Please wait a minute...
浙江大学学报(工学版)  2018, Vol. 52 Issue (4): 727-734    DOI: 10.3785/j.issn.1008-973X.2018.04.016
土木工程     
基于复杂性测度的泊位占有率序列动力学分析
梅振宇, 章伟
浙江大学 建筑工程学院, 浙江 杭州 310058
Dynamicsanalysis of parking space occupancy series based oncomplexity measurement
MEI Zhen-yu, ZHANG Wei
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
 全文: PDF(1373 KB)   HTML
摘要:

为了定量分析停车场泊位占有率时间序列的动力学特性,对序列进行复杂性测度分析.利用主分量分析方法分析序列的主分量谱图,判断序列的混沌特性;计算序列的联合熵分析序列的非线性特性,计算序列的C0复杂度分析序列中的非规则成分,综合两种复杂性测度方法对序列的动力学特性进行分析.对比分析几种典型序列和泊位占有率序列发现,泊位占有率时间序列的线性特征更明显,序列中所含的规则成分较多,是一种“拟周期”序列.利用C0复杂度的思想剔除不规则成分,对序列进行长时预测,结果表明,剔除不规则成分后的预测精度提高了26%~56%,长时预测效果提高显著.

Abstract:

The analysis of the sequence was conducted based on complexity measurement in order to quantitatively analyze the dynamic characteristics of parking space occupancy time series. The principal component analysis was used to analyze the principal component spectrum of the sequence. The nonlinear characteristic of the sequence was calculated by the joint entropy of the sequence. Irregular components of the sequence were analyzed by calculating the C0 complexity of the sequence. The analysis of comparing with several typical time series shows that the time series of parking space occupancy is a kind of sequence which situated between linearity and nonlinearity, and its linear characteristic is more significant. The sequence contains more regular components, which can be seen as a ‘quasi-periodic’ sequence, but the extremely irregular components in the original series can increase the prediction error in long-term prediction. Long-term prediction of parking space series was conducted by using the idea of C0 complexity. Results show that the prediction accuracy increases by 26% to 56% after eliminating irregular components in the original series, which shows better performance.

收稿日期: 2017-01-09
CLC:  U491  
基金资助:

国家自然科学基金资助项目(51338008);浙江省自然科学基金面上项目(Y15E080021).

作者简介: 梅振宇(1979-),男,副教授,从事交通规划研究.orcid.org/0000-0001-8752-4461.E-mail:meizhenyu@zju.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  

引用本文:

梅振宇, 章伟. 基于复杂性测度的泊位占有率序列动力学分析[J]. 浙江大学学报(工学版), 2018, 52(4): 727-734.

MEI Zhen-yu, ZHANG Wei. Dynamicsanalysis of parking space occupancy series based oncomplexity measurement. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(4): 727-734.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2018.04.016        http://www.zjujournals.com/eng/CN/Y2018/V52/I4/727

[1] SHOUP D. Cruising for parking[J]. Transport Policy, 2006, 13(6):479-486.
[2] THOMPSONR G, BONSALL P. Drivers' response to parking guidance and information systems[J]. Transport Reviews, 1997, 17(2):89-104.
[3] LIU W, YANG H, YIN Y. Expirable parking reservations for managing morning commute with parking space constraints[J]. Transportation Research Part C Emerging Technologies, 2014, 44(4):185-201.
[4] MAJIDI A, POLAT H, ÇETIN A. Finding a best parking place using exponential smoothing and cloud system in a metropolitan area[C]//International Istanbul Smart Grid Congress and Fair. Istanbul:[s.n.], 2016.
[5] CALISKAN M, BARTHELS A, SCHEUERMANN B, et al. Predicting parking lot occupancy in vehicular Ad Hoc networks[C]//Vehicular Technology Conference.Dublin:[s.n.], 2007:277-281.
[6] JI Y, TANG D, BLYTHE P, et al. Short-term forecasting of available parking space using wavelet neural network model[J]. Journal of Southeast University, 2014, 9(2):202-209.
[7] ZHENG Y, RAJASEGARAR S, LECKIE C. Parking availability prediction for sensor-enabled car parks in smart cities[C]//IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing. Singapore:IEEE, 2015.
[8] JI Y, TANG D, GUO W, et al. Forecasting available parking space with largest Lyapunov exponents method[J]. Journal of Central South University, 2014, 21(4):1624-1632.
[9] 陈群,晏克非,王仁涛,等. 基于相空间重构及Elman网络的停车泊位数据预测[J]. 同济大学学报:自然科学版,2007,35(5):607-611. CHEN Qun, YAN Ke-fei, WANG Ren-tao, et al. Parking space information prediction based on phrase construction and Elman neuraI network[J]. Journal of Tongji University:Natural Science, 2007, 35(5):607-611.
[10] SHUMWAY R H, STOFFER D S. Time series analysis and its applications[M]. New York:Springer, 2009:119-160.
[11] 吕金虎,陆君安, 陈士华. 混沌时间序列分析及其应用[M]. 武汉:武汉大学出版社,2002:49-92.
[12] LI P, LI K, LIU C, et al. Detection of coupling in short physiological series by a joint distribution entropy method[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(11):2231-2242.
[13] 李锦,宁新宝,马千里. 用联合熵分析短时心率变异信号的非线性动力学复杂性[J]. 生物医学工程学杂志,2007,24(2):285-289. LI Jin, NING Xin-bao, MA Qian-li. Nonlinear dynamical complexity analysis of short-term heartbeat series using joint entropy[J]. Journal of Biomedical Engineering, 2007, 24(2):285-289.
[14] 张勇,关伟. 基于联合熵和C0复杂度的交通流复杂性测度[J]. 计算机工程与应用,2010,46(15):22-24. ZHANG Yong, GUAN Wei. Complexity measure of traffic flow based on union entropy and C0 complexity[J]. Computer Engineering and Applications, 2010, 46(15):22-24.
[15] TONG C, HUANG Q, LIU H. Analysis on runoff time series dynamics character based on complexity theory[J]. Systems Engineering-theory and Practice, 2004, 9:102-107.
[16] 蔡志杰,孙洁. 改进的C0复杂度及其应用[J]. 复旦学报:自然科学版,2008,47(6):791-796. CAI Zhi-jie,SUN Jie. Modified C0 complexity and applications[J]. Journal of Fudan University:Natural Science, 2008, 47(6):791-796.
[17] 科费, 托马斯. 信息论基础[M]. 北京:机械工业出版社,2005:7-13.
[18] 史永胜,姜颖,宋云雪. 基于符号序列联合熵的航空发动机健康监控方法[J]. 航空动力学报,2011,26(3):670-674. SHI Yong-sheng, JIANG Ying, SONG Yun-xue. Aero-engine health monitoring method based on joint entropy of symbolic series[J]. Journal of Aerospace Power, 2011, 26(3):670-674.
[19] 雷敏,王志中. 非线性时间序列的替代数据检验方法研究[J]. 电子与信息学报,2001,23(3):248-254. LEI Min, WANG Zhi-zhong. Study of the surrogate data method for nonlinearity of time series[J]. Journal of Electronics and Information Technology, 2001, 23(3):248-254.
[20] GESTEL T, SUYKENS J A K, BASTAENS D E, et al. Financial time series prediction using least squares support vector machines within the evidence framework[J]. IEEE Transactions on Neural Networks, 2001, 12(4):809-821.

