融合天气因素与时空注意力残差双向网络的共享单车短时序轨迹预测模型  

Prediction Model of Bicycle Sharing Trajectories Incorporating Natural Factors and Spatio-Temporal Attention Residuals in a Short Time-Series Bi-Directional Network

作　　者：白涵文 曹弋 于明政 BAI Hanwen;CAO Yi;YU Mingzheng(School of Transportation Engineering,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学交通工程学院,大连116028

出　　处：《地球信息科学学报》2024年第12期2712-2721,共10页Journal of Geo-information Science

基　　金：辽宁省属本科高校基本科研业务费专项资金项目(LJ212410150048)。

摘　　要：共享单车轨迹预测是科学合理规划其基础设施的前提条件,但现有研究预测机制较单一、影响因素较片面,致使轨迹预测准确度不高,制约了共享单车的进一步发展。因此为揭示共享单车出行的时空特性与天气因素对出行轨迹的影响,提高轨迹预测精度,本文构建融合天气因素与时空注意力残差双向网络(WSTAR-BiLSTM)的轨迹预测模型。本研究利用2021年数字中国大赛公开数据开放平台提供的厦门市共享单车订单及轨迹大数据,分析其出行时空分布规律与天气因素对出行轨迹的影响。考虑气温、天气现象、风速、空气质量因素,将共享单车轨迹数据按周期性分割时间序列,融合注意力机制进行残差学习,根据天气因素调整预测结果。将轨迹数据按7:2:1划分为训练集、测试集与验证集,分别进行模型训练及预测、模型参数自适应调整及预测结果对比验证实验。实验结果表明,WSTAR-BiLSTM模型轨迹预测精度高于传统模型LSTM、BiLSTM、CNN和自建对比模型(不含天气因素的STAR-BiLSTM、不含注意力机制的WSTR-BiLSTM和不含残差网络的WSTABiLSTM),准确率分别提升了12.02%、14.5%、12.02%、4.86%、6.96%、10.80%;绝对轨迹误差分别降低了1.83、2.53、1.85、1.07、1.23、1.53 m。研究表明,WSTAR-BiLSTM模型不仅继承了普通残差网络模型的优势,还创新性融合了注意力机制与多个天气因素的影响,在提升轨迹预测精度的同时还具有较强的智能学习与成长特性,使轨迹预测的精度随反馈次数的增加而进一步提高。研究结果对共享单车轨迹的精准预测具有理论指导意义,对该出行模式的推广具有实际应用价值。Bicycle sharing offers the advantages of resource sharing,environmental sustainability,and low carbon emissions,and has been widely applied in recent years.Trajectory prediction of shared bicycles is crucial for the scientific and efficient planning of infrastructure.However,existing trajectory prediction mechanisms are relatively limited,and the influencing factors are often too narrow,leading to low prediction accuracy.This restricts the further growth and development of bicycle-sharing systems.Therefore,accurately predicting the travel trajectories of shared bicycles is essential for optimizing bicycle lanes,efficiently deploying and scheduling bicycle resources,improving road design,and addressing the"last mile"challenge in urban transportation.To better understand the spatio-temporal characteristics of shared bicycle travel and the influence of natural and weather factors on travel trajectories,and to improve prediction accuracy,this paper developes a trajectory prediction model that integrates natural and weather factors with a spatio-temporal attention residual bi-directional network(NWSTAR-BiLSTM).This study uses shared bicycle order and trajectory data from Xiamen,provided by the government’s open data platform,to analyze the spatio-temporal distribution of travel and the impact of natural and weather factors on trajectories.The model incorporates variables such as temperature,weather conditions,wind speed,and air quality,dividing the shared bicycle trajectory data into time series based on periodicity.Using an attention mechanism and residual learning,the prediction results are adjusted according to weather factors.The dataset is divided into training,testing,and validation sets in a 7:2:1 ratio,and the model undergoes training,parameter adjustment,and comparative validation.Experimental results show that the trajectory prediction accuracy of the NSTARWSTAR-BiLSTM model exceeds that of traditional models,such as LSTM,BiLSTM,CNN,Att-LSTM,and self-built comparative models(e.g.,STAR-BiLSTM without natura

关 键 词：城市交通 残差网络 交通大数据 时空特征 注意力机制 共享单车 轨迹预测 

分 类 号：U491.225[交通运输工程—交通运输规划与管理]