基于动态图神经常微分方程的地铁短时客流预测方法  

Forecasting for Short-term Passenger Flow of Subway Based on Dynamic Graph Neural Ordinary Differential Equations

作　　者：彭颢 贺玉龙[1] 宋太龙 武继壮 PENG Hao;HE Yulong;SONG Tailong;WU Jizhuang(Beijing Key Laboratory of Traffic Engineering,Beijing University of Technology,Beijing 100124,China;Shandong Transportation Institute,Jinan 250031,China;Shandong Road Region Safety and Emergency Support transportation industry Laboratory,Jinan 250031,China)

机构地区：[1]北京工业大学交通工程北京市重点实验室,北京100124 [2]山东省交通科学研究院,济南250031 [3]山东省路域安全与应急保障交通运输行业重点实验室,济南250031

出　　处：《交通信息与安全》2024年第1期150-160,共11页Journal of Transport Information and Safety

基　　金：国家自然科学基金项目(61876011)资助。

摘　　要：随着城市轨道交通的快速发展,客流量的准确预测对于改善运营服务至关重要。为了解决当前地铁客流预测存在的时空特性挖掘不充分等问题,进一步提高预测的精度与效率,研究了基于动态图神经常微分方程模型(multivariate time series with dynamic graph neural ordinary differential equations,MTGODE)的地铁短时客流预测方法。该方法通彭颢1贺玉过学习地铁站点间的动态关联强度构建动态拓扑图结构,基于学习得到的动态图进行连续图传播以传递时空信息、挖掘客流的依赖关系,并采用残差卷积提取多时间尺度下的周期性模式,实现了对站点间时空动态的连续表征,克服了传统图卷积网络模型难以刻画动态空间依赖的局限性。此外,为了充分挖掘不同站点间客流分布的时空规律,综合利用北京地铁自动售检票系统(auto fare collection,AFC)刷卡数据、天气数据、空气质量数据以及车站周边用地属性数据构建多源融合的客流预测模型。通过选取地铁北京站和积水潭站-东直门站的历史数据开展进站客流和OD客流预测实验,结果表明:与多个基准模型相比,该模型在平均绝对误差、均方根误差和平均百分比误差这3个指标中均取得了更优的预测效果,相较最优基准模型扩散卷积循环神经网络(diffusion convolutional recurrent neural network,DCRNN)分别降低了9.93%,12.30%,9.23%,对地铁客流时空分布的拟合程度更好,模型具有更好的预测精度、稳定性和拟合能力。With the rapid expansion of urban rail transit networks,accurate forecasting for passenger flows has become paramount for optimizing operational services.To solve the issue of the inadequate mining for the spatiotemporal characteristics in the forecasting of current subway passenger flow forecasting and to further enhance accuracy and efficiency of forecasting methods,a forecasting method for short-term subway passenger flow based on multivariate time series with dynamic graph neural ordinary differential equations(MTGODE)is proposed.The method constructs a dynamic topological graph structure by learning the dynamic correlation strength between subway stations.Continuous graph propagation is performed on the learned dynamic graph to transmit spatiotemporal information and capture the dependencies of passenger flows.Moreover,residual convolution is employed to extract periodic patterns at multiple time scales,enabling continuous representation of spatiotemporal dynamics between stations and overcoming the limitations of traditional graph convolutional network models in capturing dynamic spatial dependencies.Furthermore,to fully uncover the spatiotemporal patterns of passenger flow distribution among different stations,a multi-source fusion model for passenger flow forecasting is developed by comprehensively utilizing data from the Beijing subway's automatic fare collection system,weather data,air quality data,and surrounding land use attributes of stations.The proposed model was tested by forecasting inbound passenger flow and origin-destination flow using historical data from Beijing Station and Jishuitan Station-Dongzhimen Station.The experimental results demonstrate that the proposed model achieves superior performance compared to multiple benchmark models across three metrics:mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE).Compared to the best-performing benchmark model,the diffusion convolutional recurrent neural network(DCRNN),the proposed model reduces MAE,RMSE

关 键 词：轨道交通 地铁客流 动态图神经网络 MTGODE模型 深度学习 

分 类 号：U121[交通运输工程]