[1] 张帅超, 朱谊, 陈喜群. 基于移动检测数据的宏观基本图特征[J]. 浙江大学学报(工学版), 2018, 52(7): 1338-1344.
[2] 李文婧, 孙锋, 李茜瑶, 马东方. 采用递归有序聚类的信号控制时段划分方法[J]. 浙江大学学报(工学版), 2018, 52(6): 1150-1156.
[3] 阮树斌, 王福建, 马东方, 金盛, 王殿海. 基于车牌识别数据的机动车出行轨迹提取算法[J]. 浙江大学学报(工学版), 2018, 52(5): 836-844.
[4] 龚越, 罗小芹, 王殿海, 杨少辉. 基于梯度提升回归树的城市道路行程时间预测[J]. 浙江大学学报(工学版), 2018, 52(3): 453-460.
[5] 曲昭伟, 罗瑞琪, 陈永恒, 曹宁博, 邓晓磊, 汪昆维. 信号交叉口右转机动车轨迹特性[J]. 浙江大学学报(工学版), 2018, 52(2): 341-351.
[6] 曹宁博, 陈永恒, 曲昭伟, 赵利英, 白乔文, 杨秋杰. 基于社会力模型的行人路径选择模型[J]. 浙江大学学报(工学版), 2018, 52(2): 352-357.
[7] 杨庆芳, 赵小辉, 郑黎黎, 张伟. 基于模型预测控制的环形交叉口信号配时方法[J]. 浙江大学学报(工学版), 2018, 52(1): 117-124.
[8] 杨方宜, 李铁柱. 大型综合客运枢纽送站坪交通特性及通行能力[J]. 浙江大学学报(工学版), 2017, 51(11): 2207-2214.
[9] 吴江玲, 张生瑞, Amit Kumar Singh, 秦思, 孙振东. 高速公路强制换道持续时间半参数生存分析[J]. 浙江大学学报(工学版), 2017, 51(11): 2215-2221.
[10] 于德新, 田秀娟, 杨兆升, 周熙阳, 程泽阳. 改进的干线协调信号控制优化模型[J]. 浙江大学学报(工学版), 2017, 51(10): 2019-2029.
[11] 季学斌, 王慧, 宋春跃. 基于元胞自动机的施工场内交通流建模及安全分析[J]. 浙江大学学报(工学版), 2017, 51(10): 2005-2011.
[12] 李显生, 孟凡淞, 郑雪莲, 任园园, 严佳晖. 交通冲突类型对驾驶人生理特性的影响[J]. 浙江大学学报(工学版), 2017, 51(9): 1720-1726.
[13] 王薇, 程泽阳, 刘梦依, 杨兆升. 基于时空相关性的交通流故障数据修复方法[J]. 浙江大学学报(工学版), 2017, 51(9): 1727-1734.
[14] 商强, 林赐云, 杨兆升, 邴其春, 邢茹茹. 基于变量选择和核极限学习机的交通事件检测[J]. 浙江大学学报(工学版), 2017, 51(7): 1339-1346.
[15] 刘美岐, 沈莉潇, 金盛. 考虑右转信号控制的共用车道通行能力模型[J]. 浙江大学学报(工学版), 2017, 51(7): 1347-1354.