基于灰色理论的ofo需求量短时预测
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摘要
随着共享单车(ofo)的快速发展,对其需求量短时预测的研究尤为重要。根据灰色理论,分析了GM模型的特点,发现GM(1,1)模型适用于具有较强指数规律的单调序列。而考虑到短时车流量某一时段内数据具有波动性和饱和性,为提高短时预测模型的精度和效率,在GM(1,1)模型的基础上,组合出一种灰色马尔可夫模型。利用2017年9月1日～9月8日共享单车(ofo)需求量的数据,使用Matlab软件工具箱来对该模型讲行计算机仿真验证。通过理论分析和仿真验证,证明了该方案的可行性和实用性,这种组合模型具有较强的数据逼近能力,可有效提高算法的运算效率,可用于需求量波动或饱和阶段的预测。
With the rapid development of shared bike(ofo),it is very important to study its short-term demand forecasting.According to the grey theory,the characteristics of GM(1,1)model are analyzed,and it is found that GM(1,1)model is applicable to monotone sequences with strong exponential law. Considering the short-term traffic flow data in a certain period of volatility and saturation,in order to improve the precision and efficiency of short-term prediction model,Grey Markov model is combined based on GM(1,1)model. Using ofo data from September 1,2017 to September 8 th,the Matlab software toolbox is used to validate the model by computer simulation through theoretical analysis and simulation prove the feasibility and practicability of the scheme.The combination model has strong data approximation ability,effectively improves the operational accuracy and efficiency of the algorithm,and can be used in the prediction of vehicle flow fluctuation or saturation stage.
With the rapid development of shared bike(ofo),it is very important to study its short-term demand forecasting.According to the grey theory,the characteristics of GM(1,1)model are analyzed,and it is found that GM(1,1)model is applicable to monotone sequences with strong exponential law. Considering the short-term traffic flow data in a certain period of volatility and saturation,in order to improve the precision and efficiency of short-term prediction model,Grey Markov model is combined based on GM(1,1)model. Using ofo data from September 1,2017 to September 8 th,the Matlab software toolbox is used to validate the model by computer simulation through theoretical analysis and simulation prove the feasibility and practicability of the scheme.The combination model has strong data approximation ability,effectively improves the operational accuracy and efficiency of the algorithm,and can be used in the prediction of vehicle flow fluctuation or saturation stage.
引文
[1]王光荣.共享单车发展问题系统探究[J].长安大学学报(社会科学版),2017,19(02):30-35.WANG Guangrong. Systematic Research on Bicycle-sharing Development[J]. Journal of Chang'an University(Social Science Edition),2017,19(02):30-35.
[2]丁诺舟.日本共享单车的历史、现状与启示[J].长安大学学报(社会科学版),2017,19(02):36-43.DING Nuozhou. History,Current Situation and Enlightenment of Japanese Bicycle-sharing[J].Journal of Chang'an University,2017,19(02):36-43.
[3]袁健,范炳全.交通流短时预测研究进展[J].城市交通,2012,10(06):73-79.YUAN Jian,FAN Bingquan. Synthesis of Short-Term Traffic Flow Forecasting Research Progress[J]. Urban Transport of China,2012,10(06):73-79.
[4]杨兆升,邴其春,周熙阳,等.基于时间序列相似性搜索的交通流短时预测方法[J].交通信息与安全,2014,32(6):22-26.YANG Zhaosheng,BING Qichun,ZHOU Xiyang,et al. A Short-term Traffic Flow Prediction Method Based on Similarity Search of Time Series[J]. Journal of Transport Information and Safety,2014,32(6):22-26.
[5]四兵锋,何九冉,任华玲,等.基于时序特征的城市轨道交通客流预测[J].北京交通大学学报,2014,38(3):1-6.SI Bingfeng,HE Jiuran,REN Hualing,et al. Urban Railway Traffic Passenger Flow Forecast Based on the Timing Characteristics[J]. Journal of Beijing Jiaotong University,2014,38(3):1-6.
[6]戴施华,周欣荣. Kalman滤波理论在短时交通预测上的应用[J].哈尔滨商业大学学报(自然科学版),2005,21(6):728-730.DAI Shihua,ZHOU Xinrong. Application of Kalman Filtering Theory on Prediction of Short-term Traffic Volume[J]. Journal of Harbin University of Commerce(Natural Sciences Edition),2005,21(6):728-730.
[7]乐友喜,王永刚.非参数回归法在孔隙度参数预测中的应用[J].地质科学,2002(01):118-126.YUE Youxi,WANG Yonggang. Nonparametric Regression Applied in Porosity Parameter Prediction[J]. Chinese Journal of Geology,2002(01):118-126.
[8]张秋余,朱学明.基于GA-Elman神经网络的交通流短时预测方法[J].兰州理工大学学报,2013,39(03):94-98.ZHANG Qiuyu,ZHU Xueming.Short-time Traffic Flow Forecasting Method Based on GA-Elman Neural Network[J]. Journal of Lanzhou University of Technology,2013,39(03):94-98.
