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摘要: 根据时间序列中的自相关函数法, 判断交通流量、时间占有率与平均速度的时间序列的平稳性。根据混沌分析中的G-P算法, 将非平稳的交通流参数时间序列转化为平稳的交通流参数时间序列。引入了互相关系数的概念, 在阻塞流状态下, 计算了上游断面对观测断面以及观测断面对下游断面的互相关系数, 并应用K-S检验判断阻塞流状态下城市快速路进出口匝道的车辆到达特性。研究结果表明: 交通流量和时间占有率属于非平稳时间序列, 平均速度属于平稳时间序列; 当时间延迟分别取2、3、5min时, 在阻塞流状态下, 重构的交通流量相空间嵌入维数为4;观测断面的交通流参数不仅受相邻上游断面交通流参数传递的影响, 而且也受相邻下游断面交通流参数回溯的影响; 在阻塞流状态下, 城市快速路进出口匝道车辆到达特性符合负二项分布。Abstract: Based on the auto-correlation function method of time sequence, the stationarities of time sequences for traffic flow, time occupancy and average speed were judged.Based on the G-P algorithm of chaos analysis, the non-stationary time sequence of traffic flow parameter was transformed to the stationary time sequence of traffic flow parameter.The concept of cross-correlation coefficient was introduced.Under jam flow condition, the cross-correlation coefficients of upstream section on observation section and observation section on downstream section were calculated, and K-S test were used to determine the characteristics of vehicle arrival at import and export ramps on urban expressway.Research result shows that traffic flow and time occupancy belong to non-stationary time sequence, but average speed belongs to stationary time sequence.When time lags are 2, 3 and 5 min respectively, the embedding dimension of reconstruction phase space is 4 under jam flow condition.The traffic flow parameters of observation section is not only influenced by the traffic flow parameters transmission of adjacent upstream section, but also influenced by the traffic flow parameters backtrack of adjacent downstream section.Under jam flow condition, the characteristics of vehicle arrival at import and export ramps on urban expressway are accordance with the negative binomial distribution.
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[1] 董春娇. 多状态下城市快速路网交通流短时预测理论与方法研究[D]. 北京: 北京交通大学, 2011.DONG Chun-jiao. Theoretical research for short-term traffic flow prediction in multi traffic states on urban expressway network[D]. Beijing: Beijing Jiaotong University, 2011. (in Chinese). [2] WANG Yi-bing, PAPAGEORGIOU M. Real-time freeway traffic state estimation based on extended Kalman filter: a general approach[J]. Transportation Research Part B: Meth-odological, 2005, 39(2): 141-167. doi: 10.1016/j.trb.2004.03.003 [3] 董春娇, 邵春福, 诸葛承祥. 混合状态下城市快速路交通流短时预测[J]. 物理学报, 2012, 61(1): 1-9. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201201008.htmDONG Chun-jiao, SHAO Chun-fu, ZHUGE Cheng-xiang. Short-term traffic flow prediction for multi traffic states on urban expressway network[J]. Acta Physica Sinica, 2012, 61(1): 1-9. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201201008.htm [4] KERNER B S. Three-phase traffic theory and highway capacity[J]. Physica A, 2004, 333(0): 379-440. https://www.sciencedirect.com/science/article/pii/S0378437103009221 [5] 石小法, 喻军皓. 快速城市化地区中等城市交通特性[J]. 交通运输工程学报, 2010, 10(2): 88-94. http://transport.chd.edu.cn/article/id/201002016SHI Xiao-fa, YU Jun-hao. Traffic characteristics of medium city in fast urbanization area[J]. Journal of Traffic and Transportation Engineering, 2010, 10(2): 88-94. (in Chinese). http://transport.chd.edu.cn/article/id/201002016 [6] 姚荣涵, 王殿海. 交通流时空描述模型[J]. 公路交通科技, 2008, 25(10): 111-116. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK202108018.htmYAO Rong-han, WANG Dian-hai. Time-space description model of traffic flow[J]. Journal of Highway and Transporta-tion Research and Development, 2008, 25(10): 111-116. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK202108018.htm [7] DONG Chun-jiao, SHAO Chun-fu, ZHAO Dan, et al. Short-term traffic flow forecasting based on periodicity simi-larity[C]∥IEEE. Proceeding of the2011First IRAST Inter-national Conference on Data Engineering and Internet Tech-nology. Bali: IEEE, 2011: 148-151. [8] CHIABAUT N, BUISSON C, LECKERCQ L. Fundemental diagram estimation through passing rate measurements in congestion[J]. IEEE Transactions on Intelligent Transporta-tion Systems, 2009, 10(2): 355-359. doi: 10.1109/TITS.2009.2018963 [9] HELBING D. Derivation of a fundamental diagram for urban traffic flow[J]. The European Physical Journal B: Condensed Matter and Complex Systems, 2009, 70(2): 229-241. doi: 10.1140/epjb/e2009-00093-7 [10] KERNER B S, KLENOV S L, HILLER A. Empirical test of a microscopic three-phase traffic theory[J]. Nonlinear Dynamics, 2007, 49(4): 525-553. doi: 10.1007/s11071-006-9113-1 [11] DONG Chun-jiao, SHAO Chun-fu, LI Xia. Short-term traf-fic flow forecasting of road network based on spatial-temporal characteristics of traffic flow[C]∥IEEE. 2009WRI World Congress on Computer Science and Information Engineering. Los Angeles: IEEE, 2009: 645-650. [12] HEIBING D. Traffic and related self-driven many-particle systems[J]. Reviews of Modern Physics, 2001, 73(4): 1067-1141. doi: 10.1103/RevModPhys.73.1067 [13] GOMES G, HOROWITZ R. Optimal freeway ramp metering using the asymmetric cell transmission model[J]. Transpor-tation Research Part C: Emerging Technologies, 2006, 14(4): 244-262. https://www.sciencedirect.com/science/article/pii/S0968090X06000544 -
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