Shockwave-based queue estimation approach for undersaturated and oversaturated signalized intersections using multi-source detection data

With the progress of information and sensing technologies, estimating vehicular queue length at signalized intersections becomes feasible and has attracted considerable attention. The existing studies provided a solid theoretical foundation for the estimation; however, the studies have some restrictions or limitations more or less. This paper presents a new methodology for estimating vehicular queue length at signalized intersections using multi-source detection data under both undersaturated and oversaturated conditions. The methodology applies the shockwave theory to model queue dynamics. Using data from probe vehicles and point detectors, analytical formulations for calculating the maximum and minimum (residual) queue lengths of each cycle are developed. Ground truth data were collected from numerical experiments conducted at two intersections in Shanghai, China, to verify the proposed methodology. It is found that the methodology has mean absolute percentage errors of 17.09% and 12.28%, respectively, for maximum queue length estimation in two tests, which are reasonably effective. However, the methodology is unsatisfactory in estimating the residual queue length. Other limitations of the proposed models and algorithms are also discussed in the paper.     

Classification of Livebus arrivals user behavior

With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behavior of travelers and the dynamics of people flows. This work analyzes the requests for real-time arrivals of bus routes at stops in London made by travelers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for real-time arrivals for each of the 28 days under observation. These data are analyzed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviors. It was found that the classification-based approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMO methodology (Precision of 97%). Furthermore, the behavior within the six classes of users is analyzed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37% of users can be classified as interchange users. This classification could form the basis of a more personalized LiveBus Arrivals application in future, which could support management and planning by revealing how public transport and related services are actually used or update information on commuters.     

Plate scanning tools to obtain travel times in traffic networks

The management of vehicle travel times has been shown to be fundamental to traffic network analysis. To collect travel time measurement, some methods focus solely on isolated links or highway segments, and where two measurement points, at the beginning and at the end of a section, are deemed sufficient to evaluate users' travel time. However, in many cases, transport studies involve networks in which the problem is more complex. This article takes advantage of the plate scanning technique to propose an algorithm that minimizes the required number of registering devices and their location in order to identify vehicles candidates to compute the travel times of a given set of routes (or subroutes). The merits of the proposed method are explained using simple examples and are illustrated by its application to the real network of Ciudad Real.     

CCS水电站引水隧洞双护盾TBM施工围岩分类研究

为了对双护盾TBM隧洞施工中的围岩进行分类,以厄瓜多尔CCS水电站引水隧洞为例,参考RMR围岩分类方法,通过伸缩护盾的间隙和刀盘的空隙对洞壁和掌子面围岩进行观测,结合岩渣及掘进参数,获取岩石的质量指标RQD、节理间距和节理性状等信息。通过综合分析,选取岩石的回弹值、围岩的完整性、岩石的质量指标RQD、刀盘推力、刀盘扭矩、片状岩渣含量和地下水渗流量作为围岩分类的指标,然后对7个指标分别赋值,建立围岩的分类标准,并通过求和的方法进行综合围岩分类。分类结果表明:选取的7个指标可以克服双护盾TBM施工时无法对围岩进行全面地质素描的困难,可以满足TBM快速施工的需要,并且能较真实地反映围岩的情况,分类结果可靠。     

Magneto-rheological suspensions for improving ground vehicle’s ride comfort,stability, and handling

ABSTRACT

A state-of-the-art discussion on the applications of magneto-rheological (MR) suspensions for improving ride comfort, handling, and stability in ground vehicles is discussed for both road and rail applications. A historical perspective on the discovery and engineering development of MR fluids is presented, followed by some of the common methods for modelling their non-Newtonian behaviour. The common modes of the MR fluids are discussed, along with the application of the fluid in valve mode for ground vehicles’ dampers (or shock absorbers). The applications span across nearly all road vehicles, including automobiles, trains, semi-trucks, motorcycles, and even bicycles. For each type of vehicle, the results of some of the past studies is presented briefly, with reference to the originating study. It is discussed that Past experimental and modelling studies have indicated that MR suspensions provide clear advantages for ground vehicles that far surpasses the performance of passive suspension. For rail vehicles, the primary advantage is in terms of increasing the speed at which the onset of hunting occurs, whereas for road vehicles – mainly automobiles – the performance improvements are in terms of a better balance between vehicle ride, handling, and stability. To further elaborate on this point, a single-suspension model is used to develop an index-based approach for studying the compromise that is offered by vehicle suspensions, using the H₂ optimisation approach. Evaluating three indices based on the sprung-mass acceleration, suspension rattlespace, and tyre deflection, it is clearly demonstrated that MR suspensions significantly improve road vehicle’s ride comfort, stability, and handling in comparison with passive suspensions. For rail vehicles, the simulation results indicate that using MR suspensions with an on-off switching control can increase the speed at which the on-set of hunting occurs by as much as 50% to more than 300%.     

