-
摘要: 为了提高交通流数据的准确性, 从时间相关性、空间相关性和历史相关性三方面分析了交通流大数据的特点, 建立了基础交通流时空模型。为保证数据处理的精度和速度, 进行了时空模型的简化和标定。将时空模型简化, 抽象为双层规划模型, 上层模型通过控制时空相关参数的数量实现运算速度的优化, 下层模型通过控制误差实现计算精度的优化。应用数据驱动法进行双层规划模型的求解, 完成时空模型的标定。在时空模型的基础上, 提出了交通流故障数据修正方法。以北京市某路段为例, 对交通流故障数据修正方法进行有效性和可行性验证。验证结果表明: 基于历史趋势、空间相关与时间序列的交通流故障数据修正方法的精度分别为79.65%、85.16%、89.84%, 基于时空模型的交通流故障数据修正方法的精度为90.91%, 具有较高的精度, 而且可准确描述交通流大数据的特点。Abstract: In order to improve the accuracy of traffic flow data, the characteristics of temporal correlation, spatial correlation and historical correlation of traffic flow big data were considered, and a basic traffic flow temporal-spatial model was built.To ensure the accuracy and speed of data processing, the simplification and calibration of temporal-spatial model were realized.The temporal-spatial model was simplified and abstracted into a bi-level programming model.The operation speed was optimized in the upper level model by controlling the number of temporalspatial correlation coefficients, and the calculation accuracy was optimized in the lower level model by controlling the error.Based on the data-driven method, the bi-level programming model was solved, and the calibration of temporal-spatial model was completed.Based on the proposed temporal-spatial model, a repair method of traffic flow malfunction data was presented.Taking a road section in Beijing City as example, the validity and feasibility of proposed repair method were verified.Verification result indicates that the precisions of repair methods of traffic flow malfunction data based on historical trend, spatial correlation and time series are 79.65%, 85.16%, 89.84% respectively, however, the precision of proposed method based on the temporal-spatial model is 90.91%, that is relatively higher, the characteristics of traffic flow big data can be accurately described by the proposed repair method.
-
表 1 时空相关系数
Table 1. Temporal-spatial correlation coefficients
表 2 不同方法的精度
Table 2. Precisions of different methods
-
[1] FRANKEL F, REID R. Big data: distilling meaning from data[J]. Nature, 2008, 455(7209): 30. doi: 10.1038/455030a [2] LOS W, WOOD J. Dealing with data: upgrading infrastructure[J]. Science, 2011, 331(6024): 1515-1516. [3] 陆化普, 孙智源, 屈闻聪. 大数据及其在城市智能交通系统中的应用综述[J]. 交通运输系统工程与信息, 2015, 15(5): 45-52. doi: 10.3969/j.issn.1009-6744.2015.05.007LU Hua-pu, SUN Zhi-yuan, QU Wen-cong. Big data and its applications in urban intelligent transportation system[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5): 45-52. (in Chinese). doi: 10.3969/j.issn.1009-6744.2015.05.007 [4] DONG Hong-hui, SUN Xiao-liang, JIA Li-min, et al. Traffic condition estimation with a pre-selection space time model[J]. Journal of Central South University of Technology: English Edition, 2012, 19(1): 206-212. doi: 10.1007/s11771-012-0993-6 [5] DARAGHMIY A, YI C W, CHIANG T C. Negative binomial additive models for short-term traffic flow forecasting in urban areas[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2): 784-793. doi: 10.1109/TITS.2013.2287512 [6] ARNAOUT G M, BOWLING S. A progressive deployment strategy for cooperative adaptive cruise control to improve traffic dynamics[J]. International Journal of Automation and Computing, 2014, 11(1): 10-18. doi: 10.1007/s11633-014-0760-2 [7] CHEN Da-shan, XIA Li-guo, GUO Da-yuan, et al. Space optimization of speed guidance control on urban expressway[J]. International Journal of Control and Automation, 2014, 7(9): 215-224. doi: 10.14257/ijca.2014.7.9.18 [8] 王晓原, 张敬磊, 吴芳. 交通流数据清洗规则研究[J]. 计算机工程, 2011, 37(20): 191-193.WANG Xiao-yuan, ZHANG Jing-lei, WU Fang. Research on traffic flow data cleaning rules[J]. Computer Engineering, 2011, 37(20): 191-193. (in Chinese). [9] 刘东波, 代磊磊, 李娅, 等. 基于信号周期计算的交叉口管控时段划分[J]. 吉林大学学报: 工学版, 2013, 43(6): 1471-1475.LIU Dong-bo, DAI Lei-lei, LI Ya, et al. Intersection traffic control period division method based on signal cycle calculation[J]. Journal of Jilin University: Engineering and Technology Edition, 2013, 43(6): 1471-1475. (in Chinese). [10] 金逸文, 王景成. 基于相关分析的交通流参数修正方法研究[J]. 控制工程, 2008, 15(增): 142-144, 147. https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF2008S1046.htmJIN Yi-wen, WANG Jing-cheng. Modifying of traffic flow data based on correlation analysis[J]. Control Engineering of China, 2008, 15(S): 142-144, 147. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF2008S1046.htm [11] CHANG Gang, ZHANG Yi, YAO Dan-ya. Missing data imputation for traffic flow based on improved local least squares[J]. Tsinghua Science and Technology, 2012, 17(3): 304-309. doi: 10.1109/TST.2012.6216760 [12] 郭敏, 蓝金辉, 李娟娟, 等. 