考虑交通大数据的交通检测器优化布置模型 |
孙智源, 陆化普 |
清华大学 土木工程系, 交通研究所, 北京 100084 |
Optimal traffic sensor layout model considering traffic big data |
SUN Zhiyuan, LU Huapu |
Institute of Transport Engineering, Department of Civil Engineering, Tsinghua University, Beijing 100084, China |
摘要:
| |||
摘要为了提高城市交通信息采集的准确性、可靠性和经济性,提出了一种交通检测器优化布置模型。大数据背景下,考虑系统成本、多源数据共享、数据需求、检测器故障、道路基础设施、检测器类型等因素,构建了交通检测器布置的影响因素集。综合分析各个影响因素,提出了由最小系统成本优化、最大截断流优化、最小包含路径优化和OD (origin-destination)覆盖约束构成的多目标优化模型。应用基于遗传算法的宽容分层序列法,对模型进行求解。算例研究表明:该文的模型实现了多目标的优化,反映了多源数据共享和检测器故障的影响,满足了OD覆盖约束,可达到交通检测器的优化布置。 | |||
关键词 :交通调查,交通检测器优化布置,多目标优化,交通大数据,遗传算法,宽容分层序列 | |||
Abstract:An optimal traffic sensor layout model was developed to improve the accuracy, reliability and economy of urban traffic information collection. The traffic sensor layout was optimized in light of big data traffic information with the system optimized with consideration of the system cost, multi-source data sharing, data demand, fault conditions, road infrastructure, and different types of sensors. The impact of these influential factors was taken into account in a multi-objective programming model that included system cost minimization, traffic flow intercept maximization, path coverage minimization, and an origin-destination(OD) coverage constraint. The model was solved by the tolerant lexicographic method based on a genetic algorithm. A case study shows that the model provides multi-objective optimization, reflects the influence of multi-source data sharing and fault conditions, satisfies the origin-destination coverage constraint, and provides the optimal traffic sensor layout. | |||
Key words:traffic surveyoptimal traffic sensor layoutmulti-objective programmingtraffic big datagenetic algorithmtolerant lexicographic method | |||
收稿日期: 2015-05-20 出版日期: 2016-07-22 | |||
| |||
基金资助:“十二五”国家科技支撑计划资助项目(2014BAG01B04);清华大学苏州汽车研究院(吴江)返校经费课题(2015WJ-B-02) | |||
通讯作者:陆化普,教授,E-mail:luhp@tsinghua.edu.cnE-mail: luhp@tsinghua.edu.cn |
引用本文: |
孙智源, 陆化普. 考虑交通大数据的交通检测器优化布置模型[J]. 清华大学学报(自然科学版), 2016, 56(7): 743-750. SUN Zhiyuan, LU Huapu. Optimal traffic sensor layout model considering traffic big data. Journal of Tsinghua University(Science and Technology), 2016, 56(7): 743-750. |
链接本文: |
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2016.24.025或 http://jst.tsinghuajournals.com/CN/Y2016/V56/I7/743 |
图表:
图1 NguyenGDupuis网络 |
表1 OD 交通需求 |
表2 有效路径集合及流量 |
图2 最小系统成本随点位数的变化图 |
图3 最大截断流随点位数的变化图 |
图4 最小包含路径随点位数的变化图 |
参考文献:
[1] Kwon J, Coifman B, Bickel P. Day-to-day travel-time trends and travel-time prediction from loop-detector data[J]. Transportation Research Record, 2000(1717):120-129. [2] Zwahlen H T, Russ A, Oner E, et al. Evaluation of microwave radar trailers for nonintrusive traffic measurements[J]. Transportation Research Record, 2005(1917):127-140. [3] Nanthawichit C, Nakatsuji T, Suzuki H. Application of probe-vehicle data for real-time traffic-state estimation and short-term travel-time prediction on a freeway[J]. Transportation Research Record, 2003(1855):49-59. [4] 于渊, 雷利军, 景泽涛, 等. 北斗卫星导航在国内智能交通等领域的应用分析[J]. 工程研究——跨学科视野中的工程, 2014, 6(1):86-91.YU Yuan, LEI Lijun, JING Zetao, et al. Brief probe into the status and trend of compass navigation's application in domestic intelligent transportation systems[J]. Journal of Engineering Studies, 2014, 6(1):86-91.(in Chinese) [5] Ahas R, Aasa A, Silm S, et al. Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area:Case study with mobile positioning data[J]. Transportation Research Part C, 2010, 18(1):45-54. [6] Bartin B, Ozbay K. A clustering based methodology for determining the optimal roadway configuration of detectors for travel time estimation[C]//Proceedings of the 2007 IEEE Intelligent Vehicles Symposium. Istanbul, Turkey:Institute of Electrical and Electronics Engineers, 2007:285-289. [7] 张墨逸, 曹洁, 牛建强, 等. 