基于小波和粒子群算法的HEV行驶状况辨识方法研究

针对混合动力汽车(HEV)行驶状况(道路坡度和整车载荷)变化难以有效识别,导致驱动系统控制策略不能有效满足驾驶员意图问题,以混联式HEV为研究对象,提出了基于小波滤波和粒子群算法的HEV行驶状况辨识方法。首先建立了汽车行驶状况辨识模型,采用最小二乘法确立了优化目标函数,其次研究了基于小波滤波和粒子群算法的HEV行驶状况辨识原理,最后进行了行驶状况粒子群智能算法辨识试验。在采集实车数据的基础上,对实车数据进行小波滤波,并运用行驶状况辨识方法对道路坡度和整车载荷进行了辨识,并对辨识结果进行小波滤波,结果表明,试验工况下整车载荷辨识的相对误差绝对平均值为2.71%,道路坡度辨识的相对误差绝对平均值为3.85%,验证了所提出方法的有效性。     

基于瞬时阻抗的路径规划策略仿真性能评估

根据对实时路况的数据处理方式,常见的动态路径规划策略可分为瞬时阻抗T-SPF和平均阻抗A-SPF两种方式.如何验证这些策略的个体效益及其对城市路网的中长期影响一直是相关系统部署的核心问题.针对这一问题,通过Vissim的Car2X模块与自定义程序实现了仿真中的动态转向,并以现实路网为蓝本,对T-SPF和A-SPF在不同程度的市场占有率情况下的运行性能进行了测试.测试结果表明2种现有策略都存在各自的问题;当市场占有率达到15%以上时,T-SPF 策略可能令路网的通行容量处于振荡状态,而实施了 A-SPF 的路网的平均延误和通行量表现稳定,但在个体效益方面表现还不如静态策略.建议为城市路网实施路径规划时设计新的策略以及与之匹配的信号优化机制.     

高速公路车辆典型交通违法行为危险度分级预警方法

高速公路不文明驾驶行为及交通违法行为的频发严重影响了高速公路交通流运行的安全与效率.基于采集的高速公路交通安全相关数据,对高速公路典型交通违法行为的危险度的分级预警方法进行了研究.针对高速公路的交通事故数据,以从恶劣天气、道路线形、违法类型的角度分析了道路交通事故与各类因素的相关性,在此基础上确定进行分级预警的影响因素.综合考虑在气象条件、交通状况、道路线形及各类违法行为,提出了层次分析与模糊评价相结合的方法,建立了针对高速公路违法行为危险性的分级预警模型,并用实际案例数据对模型的适用性进行了验证.结果显示,模型具有良好的适用性,所建立的分级预警模型可以为高速公路的主动交通预警及管理提供参考与支撑.     

基于Kinect深度虚拟线圈的夜间车流量检测

针对夜间环境下基于摄像机的车辆检测方法存在精度低、稳定性差以及无法对车型进行有效识别等问题,提出一种基于Kinect深度虚拟线圈的夜间车辆检测与计数算法.首先对Kinect深度图像进行预处理,分别获得运动目标深度图(MDM)与空洞深度图(HDM).然后在MDM与HDM上设置虚拟线圈,利用积分图像分别生成对应的一维运动信号,对其进行加权合成获得对车辆运动特征的表达,并在合成的运动信号范围内检测出车辆目标,并计算出车辆目标的几何特征,通过SVM对车型进行有效识别.实验结果表明,该算法对于单双车道的车辆计数正确率分别高达99.75%与99.25%,大小车型分类正确率可达99.80%,处理单张图片的平均时间仅为7 ms.     

郑万高铁隧道大断面机械化施工关键技术研究

为实现大断面隧道机械化施工,针对郑万高速铁路湖北段隧道机械化施工所面临的掌子面稳定性控制、支护结构参数优化、机械化施工工法及施工工艺参数选取等主要问题,采取现场试验、资料调研、理论分析等手段,提出掌子面稳定性综合分级方法及稳定性控制措施,并对超前支护、洞身支护参数进行优化。郑万高速铁路采用全断面法和微台阶法2种工法进行施工,同时,对高压劈裂注浆、低预应力锚杆、超挖控制、高标号初喷混凝土等施工工艺进行探索和应用; 在信息化项目管理平台的基础上,建立基于施工动态管理的隧道作业面分类管理方法。研究成果在郑万高速铁路的成功应用将极大提高隧道建设的机械化水平,为隧道机械化施工提供参考。