基于灰色残差GM(1, N)模型的交通流数据恢复算法[J]. 交通运输系统工程与信息, 2012, 12(1): 42-47. doi: 10.3969/j.issn.1009-6744.2012.01.008GUO Min, LAN Jin-hui, LI Juan-juan, et al. Traffic flow data recovery algorithm based on gray residual GM(1, N)model[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(1): 42-47. (in Chinese). doi: 10.3969/j.issn.1009-6744.2012.01.008 [13] 李琦, 姜桂艳. 城市快速路车辆检测器数据质量评价与控制方法[J]. 交通运输工程学报, 2013, 13(2): 120-126. doi: 10.3969/j.issn.1671-1637.2013.02.018LI Qi, JIANG Gui-yan. Quality evaluation and control method of vehicle detector data on urban expressway[J]. Journal of Traffic and Transportation Engineering, 2013, 13(2): 120-126. (in Chinese). doi: 10.3969/j.issn.1671-1637.2013.02.018 [14] 陆化普, 屈闻聪, 孙智源. 基于S-G滤波的交通流故障数据识别与修复算法[J]. 土木工程学报, 2015, 48(5): 123-128. https://www.cnki.com.cn/Article/CJFDTOTAL-TMGC201505018.htmLU Hua-pu, QU Wen-cong, SUN Zhi-yuan. Detection and repair algorithm of traffic erroneous data based on S-G filtering[J]. China Civil Engineering Journal, 2015, 48(5): 123-128. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TMGC201505018.htm [15] 赵玲, 王佳璆, 邓敏, 等. 基于行车时间的道路网络时空自相关模型研究[J]. 中南大学学报: 自然科学版, 2012, 43(10): 4114-4122.ZHAO Ling, WANG Jia-qiu, DENG Min, et al. Spatialtemporal autocorrelation model of road network based on travelling time[J]. Journal of Central South University: Science and Technology, 2012, 43(10): 4114-4122. (in Chinese). [16] 陈绍宽, 韦伟, 毛保华, 等. 基于改进时空Moran's I指数的道路交通状态特征分析[J]. 物理学报, 2013, 62(14): 519-525.CHEN Shao-kuan, WEI Wei, MAO Bao-hua, et al. Analysis on urban traffic status based on improved spatio-temporal Moran's I[J]. Acta Physica Sinica, 2013, 62(14): 519-525. (in Chinese). [17] LIANG Zi-lu, WAKAHARA Y. Real-time urban traffic amount prediction models for dynamic route guidance systems[J]. EURASIP Journal on Wireless Communications and Networking, 2014, 2014(8): 1018-1030. [18] LI Li, CHEN Xi-qun, LI Zhi-heng, et al. Freeway traveltime estimation based on temporal-spatial queueing model[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3): 1536-1541. doi: 10.1109/TITS.2013.2256132 [19] WU Shan-hua, YANG Zhong-zhen, ZHU Xiao-cong, et al. Improved k-nn for short-term traffic forecasting using temporal and spatial information[J]. Journal of Transportation Engineering, 2014, 140(7): 1-9. [20] 弓晋丽, 彭贤武. 城市道路交通流时间序列模式相似性分析[J]. 公路交通科技, 2013, 30(11): 119-123. doi: 10.3969/j.issn.1002-0268.2013.11.019GONG Jin-li, PENG Xian-wu. Analysis of similarity of pattern of traffic flow time series on urban road[J]. Journal of Highway and Transportation Research and Development, 2013, 30(11): 119-123. (in Chinese). doi: 10.3969/j.issn.1002-0268.2013.11.019 [21] 刘小明, 及延辉, 李颖宏, 等. 交通瓶颈影响下两路口联动控制策略研究[J]. 武汉理工大学学报: 交通科学与工程版, 2012, 36(1): 29-33.LIU Xiao-ming, JI Yan-hui, LI Ying-hong, et al. Study on two intersections linkage control under traffic bottleneck[J]. Journal of Wuhan University of Technology: Transportation Science and Engineering, 2012, 36(1): 29-33. (in Chinese). [22] 孙立光, 李瑞敏, 董屾, 等. 短时交通流组合预测模型研究[J]. 武汉理工大学学报: 交通科学与工程版, 2010, 34(5): 874-876, 881.SUN Li-guang, LI Rui-min, DONG Shen, et al. Study on short-term traffic flow combined forecast method[J]. Journal of Wuhan University of Technology: Transportation Science and Engineering, 2010, 34(5): 874-876, 881. (in Chinese). [23] PENG Yu, LEI Miao, LI Jun-bao, et al. A novel hybridization of echo state networks and multiplicative seasonal ARIMA model for mobile communication traffic series forecasting[J]. Neural Computing and Applications, 2014, 24(3/4): 883-890. [24] 于滨, 邬珊华, 王明华, 等. K近邻短时交通流预测模型[J]. 交通运输工程学报, 2012, 12(2): 105-111. http://transport.chd.edu.cn/article/id/201202015YU Bin, WU Shan-hua, WANG Ming-hua, et al. K-nearest neighbor model of short-term traffic flow forecast[J]. Journal of Traffic and Transportation Engineering, 2012, 12(2): 105-111. (in Chinese). http://transport.chd.edu.cn/article/id/201202015 [25] 夏英, 梁中军, 王国胤. 基于时空分析的短时交通流量预测模型[J]. 南京大学学报: 自然科学, 2010, 46(5): 552-560. https://www.cnki.com.cn/Article/CJFDTOTAL-NJDZ201005010.htmXIA Ying, LIANG Zhong-jun, WANG Guo-yin. Research of short-term traffic flow forecasting model based on spatio-temporal analysis[J]. Journal of Nanjing University: Natural Sciences, 2010, 46(5): 552-560. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-NJDZ201005010.htm -
下载:
点击查看大图
图(19) / 表(2)
计量
- 文章访问数: 591
- HTML全文浏览量: 110
- PDF下载量: 845
- 被引次数: 0
