考虑环境、噪音、需求波动和负值输出的公共自行车短期需求预测  

Short-term Demand Prediction for Public Bikes Considering Environment,Noise,Demand Fluctuation,and Negative Output

作　　者：乔健[1] 何梦莹 陈少博 QIAO Jian;HE Mengying;CHEN Shaobo(School of Management,Northwestern Polytechnical University,Xi’an 710072,China)

机构地区：[1]西北工业大学管理学院,陕西西安710072

出　　处：《运筹与管理》2024年第10期224-231,共8页Operations Research and Management Science

基　　金：国家自然科学基金资助项目(71971171)。

摘　　要：既有公共自行车短期需求预测模型忽视了不变与可变环境因素的区别,未考虑数据噪音、需求波动和负值输出的影响。为此,本文利用图卷积神经网络(GCNN)展现短期需求的空间相关性,利用门控循环单元(GRU)展现短期需求和可变环境的时间相关性,提出考虑可变环境、数据噪音和需求波动的GCNN-GRU-E模型,基于该模型提出能自动识别与修正负值输出的GCNN-GRU-E-C模型,还制定了1种数据降噪和5种数据平滑方案。实验结果表明,考虑可变环境时间特性的GCNN-GRU-E的预测精度高于所有基准模型,时间粒度和数据质量均影响预测精度,降噪和平滑数据能显著提高GCNN-GRU-E的预测精度,加权移动平均+局部拟合是效果最好的数据平滑方案,GCNN-GRU-E-C自动识别并修正负值输出的能力既保证了预测结果的合理性、提高了预测精度,又确保了后续动态调度计划的正确制定。With the rapid increase in the number of motor vehicles,many cities around the world have begun to vigorously develop public transportation due to the increasing pressure of traffic congestion,environmental pollution,and energy consumption.The green,energy-saving,and healthy bike sharing system not only solves the problem with connecting public transportation systems,but also meets other short-distance transportation needs,so it has become an important supplement to public transportation systems.The purpose of predicting the short-term demand of a public bike system(PBS)is to provide a basis for setting the target inventory of each station when making a dynamic rebalancing plan.Therefore,accurately predicting the short-term demand of a PBS is the premise of accurately making a dynamic rebalancing plan.Existing short-term demand prediction models for PBSs ignore the impacts of the difference between constant and variable environmental factors,the noise that may exist in demand data,demand fluctuation,and negative output on prediction accuracy.In this paper,a GCNN-GRU-E model considering variable environment,data noise and demand fluctuation is proposed by capturing both the spatial dependency of user demand with Graph Convolutional Neural Network(GCNN),and the temporal dependencies of user demand and variable environmental factors with Gated Recurrent Unit(GRU).Based on the GCNN-GRU-E,a GCNN-GRU-E-C model that can automatically identify and correct negative output is proposed,and a data noise reduction scheme and five data smoothing schemes(i.e.,local fitting,moving average,weighted moving average,moving average+local fitting,and weighted moving average+local fitting)are developed.The datasets used to validate the proposed models in this paper include the transaction dataset of PBS in New York,the station status dataset of PBS in Xi’an,and the variable environmental factors datasets in New York and Xi’an.The transaction dataset of PBS in New York is open data automatically recorded by the system,so it is intac

关 键 词：公共自行车 需求预测 可变环境 数据噪音 需求波动 负值输出 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]