基于当期事件识别的拥堵传播特征研究  

Spatial Propagating Study of Urban Traffic Congestion Based on Current Event Recognition

作　　者：韦伟[1] 毛保华[1] 陈绍宽[1] 许得杰[1] 

机构地区：[1]北京交通大学城市交通运输复杂系统理论与技术教育部重点实验室,北京100044

出　　处：《交通运输系统工程与信息》2016年第4期165-170,共6页Journal of Transportation Systems Engineering and Information Technology

基　　金：国家自然科学基金重点项目(71390332);国家基础研究计划项目(2012CB725406)~~

摘　　要：城市突发性交通拥堵的空间传播规律,是制定有针对性的交通管理、控制和诱导措施的重要依据.本研究以实测的道路交通流数据为基础,在对交通流当期事件进行识别和分析的基础上,构建当期事件SDM模型,并据此提出一种交通流当期事件拥堵空间传播分析方法.通过北京市的案例研究发现,在去除长期趋势后,交通流的当期事件SDM模型能够更准确地描述事件发生情况下交通拥堵的传播结构.案例研究表明,当路网密度为22辆/km(14:00左右),局部路段交通事件严重程度加剧所传播的空间影响达到最大.因此,在制定拥堵缓解措施时,应对路网临界密度状态进行重点监控和疏导.Research on spatial propagation rules of sudden congestion is a significant foundation of traffic management, control and route guidance. In this paper, traffic data in field is used as study object. After the recognition of long term trend and current event, an improved SDM（Spatial Durbin Model） of current event is proposed, on the base of which a traffic congestion propagation analysis method is also derived.Effectiveness verification of the proposed method is embodied in the case studies of the road network of Beijing. The case studies imply that, after removing the influence of long term trend, the proposed SDM of current event can reflect the space propagation structure of traffic event more accurately. While the spatial influence of traffic event reach the maximum in road networks with the average density of 22 pcu/km（14：00）.Therefore, closer supervision and control against the key nodes and critical-density status of road network is needed.

关 键 词：智能交通 当期事件 SDM模型 交通拥堵 传播特性 

分 类 号：U491.2[交通运输工程—交通运输规划与管理]