A generic class of first order node models for dynamic macroscopic simulation of traffic flows

Node models for macroscopic simulation have attracted relatively little attention in the literature. Nevertheless, in dynamic network loading (DNL) models for congested road networks, node models are as important as the extensively studied link models. This paper provides an overview of macroscopic node models found in the literature, explaining both their contributions and shortcomings. A formulation defining a generic class of first order macroscopic node models is presented, satisfying a list of requirements necessary to produce node models with realistic, consistent results. Defining a specific node model instance of this class requires the specification of a supply constraint interaction rule and (optionally) node supply constraints. Following this theoretical discussion, specific macroscopic node model instances for unsignalized and signalized intersections are proposed. These models apply an oriented capacity proportional distribution of the available supply over the incoming links of a node. A computationally efficient algorithm to solve the node models exactly is included.     

Clockwise hysteresis loops in the Macroscopic Fundamental Diagram: An effect of network instability

A recent study reported that the Macroscopic Fundamental Diagram of a medium size city exhibited a clockwise hysteresis loop on a day in which a major disturbance caused many drivers to use unfamiliar routes. It is shown below that, even in a perfectly symmetric network with uniform demand, clockwise loops are to be expected when there are disturbances, especially if the disturbances cause a significant fraction of the drivers to not change routes adaptively. It is also shown that when drivers are not adaptive networks are inherently more unstable as they recover from congestion than as they are loaded. In other words, during recovery congestion tends more strongly toward unevenness because very congested areas clear more slowly than less congested areas. Since it is known that uneven congestion distributions reduce network flows, it follows that lower network flows should arise during recovery, resulting in clockwise loops. Fortunately, the presence of a sufficient number of drivers that choose routes adaptively to avoid congested areas helps to even out congestion during recovery, increasing flow. Thus, clockwise loops are less likely to occur when driver adaptivity is high.     

Real time queue length estimation for signalized intersections using travel times from mobile sensors

We study how to estimate real time queue lengths at signalized intersections using intersection travel times collected from mobile traffic sensors. The estimation is based on the observation that critical pattern changes of intersection travel times or delays, such as the discontinuities (i.e., sudden and dramatic increases in travel times) and non-smoothness (i.e., changes of slopes of travel times), indicate signal timing or queue length changes. By detecting these critical points in intersection travel times or delays, the real time queue length can be re-constructed. We first introduce the concept of Queue Rear No-delay Arrival Time which is related to the non-smoothness of queuing delay patterns and queue length changes. We then show how measured intersection travel times from mobile sensors can be processed to generate sample vehicle queuing delays. Under the uniform arrival assumption, the queuing delays reduce linearly within a cycle. The delay pattern can be estimated by a linear fitting method using sample queuing delays. Queue Rear No-delay Arrival Time can then be obtained from the delay pattern, and be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can also be constructed. The model and algorithm are tested in a field experiment and in simulation.     

基于多分辨率分析的区间类型2模糊系统

为了更有效地表达和处理系统的不确定性,提出了基于多分辨率分析的区间类型2(简称多分辨率区间类型2)模糊系统,构造了多分辨率区间类型2隶属度函数,并给出了基于尺度函数分解的结构和参数学习算法.多分辨率区间类型2模糊系统的前件是多分辨率隶属度函数,后件是输入的线性函数,因此系统可以自适应地划分输入论域空间,优化系统结构.将多分辨率区间类型2模糊系统应用于带有噪声的时间序列预测,预测结果的复杂度较小,均方根误差为0.105 2.     

小城镇道路交通安全管理规划研究

通过分析我国小城城镇特点及其交通安全面临的问题,并结合交通安全规划的理论,提出小城镇道路交通安全规划的具体原则和内容,具有一定的理论和实际意义。     

公路平面交叉口交通组织设计

交叉口是道路系统的重要组成部分,既是道路交通的咽喉,但也是"瓶颈"。存在行车速度低、事故率高、交通拥挤甚至交通堵塞等弊病。针对交叉口交通事故的严重性,提出进行交通组织设计的必要性。主要介绍了3种交通组织方法:设置专用车道、左转弯车辆的交通组织、组织渠化交通。并结合具体设计实例对交通组织设计进行进一步的分析和阐明。     

事故树分析法在城市轨道交通事故分析中的应用

从系统工程学的角度出发,将广泛应用于系统安全研究中的事故树分析方法应用到城市轨道交通事故分析中。分析城市轨道交通运营事故中脱轨这一典型事故。通过分析引起列车脱轨事故各个因素,得出城市轨道交通脱轨事故的发生概率较小,预防和控制难度较大的结论。该研究有助于城市轨道交通运营公司对引起该事故发生的各个因素提高警惕意识,也为运营安全管理工作提供理论依据。     

城市交通拥堵分析及对策研究

从宏观、中观、微观层次分析导致城市拥堵的原因,倡导可持续的城市交通发展政策,以交通管理、交通供给、交通需求为出发点,提出深化交通体制改革与加强智能信息管理,增加道路有效供给与提升停车管理,构建快速公交系统与加强汽车需求管理,并结合土地利用、城市规划与交通建设协调发展提出建议。     

两种短时交通流混沌预测方法分析

短时交通流预测是智能交通系统的核心内容和交通信息服务、交通诱导的重要基础。采用符合交通流特性的混沌理论对短期交通流进行预测。在相空间重构和混沌识别的基础上,建立短期交通流加权一阶局域预测模型和基于最大Lyapunov指数的预测模型,并对一组实际的交通流数据进行预测。仿真结果表明:两种方法都能较准确的预测交通流,但最大Lyapunov指数预测模型的预测精度相对较高。