城市交通拥堵态势监控的时空分布形态识别模型

交通运输系统工程与信息 ›› 2012, Vol. 12 ›› Issue (3): 41-45.

• 智能交通系统与信息技术 • 上一篇下一篇

城市交通拥堵态势监控的时空分布形态识别模型

胡启洲^*，刘英舜，郭唐仪

南京理工大学自动化学院，南京 210094

收稿日期:2011-12-23 修回日期:2012-02-06 出版日期:2012-06-24 发布日期:2012-07-03
作者简介:胡启洲（1975-），男，甘肃会宁人，博士，副教授.
基金资助:
国家自然科学基金（51178157，11102180）；南京理工大学自主科研专项计划基金（2011zdjh29）；教育部人文社会科学研究基金资助项目（NO.12YJCZH071）.

Space-Time Distribution Model on State Monitoring of Urban Traffic Congestion

HU Qi-Zhou, LIU Ying-Shun, GUO Tang-Yi

School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2011-12-23 Revised:2012-02-06 Online:2012-06-24 Published:2012-07-03

摘要/Abstract

摘要：

为了及时发现和预警城市交通高峰时期的偶发事件，研究了态势监控的城市交通拥堵动态跟踪问题.对交通拥堵的相关属性、变化规律、空间分布以及判别方式等进行综合研究的基础上，分析了道路交通流的非线性动力学特征，建立了基于时空分布的路网交通拥堵态势监控的动态预警模型，提出了解决城市路网交通拥堵的方法，达到了充分利用交通资源、疏导交通、缓解交通拥堵的目的.该模型在对占有率、速度、流量三个基本交通流参数进行处理获得新的交通拥挤判别指标基础上，通过形态识别模型对拥堵状态进行判定.实例分析表明，该模型为缓解城市交通拥堵问题、提高城市交通管理水平、改善道路交通安全形势等具有重要的理论意义和应用价值.

关键词: 城市交通, 拥堵, 态势监控, 时空模型

Abstract:

Based on characteristics parameters analysis, some indexes of the state monitoring on urban traffic congestions are defined. By establishing the urban traffic network model, this paper designs a quantitative analysis flow of space-time congestion monitoring of road network based on the multi-dimension theory. Then, it uses three space-time parameters to study the traffic jams monitoring model of road traffic network. Aimed at resolving the quick measurements and control of traffic congestions, a real-time decision support model which can timely reflect traffic congestion situations is developed. The results show that this real-time decision support model can take place of traffic engineers’ management works and raise the efficiency of congestion management. To quickly identify the non-recurrent incidents during peak period, an auto-identifying algorithm of urban traffic congestion is designed. An improved multi-dimension monitoring algorithm and interconnected model are proposed to identify the traffic congestion and its diffusion. The application results reveal the validity and maneuverability of the study, and a foundation for developing the decision support system is thus provided for urban traffic congestion management.

Key words: urban traffic, congestion, state monitoring, space-time model

中图分类号:

U491.113

胡启洲, 刘英舜, 郭唐仪. 城市交通拥堵态势监控的时空分布形态识别模型[J]. 交通运输系统工程与信息, 2012, 12(3): 41-45.

HU Qi-Zhou, LIU Ying-Shun, GUO Tang-Yi. Space-Time Distribution Model on State Monitoring of Urban Traffic Congestion[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(3): 41-45.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract18402.shtml

