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 |
[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. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|