基于累积流量比里程分布的区域交通状态评价
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摘要
准确地交通状态研判结果是有效地进行交通拥挤控制的前提和依据。以成都市金牛区二环路、人民北路二段、一环路、沙湾路围合成的局域路网为例,引入宏观基本图理论,建立了不同交通状态下路段累积流量比的里程分布模型,设计了基于路段累积流量比里程分布的区域交通状态指数(RTSI)。首先,利用宏观基本图理论,将区域交通状态划分为畅通、基本畅通、轻微拥挤、中度拥挤和严重拥挤5个等级并对各等级对应的累积流量比阈值区间进行了划分。然后,利用交通仿真数据,对城市区域路网上累积流量比的里程概率分布分别进行了统计及4种常用分布拟合,根据拟合结果建立了区域路网累积流量比累积里程分布模型。最后,通过关联宏观交通流状态与路段累积流量比累积里程分布,借鉴交通工程中最高限速的取值依据,构造了基于累积流量比里程分布的区域交通状态指数评价模型。实例分析表明:RTSI与北京交通发展研究中心提出的TPI契合程度达85%以上,充分证明了RTSI的有效性;同时,TRSI集成了网络中所有路段的累积流量比,其变化过程是连续的,避免了TPI指数的骤增和骤减的情况,因此其变化趋势对交通拥挤发生、发展、蔓延及消散过程的描述更贴近于实际,具有更好的适用性。
An accurate evaluation of traffic status is the precondition and basis for effective congestion control. Taking a regional road network formed by 2 nd Ring Road, 2 nd Section of North Renmin Road, 1 st Ring Road and Shawan Road in Jinniu District of Chengdu for example, by introducing the theory of macroscopic fundamental diagram(MFD), the VKT distribution model of accumulating volume ratio under different traffic conditions is established, and the regional traffic status index(RTSI) based on VKT distribution of AVR is developed. First, the regional traffic status is divided into levels of free-flow, near-free-flow, slight congestion, moderate congestion and severe congestion based on the theory of MFD,and the corresponding AVR threshold intervals of different traffic statuses are divided as well. Then, based on the traffic simulation data, the VKT probability distributions of AVR of regional road network are accounted and 4 common distributions are fitted respectively, and the accumulative VKT distribution model of AVR of regional road network is established accordingly. At last, by associating the macroscopic traffic status with the accumulative VKT distribution of AVR of the road sections and referring the gist of maximum speed limitation in traffic engineering, the RTSI evaluation model based on VKT distribution of AVR is developed. The example analysis shows that(1) the fitting degree of RTSI and TPI proposed by Beijing Transportation Development Research Center is more than 85%, which proved the effectiveness of RTSI;(2) as the RTSI integrated the AVRs of all the sections, its changing process is continuous, which avoided the sudden increase and decrease of TPI, so its variation trend is more realistic for describing traffic congestion occurrence, evolution, spreading and evacuation process with better applicability.
An accurate evaluation of traffic status is the precondition and basis for effective congestion control. Taking a regional road network formed by 2 nd Ring Road, 2 nd Section of North Renmin Road, 1 st Ring Road and Shawan Road in Jinniu District of Chengdu for example, by introducing the theory of macroscopic fundamental diagram(MFD), the VKT distribution model of accumulating volume ratio under different traffic conditions is established, and the regional traffic status index(RTSI) based on VKT distribution of AVR is developed. First, the regional traffic status is divided into levels of free-flow, near-free-flow, slight congestion, moderate congestion and severe congestion based on the theory of MFD,and the corresponding AVR threshold intervals of different traffic statuses are divided as well. Then, based on the traffic simulation data, the VKT probability distributions of AVR of regional road network are accounted and 4 common distributions are fitted respectively, and the accumulative VKT distribution model of AVR of regional road network is established accordingly. At last, by associating the macroscopic traffic status with the accumulative VKT distribution of AVR of the road sections and referring the gist of maximum speed limitation in traffic engineering, the RTSI evaluation model based on VKT distribution of AVR is developed. The example analysis shows that(1) the fitting degree of RTSI and TPI proposed by Beijing Transportation Development Research Center is more than 85%, which proved the effectiveness of RTSI;(2) as the RTSI integrated the AVRs of all the sections, its changing process is continuous, which avoided the sudden increase and decrease of TPI, so its variation trend is more realistic for describing traffic congestion occurrence, evolution, spreading and evacuation process with better applicability.