基于图论与矩阵论的交通检测器布设新方法[J]. 公路交通科技, 2012, 29(11):130-134.ZHANG Moyi, CAO Jie, NIU Jianqiang, et al. A new layout of traffic detectors based on graph theory and matrix theory[J]. Journal of Highway and Transportation Research and Development, 2012, 29(11):130-134.(in Chinese) [8] LIU Yang, ZHU Ning. A multi-objective detector location optimization approach[C]//Proceedings of the 14th COTA International Conference of Transportation Professionals. Changsha:American Society of Civil Engineers, 2014:1788-1800. [9] HAO Guang, XIANG Hongyan, ZHANG Guolin. Traffic detector optimal location model and algorithm for dynamic OD estimation[C]//Proceedings of the 2nd International Conference on Transportation Engineering. Chengdu:American Society of Civil Engineers, 2009:2431-2436. [10] 邵敏华, 孙立军, 邵显智. 基于转弯比例的网络检测器布设模型及算法[J]. 吉林大学学报:工学版, 2013, 43(6):1476-1481.SHAO Minhua, SUN Lijun, SHAO Xianzhi. Network location model of sensors and algorithm based on tuning ratios[J]. Journal of Jilin University:Engineering and Technology Edition, 2013, 43(6):1476-1481.(in Chinese) [11] Bertini R L, Lovell D J. Impacts of sensor spacing on accurate freeway travel time estimation for traveler information[J]. Journal of Intelligent Transportation Systems, 2009, 13(2):97-110. [12] Barceló J, Gilliéron F, Linares M P, et al. Exploring link covering and node covering formulations of detection layout problem[J]. Transportation Research Record, 2012(2308):17-26. [13] ZHANG Jisheng, JIN Maojing. Optimal sensor density for freeway travel time estimation with multi-class traffic flow[C]//Proceedings of Second International Conference on Transportation Information and Safety. Wuhan:American Society of Civil Engineers, 2013:904-913. [14] LI Haijian, DONG Honghui, JIA Limin, et al. A new measure for evaluating spatially related properties of traffic information credibility[J]. Journal of Central South University, 2014, 21(6):2511-2519. [15] 陆化普, 孙智源, 屈闻聪. 大数据及其在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.LU Huapu, SUN Zhiyuan, QU Wencong. Big data and its application in urban intelligent transportation system[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5):45-52. (in Chinese) [16] YANG Hai, ZHOU Jing. Optimal traffic counting locations for origin-destination matrix estimation[J]. Transportation Research Part B, 1998, 32(2):109-126. [17] YANG Hai, YANG Chao, GAN Liping. Models and algorithms for the screen line-based traffic counting location problems[J] Computers and Operations Research, 2006, 33(3):836-858. [18] 朱宁. 交通网络检测器布设优化问题研究[D]. 天津:天津大学, 2012.ZHU Ning. Detector Location Optimization on Transportation Network[D]. Tianjin:Tianjin University, 2012.(in Chinese) [19] 谭昌柏, 袁军, 周来水. 基于宽容分层序列法的飞机装配公差稳健设计技术[J]. 中国机械工程, 2012, 23(24):2962-2967.TAN Changbai, YUAN Jun, ZHOU Laishui. Robust tolerancing for aircraft assembly based on tolerant lexicographic method[J]. China Mechanical Engineering, 2012, 23(24):2962-2967.(in Chinese) [20] 邢文训, 谢金星. 现代优化计算方法[M]. 第2版. 北京:清华大学出版社, 2006.XING Wenxun, XIE Jinxing. Modern Optimization Algorithms[M]. 2 ED. Beijing:Tsinghua University Press, 2006.(in Chinese) [21] Nguyen S, Dupuis C. An efficient method for computing traffic equilibria in networks with asymmetric transportation costs[J]. Transportation Science, 1984, 18(2):185-202. [22] 陆化普. 交通规划理论与方法[M]. 第2版. 北京:清华大学出版社, 2006.LU Huapu. Theory and Method in Transportation Planning[M]. 2 ED. Beijing:Tsinghua University Press, 2006.(in Chinese) |
相关文章:
|