http://www.tseit.org.cn/CN/Y2012/V12/I3/41

参考文献

[1]Henry X Liu, Xinkai Wu, Wenteng Ma, et al. Realtime queue length estimation for congested signalized intersections[J]. Transportation Research Part C, 2009, 17(4):412-427.
[2]Nicolas Chiabaut, Ludovic Leclercq, Christine Buisson. From heterogeneous drivers to macroscopic patterns in congestion[J]. Transportation Research Part B, 2010, 44(2):299-308.
[3]I do Juran, Joseph N Prashker, Shlomo Bekhor, et al. A dynamic traffic assignment model for the assessment of moving bottlenecks[J]. Transportation Research Part C, 2009, 17(3):240-250.
[4]K. Aboudolas, M Papageorgiou, E Kosmatopoulos. Storeandforward based methods for the signal control problem in largescale congested urban road networks[J]. Transportation Research Part C, 2009, 17(2):163-174.
[5]Jarkko Niittymkia,Esko urunenb.Traffic signal control on similarity logic reasoning[J]. Fuzzy Sets and Systems, 2003, 133 (1) : 109-131.
[6]Nikolas Geroliminis, Matthew G Karlaftis, Alexander Skabardonis. A spatial queuing model for the emergency vehicle districting and location problem[J]. Transportation Research Part B, 2009, 43(7):798-811.
[7]Turner S，Lomax T，Levinson H.Measuring and estimating congestion using travel timebased procedures［C］. In Transportation Research Record，No 1564：Journal of Transportation Research Board Series，TRB，National Research Council. Washington，D.C.，1996:11-19.
[8]Inoue H.Traffic equilibrium and its solution in congested road networks[C] . Proceedings of IFAC Control in Transportation Systems , 1986:267-272.
[9]Turner S，Holdener D. Probe vehicle sample sizes for realtime information：The houston experience. Proceedings of the vehicle navigation and information system conference[C]. Seattle：IEEE，1995:3-10.
[10]Yingyan Lou,Yafeng Yin,Siriphong Lawphongpanich.Robust congestion pricing under bounded rational user equilibrium[J]. Transportation Research PartB, 2010, 44(1):15-28.
[11]Peng Zhang, S C Wong, S Q Dai. A conserved higherorder anisotropic traffic flow model: Description of equilibrium and nonequilibrium flows[J]. Transportation Research Part B, 2009, 43(5):562-574.
[12]Michal Blumberg, Hillel BarGera. Consistent node arrival order in dynamic network loading models[J]. Transportation Research Part B, 2009, 43(3):285-300.
[13]Sazi M urat Y, Ergun Gedizlioglu. A fuzzy logic multiphased signal control model for isolated junctions [J]. Transportation Research: P art C, 2005,13 (1) : 19-36.
[14]朱茵,陆化普,刘强. 城市交通预警系统的事件自动检测算法研究[J]. 公路交通科技, 2004, 21(10): 85-88.［ZHU Y, LU H P, LIU Q.Urban traffic warning system automatic incident detection algorithm[J]. Highway Transportation Technology, 2004, 21(10):85-88.］
[15]Bo Chen,Harry H Cheng,Joe Palen.Integrating mobile agent technology with multiagent systems for distributed traffic detection and management systems[J]. Transportation Research Part C, 2009,17(1):1-10.
[16]Iisakk i Ko sonen. Multi agent fuzzy signal control based on realtime simulation[J]. Transportation Research: Part C, 2003, 11 (5) : 389-403.
[17]姚智胜,邵春福.基于状态空间模型的道路交通状态多点时间序列预测[J].中国公路学报,2007, 20(4):113-117.［YAO Z S, SHANG C F. Road traffic state multispot time series forecasting based on state space model[J].China Journal of Highway and Transport,2007, 20(4) :113-117.］
[18]Cheng L , Lida Y Uno N. A stochastic flow dependent model for path flow estimation[J]. Infrastructure Planning Review , 2001 ,18 (3) :573-580.

城市交通拥堵态势监控的时空分布形态识别模型

Space-Time Distribution Model on State Monitoring of Urban Traffic Congestion

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	刘晓冰, 李奉孝, 田欣妹, 闫学东. 基于通勤模式的都市圈中心结构判别研究[J]. 交通运输系统工程与信息, 2022, 22(2): 17-28.
[2]	许奇, 陈越, 黄靖茹, 高顺祥, 张志健. 考虑出行费用的就业可达性分析[J]. 交通运输系统工程与信息, 2022, 22(2): 37-44.
[3]	陈鼎, 周水庭, 陈云, 苏敏咸. 拥堵指数自适应调节的交通运行状态识别方法及应用研究[J]. 交通运输系统工程与信息, 2022, 22(2): 137-144.
[4]	刘春禹, 刘永红, 罗霞, 朱颖. 混合交通流环境下单交叉口自动驾驶车辆轨迹优化[J]. 交通运输系统工程与信息, 2022, 22(2): 154-162.
[5]	张小雨, 邵春福, 王博彬, 黄士琛. 新冠疫情影响下居民共享出行方式选择行为研究[J]. 交通运输系统工程与信息, 2022, 22(2): 186-196.
[6]	李欣, 戴章, 李怀悦, 胡笳. 基于连续近似模型的轨道交通与常规公交耦合优化设计[J]. 交通运输系统工程与信息, 2022, 22(2): 206-213.
[7]	郝妍熙, 刘戎阳, 胡华, 方勇, 刘志钢. 单向通道行人-自行车混合流建模及自组织现象研究[J]. 交通运输系统工程与信息, 2022, 22(2): 223-229.
[8]	赵传林, 贺少松, 孙淑敏, 王钰涵. 基于理性疏忽理论的交通疏散网络双层优化模型[J]. 交通运输系统工程与信息, 2022, 22(2): 239-246.
[9]	陈小红, 蓝秋雨. 不确定交通需求下交叉口信号配时区间优化模型[J]. 交通运输系统工程与信息, 2022, 22(2): 247-256.
[10]	李冰, 王正辉, 马铭炜, 杨鸿宇, 冯悦. 信号交叉口行人二次过街下右转车通行能力与延误估计模型[J]. 交通运输系统工程与信息, 2022, 22(2): 257-267.
[11]	朱彤, 秦丹, 董傲然, 杜雨萌, 魏雯. 公交驾驶员违规类型同交通事故间的关联分析[J]. 交通运输系统工程与信息, 2022, 22(2): 322-329.
[12]	毛保华, 王敏, 何天健, 陈海波. 城市公共交通服务水平研究回顾和展望[J]. 交通运输系统工程与信息, 2022, 22(1): 2-13.
[13]	王子甲, 贾慧慧, 朱亚迪, 陈峰. 基于智能卡数据的轨道与公交复合网络通勤方式选择行为研究[J]. 交通运输系统工程与信息, 2022, 22(1): 67-73.
[14]	贾斌, 朱凌, 李树凯, 刘家林. 城市轨道交通小交路计划与客流控制协同优化策略[J]. 交通运输系统工程与信息, 2022, 22(1): 124-132.
[15]	牟振华, 汪寒冰, 林本江, 陈逸群, 金程程, 陈艳艳. 基于演化博弈的违章停车动态罚金策略效用仿真评价[J]. 交通运输系统工程与信息, 2022, 22(1): 152-162.