引文
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[3]刘澜,马亚锋.基于出入流率的交通拥挤量化研究[J].公路交通科技,2013,30(3):111-117,124.LIU Lan,MA Ya-feng.Traffic Congestion Measurement Based on Inflow and Outflow Rates[J].Journal of Highway and Transportation Research and Development,2013,30(3):111-117,124.
[4]SCHRANK D L,LOMAX T J.2009 Urban Mobility Report,DTRT06-G-0044[R].Austin:Texas A&MTransportation Institute,2009.
[5]关伟,何蜀燕,马继辉.HMI:城市快速路发生交通拥堵的一个预警指标[J].交通运输系统工程与信息,2008(5):26-31.GUAN Wei,HE Shu-yan,MA Ji-hui.HMI:Prewarning Index of Traffic Jam Formation on Urban Freeway[J].Journal of Transportation Systems Engineering and Information Technology,2008(5):26-31.
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[7]郑建湖,文子娟,黄明芳,等.交通拥挤状态模糊识别方法的设计与应用[J].华东交通大学学报,2009(1):29-33.ZHENG Jian-hu,WEN Zi-juan,HUANG Ming-fang,et al.Application and Design of Traffic Congestion Identification Method Using Fuzzy Logic[J].Journal of East China Jiaotong University,2009(1):29-33.
[8]HERMAN R,PRIGOGINE I.A Two-fluid Approach to Town Traffic[J].Science,1979,204(4389):148-151.
[9]王殿海,陈松,魏强,等.基于二流理论的路网宏观交通状态判断模型[J].东南大学学报:自然科学版,2011(5):1098-1103.WANG Dian-hai,CHEN Song,WEI Qiang,et al.Discrimination Model for Macroscopic Traffic Conditions of Urban Networks Using Two-fluid Theory[J].Journal of Southeast University:Natural Science Edition,2011(5):1098-1103.
[10]田亮,刘其鑫.基于二流理论的宏观交通评价模型的建立[J].交通科技与经济,2008(1):83-84.TIAN Liang,LIU Qi-xin.Macroscopic Traffic Evaluation Model Based on the Second Level Theory[J].Technology&Economy in Areas of Communications,2008(1):83-84.
[11]GEROLIMINIS N,DAGANZO C F.Existence of Urbanscale Macroscopic Fundamental Diagrams:Some Experimental Findings[J].Transportation Research Part B:Methodological,2008,42(9):759-770.
[12]DAGANZO C F,GEROLIMINIS N.An Analytical Approximation for the Macroscopic Fundamental Diagram of Urban Traffic[J].Transportation Research Part B:Methodological,2008,42(9):771-781.
[13]DAGANZO C F,GAYAH V V,GONZALES E J.Macroscopic Relations of Urban Traffic Variables:Bifurcations,Multi-valuedness and Instability[J].Transportation Research Part B:Methodological,2011,45(1):278-288.
[14]DAGANZO C F.On the Macroscopic Stability of Freeway Traffic[J].Transportation Research Part B:Methodological,2011,45(5):782-788.
[15]GAO F.Macroscopic Fundamental Diagrams for Stockholm Using FCD Data[D].Stockholm:Royal Institute of Technology,2011.
[16]BOYAC B,GEROLIMINIS N.Estimation of the Network Capacity for Multimodal Urban Systems[J].ProcediaSocial and Behavioral Sciences,2011,16:803-813.
[17]王福建,韦薇,王殿海,等.基于宏观基本图的城市路网交通状态判别与监控[C]//第七届中国智能交通年会优秀论文集.北京:电子工业出版社,2012:35-40.WANG Fu-jian,WEI Wei,WANG Dian-hai,et al.Urban Road Network Traffic State Identification and Monitoring Based on Macroscopic Fundamental Diagram[C]//Proceedings of the Seventh China Intelligent Transportation Conference.Beijing:Publishing House of Electronics Industry,2012:35-40.
[18]郑淑鉴,杨敬锋.国内外交通拥堵评价指标计算方法研究[J].公路与汽运,2014(1):57-61.ZHENG Shu-jian,YANG Jing-feng.Research on Computational Methods of Traffic Congestion Evaluation Indexes at Home and Abroad[J].Highways&Automotive Applications,2014(1):57-61.
[19]KERNER B S.Three-phase Traffic Theory and Highway Capacity[J].Physica A:Statistical Mechanics and Its Applications,2004,333(1):379-440.