[9]赵玲,许宏科.基于新维无偏灰色马尔可夫的交通事故预测[J].计算机工程与应用,2013,49(7):35-38.ZHAO Ling,XU Hongke. Traffic Accident Prediction Based on Equal Dimension and New Information Unbiased Grey Markov Model[J]. Computer Engineering and Applications,2013,49(7):35-38.
[10]李红梅.基于Matlab的短时交通流预测分析及实现[D].山东:山东科技大学,2007:21-25.LI Hongmei. Predicition of the Short Traffic Flow and Visuliazation Based on MATLAB[D]. Shandong:Shandong University of Science and Technology,2007:21-25.
[11]邓聚龙.灰色控制系统[J].华中科技大学学报(自然科学版),1982(3):11-20.DENG Julong. The Grey Control System[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition),1982(3):11-20.
[12]陈淑燕,陈家胜.一种改进的灰色模型在交通量预测中的应用[J].公路交通科技,2004(02):80-83.CHEN Shuyan,CHEN Jiasheng. Application of A Novel Grey Model to Traffic Flow Prediction[J]. Journal of Highway and Transportation Research and Development,2004(02):80-83.
[13]谢乃明,刘思峰.离散GM(1,1)模型与灰色预测模型建模机理[J].系统工程理论与实践,2005(1):93-99.XIE Naiming,LIU Sifeng. Discrete GM(1,1)and Mechanism of Grey Forecasting Model[J]. Systems Engineering-Theory&Practice,2005(1):93-99.
[14]于德江.灰色系统建模方法的探讨[J].系统工程,1991(05):9-12.YU Dejiang. A Discussion About Modelling of Grey Systems[J]. Systems Engineering,1991(05):9-12.
[15]周伟萍,王丰效.灰色DNGM(1,1)预测模型及其优化[J].计算机工程与应用,2013,49(10):28-31.ZHOU Weiping,WANG Fengxiao. DNGM(1,1)Model and Its Parameters Optimal[J]. Computer Engineering and Applications,2013,49(10):28-31.
[16]王正武,黄中祥.短时交通流预测模型的分析与评价[J].系统工程,2003(06):97-100.WANG Zhengwu,HUANG Zhongxiang. An Analysis and Discussion on Short-Term Traffic Flow Forecasting[J].Systems Engineering,2003(06):97-100.
[17]唐睿,冯学钢.基于马尔科夫预测的国内赴沪旅游者旅游消费结构分析[J].旅游论坛,2015,8(01):38-45.TANG Rui,FENG Xuegang. The Study on the Tourism Consumption Structure of Domestic Tourists in Shanghai Based on the Markov Prediction[J]. Tourism Forum,2015,8(01):38-45.
[18]严伟,钱育渝.马尔科夫预测在房地产市场营销中的应用研究[J].重庆建筑大学学报,2004,26(2):110-115.YAN Wei,QIAN Yuyu. A.A.Marcov Forecast Method of the System Ration in Estate Business Study[J]. Journal of Chongqing Jianzhu University,2004(4):110-115.
[19]陈建宏,郑海力,施飞,等.改进Verhulst模型与灰色效果测度的软岩巷道顶板破坏规律分析[J].中南大学学报(自然科学版),2011,42(9):2790-2796.CHEN Jianhong,ZHENG Haili,SHI Fei,et al. Improved Verhulst Model and Gray Effect Measure of Soft Rock Roof Failure Pattern Analysis[J]. Journal of Central South University(Science and Technology),2011,42(9):2790-2796.
[20]赵玲,许宏科.基于新维无偏灰色马尔可夫的交通事故预测[J].计算机工程与应用,2013,49(7):35-38.ZHAO Ling,XU Hongke. Traffic Accident Prediction Based on Equal Dimension and New Information Unbiased Grey Markov Model[J]. Computer Engineering and Applications,2013,49(7):35-38.
[2]丁诺舟.日本共享单车的历史、现状与启示[J].长安大学学报(社会科学版),2017,19(02):36-43.DING Nuozhou. History,Current Situation and Enlightenment of Japanese Bicycle-sharing[J].Journal of Chang'an University,2017,19(02):36-43.
[3]袁健,范炳全.交通流短时预测研究进展[J].城市交通,2012,10(06):73-79.YUAN Jian,FAN Bingquan. Synthesis of Short-Term Traffic Flow Forecasting Research Progress[J]. Urban Transport of China,2012,10(06):73-79.
[4]杨兆升,邴其春,周熙阳,等.基于时间序列相似性搜索的交通流短时预测方法[J].交通信息与安全,2014,32(6):22-26.YANG Zhaosheng,BING Qichun,ZHOU Xiyang,et al. A Short-term Traffic Flow Prediction Method Based on Similarity Search of Time Series[J]. Journal of Transport Information and Safety,2014,32(6):22-26.
[5]四兵锋,何九冉,任华玲,等.基于时序特征的城市轨道交通客流预测[J].北京交通大学学报,2014,38(3):1-6.SI Bingfeng,HE Jiuran,REN Hualing,et al. Urban Railway Traffic Passenger Flow Forecast Based on the Timing Characteristics[J]. Journal of Beijing Jiaotong University,2014,38(3):1-6.
