基于SARIMA模型的贵阳市尾号限行政策影响研究
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摘要
研究SARIMA模型在城市机动车普通号牌尾号限行取消后号牌增量预测中的应用价值。通过收集贵阳市2011年7月至2018年7月专段号牌和普通号牌的新增办理量数据,建立乘积季节性SARIMA模型,对2018年取消普通号牌尾号限行政策后普通号牌新增办理量进行预测。SARIMA模型能够较好地拟合每月普通号牌新增办理量的情况,平均相对误差为0.0919。结合新政策实行后贵阳市一环内号牌分析,为贵阳市号牌限行政策的制定提供数据支撑和交通治理依据。
This paper studies the application value of SARIMA model in predicting the increment of city car's ordinary license plate after the end number limit is cancelled. By collecting the new traffic volume of special section and ordinary license plate from July 2011 to July 2018 in Guiyang,SARIMA model was established to predict the new traffic volume of ordinary license plate after canceling the policy of ending number limit of ordinary license plate in 2018. The SARIMA model can well fit the monthly increase of the number of ordinary license plates,and the average relative error is 0.0919. Combined with the analysis of the license plate in the inner ring of Guiyang after the implementation of the new policy,this paper provides data support and traffic management basis for the formulation of the policy of license plate restriction in Guiyang.
This paper studies the application value of SARIMA model in predicting the increment of city car's ordinary license plate after the end number limit is cancelled. By collecting the new traffic volume of special section and ordinary license plate from July 2011 to July 2018 in Guiyang,SARIMA model was established to predict the new traffic volume of ordinary license plate after canceling the policy of ending number limit of ordinary license plate in 2018. The SARIMA model can well fit the monthly increase of the number of ordinary license plates,and the average relative error is 0.0919. Combined with the analysis of the license plate in the inner ring of Guiyang after the implementation of the new policy,this paper provides data support and traffic management basis for the formulation of the policy of license plate restriction in Guiyang.
引文
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[3]陈磊,李庚.天津市尾号限行政策的接受度及其影响因素研究[J].天津大学学报(社会科学版),2017,19(6):520-524.
[4]RUBY-FIGUEROA R,SAAVEDRA J,BAHAMONDE N,et al.Permeate flux prediction in the ultrafiltration of fruit juices by ARIMA models[J]. Journal of Membrane Science,2017,524:108-116.
[5]史其信,郑为中.道路网短期交通流预测方法比较[J].交通运输工程学报,2004,4(4):68-83.
[6]TANEJA K,AHMAD S,AHMAD K,et al. Time series analysis of aerosol optical depth over New Delhi using Box-Jenkins ARIMA modeling approach[J]. Atmospheric Pollution Research,2016,7(4):585-596.
[7]李红,潘东峰,郭忠琴,等.时间序列模型在医院感染发生率拟合预测中的比较研究[J].中国卫生统计,2013,30(1):87-89.
[8]王莹,韩宝明,张琦,等.基于SARIMA模型的北京地铁进站客流量预测[J].交通运输系统工程与信息,2015,15(6),205-211.
[9]王清青,范馨月,李凌竹,等.乘积季节性ARIMA模型在重症监护病房医院感染发病率预测中的应用[J].中国消毒学杂志,2018,35(6):432-435.
[10]张梦迪,黄玮青,安姝静.基于X-12-ARIMA模型的公路运价季节性波动影响研究[J].铁道运输与经济,2018,40(4):8-12.
[11]高雅,王伶,吴伟,等.辽宁省手足口病疫情季节性ARIMA模型预测效果评价[J].中国公共卫生,2017,32(10):1482-1484.
[12]王燕.应用时间序列分析[M].北京:中国人民大学出版社,2012.
[13]杨雨,李庚,王蓉,等.限行政策对道路交通流的影响研究[J].交通信息与安全,2016,34(1).
[14]吴德瑄,李睿,武晋飞.城市小汽车尾号限行的经济学分析[J].交通科技与经济,2012,14(2):100-103.
[15]陶志梅,阮婷.基于系统动力学的城市交通拥堵问题治理研究[J].西北师范大学学报(自然科学版),2018,54(2):108-113.
[16]祝超,孙玲,顾涛,等.北京市交通需求管理政策20年发展历程及反思[J].交通运输研究,2018,4(3):1-8.