基于Elman神经网络的公共自行车单站点需求预测  

Demand prediction of public bicycle rental station based on Elman neural network

作　　者：解小平[1] 邱建东[1] 汤旻安[2] XIE Xiaoping;QIU Jiandong;TANG Min-an(Mechatronic T&R Institute of Lanzhou Jiaotong University, Lanzhou 730070, China;College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

机构地区：[1]兰州交通大学机电技术研究所,兰州730070 [2]兰州交通大学新能源与动力工程学院,兰州730070

出　　处：《计算机工程与应用》2017年第16期221-224,236,共5页Computer Engineering and Applications

基　　金：甘肃省自然科学研究基金计划(No.1208RJZA292);兰州市科技计划资助基金(No.2013-4-18)

摘　　要：公共自行车租赁系统目前存在的主要问题是高峰时段用户存取车困难,站点需要工作人员值守。为了提高城市公共自行车调度的科学性、缩短调度时间、降低调度成本,避免调度过程中调度员凭经验确定各个站点需求量的盲目性,从而更好地服务租赁者,满足其出行需求。建立了基于改进的Elman神经网络的公共自行车单站点需求量预测模型。通过仿真实验将改进模型和已有模型的预测结果与实际需求量进行对比,证明了提出的预测方法有效可行。The main problem of the public bicycle rental system is the difficulty of the user access to the bicycle and the rental station needs staff on duty at the peak hours.In order to avoid scheduling process determined by dispatcher empirically blindly,improve the scheduling scientifically,shorten the operation time and reduce the cost,so as to better meet the rental demand of the users,a method is proposed to predict the demand of single public bicycle rental station based on improved Elman Neural Network.The effectiveness of the proposed method is proved by comparing the predicted results with the actual demand.

关 键 词：城市交通 公共自行车租赁系统 单站点需求量预测 ELMAN神经网络 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] U484[自动化与计算机技术—控制科学与工程]