[6]戴施华,周欣荣. Kalman滤波理论在短时交通预测上的应用[J].哈尔滨商业大学学报(自然科学版),2005,21(6):728-730.DAI Shihua,ZHOU Xinrong. Application of Kalman Filtering Theory on Prediction of Short-term Traffic Volume[J]. Journal of Harbin University of Commerce(Natural Sciences Edition),2005,21(6):728-730.
[7]乐友喜,王永刚.非参数回归法在孔隙度参数预测中的应用[J].地质科学,2002(01):118-126.YUE Youxi,WANG Yonggang. Nonparametric Regression Applied in Porosity Parameter Prediction[J]. Chinese Journal of Geology,2002(01):118-126.
[8]张秋余,朱学明.基于GA-Elman神经网络的交通流短时预测方法[J].兰州理工大学学报,2013,39(03):94-98.ZHANG Qiuyu,ZHU Xueming.Short-time Traffic Flow Forecasting Method Based on GA-Elman Neural Network[J]. Journal of Lanzhou University of Technology,2013,39(03):94-98.
[9]赵玲,许宏科.基于新维无偏灰色马尔可夫的交通事故预测[J].计算机工程与应用,2013,49(7):35-38.ZHAO Ling,XU Hongke. Traffic Accident Prediction Based on Equal Dimension and New Information Unbiased Grey Markov Model[J]. Computer Engineering and Applications,2013,49(7):35-38.
[10]李红梅.基于Matlab的短时交通流预测分析及实现[D].山东:山东科技大学,2007:21-25.LI Hongmei. Predicition of the Short Traffic Flow and Visuliazation Based on MATLAB[D]. Shandong:Shandong University of Science and Technology,2007:21-25.
[11]邓聚龙.灰色控制系统[J].华中科技大学学报(自然科学版),1982(3):11-20.DENG Julong. The Grey Control System[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition),1982(3):11-20.
[12]陈淑燕,陈家胜.一种改进的灰色模型在交通量预测中的应用[J].公路交通科技,2004(02):80-83.CHEN Shuyan,CHEN Jiasheng. Application of A Novel Grey Model to Traffic Flow Prediction[J]. Journal of Highway and Transportation Research and Development,2004(02):80-83.
[13]谢乃明,刘思峰.离散GM(1,1)模型与灰色预测模型建模机理[J].系统工程理论与实践,2005(1):93-99.XIE Naiming,LIU Sifeng. Discrete GM(1,1)and Mechanism of Grey Forecasting Model[J]. Systems Engineering-Theory&Practice,2005(1):93-99.
[14]于德江.灰色系统建模方法的探讨[J].系统工程,1991(05):9-12.YU Dejiang. A Discussion About Modelling of Grey Systems[J]. Systems Engineering,1991(05):9-12.
[15]周伟萍,王丰效.灰色DNGM(1,1)预测模型及其优化[J].计算机工程与应用,2013,49(10):28-31.ZHOU Weiping,WANG Fengxiao. DNGM(1,1)Model and Its Parameters Optimal[J]. Computer Engineering and Applications,2013,49(10):28-31.
[16]王正武,黄中祥.短时交通流预测模型的分析与评价[J].系统工程,2003(06):97-100.WANG Zhengwu,HUANG Zhongxiang. An Analysis and Discussion on Short-Term Traffic Flow Forecasting[J].Systems Engineering,2003(06):97-100.
[17]唐睿,冯学钢.基于马尔科夫预测的国内赴沪旅游者旅游消费结构分析[J].旅游论坛,2015,8(01):38-45.TANG Rui,FENG Xuegang. The Study on the Tourism Consumption Structure of Domestic Tourists in Shanghai Based on the Markov Prediction[J]. Tourism Forum,2015,8(01):38-45.
[18]严伟,钱育渝.马尔科夫预测在房地产市场营销中的应用研究[J].重庆建筑大学学报,2004,26(2):110-115.YAN Wei,QIAN Yuyu. A.A.Marcov Forecast Method of the System Ration in Estate Business Study[J]. Journal of Chongqing Jianzhu University,2004(4):110-115.
[19]陈建宏,郑海力,施飞,等.改进Verhulst模型与灰色效果测度的软岩巷道顶板破坏规律分析[J].中南大学学报(自然科学版),2011,42(9):2790-2796.CHEN Jianhong,ZHENG Haili,SHI Fei,et al. Improved Verhulst Model and Gray Effect Measure of Soft Rock Roof Failure Pattern Analysis[J]. Journal of Central South University(Science and Technology),2011,42(9):2790-2796.
[20]赵玲,许宏科.基于新维无偏灰色马尔可夫的交通事故预测[J].计算机工程与应用,2013,49(7):35-38.ZHAO Ling,XU Hongke. Traffic Accident Prediction Based on Equal Dimension and New Information Unbiased Grey Markov Model[J]. Computer Engineering and Applications,2013,49(7):35-38.