an example,predicting the pedestrian volume in canteen is helpful with canteen epidemic prevention worker to make scheduling and arrangement,which not only reduces the risk of crowd gathering,but also provides more considerate service according to the distribution of the pedestrian volume in canteen.Considering the requirements of campus management,e.g.,holiday,course arrangement,pedestrian volume prediction in the campus public area is challenging.This study proposes a multi-scale temporal patterns convolution neural networks(MSCNN)based on deep learning to obtain the short-term dependencies as well as long-term periodicities,and reweights the multi-scale temporal pattern characteristics to predict the pedestrian volume at any given time.The effectiveness and efficiency of the MSCNN model are verified by experiments on real-world datasets.