基于单根影响矩阵的钢管混凝土拱桥索力调整方法

文章以一座净跨为220m的钢管混凝土桁架式中承拱桥施工控制项目为依托,针对同一对吊杆上、下游索力偏差较大的情况,在常规影响矩阵的基础上,提出改进的单根索力影响矩阵方法,并结合数值仿真理论和迭代算法,提出确定混凝土桁架式中承拱桥吊杆成桥索力调整方案的方法。该方法利用有限元数值模型计算单根吊杆张拉的影响矩阵,通过迭代算法,求解吊杆索力调整值;同时对依托工程的吊杆张拉索力调整方案进行设计,通过有限次的迭代运算,可使最终的计算成桥吊杆索力与目标索力的偏差达到5%以下,并通过实测成桥索力与设计值对比验证了该方法的可行性。结果表明,实测成桥索力与目标成桥索力误差满足工程要求。     

基于固定站激光扫描数据的公路隧道拱顶多期形变监测方法

激光点云技术具有高精度、高密度和适应性良好的特点，将其应用于公路隧道形变监测中，研究基于固定站激光扫描数据的公路隧道断面连续提取、滤波和多期形变监测的方法。为保证多期形变的观测断面在同一位置，在隧道中安装位置固定的扫描仪，利用三维中轴线和配准算法保证多期断面的同一性。提出一种两次迭代的椭圆曲线拟合方法，该方法第一次迭代基于椭圆形状约束对隧道拱顶断面的非拱顶断面点云进行滤除，第二次迭代利用迭代最小二乘拟合算法对滤波后的拱顶断面进行椭圆曲线拟合。基于拟合的椭圆曲线和极坐标法监测断面的多期形变。与传统RANSAC方法进行对比，两次迭代的椭圆曲线拟合方法在提取隧道椭圆曲线方面表现出更好的滤波效果和更高的精度。搭建隧道模型进行形变模拟和监测试验，对提取的多期数据进行分析，结果表明所提出的方法在公路隧道多期形变监测中具有可行性，且中误差小于2 mm，具有较高的精度。     

基于轨迹数据的无信号交叉口行人过街行为研究

随着我国城市道路运行效率的降低和交通安全形势的日益严峻,行人交通安全已成为普遍关注的问题。本文从无信号道路交叉口的实测轨迹数据中提取相关参数,建立基于随机森林算法的行人过街决策模型并进行效果检验和验证,模型正确率可达93.1%。同时挖掘出各个解释变量对过街决策的重要度,证明将随机森林算法用于行人过街决策分析具有较好的拟合度与预测精度。本研究可应用于人车交互仿真工具中模拟行人行为,为进一步研究人车混行的无信号道路交叉口仿真和控制系统奠定了基础。     

韦伯斯特算法在不同流量下的排队延误优化效果

韦伯斯特算法是一种优化信号交叉口配时的算法,该算法可以降低交叉口的车辆延误,从而达到缓解交通压力和交通拥堵的目的。若某交叉口车流量较低,利用韦伯斯特算法进行信号配时优化后,该交叉口排队延误改变不大,考虑行政成本,应用价值不高。因此,基于VISSIM仿真软件,以拉萨市江苏路与藏大西路交叉口为例,在该交叉口原信号配时的基础上,利用韦伯斯特算法优化信号配时,通过对比,研究在不同交通流量的情况下,韦伯斯特算法降低排队延误的效果。     

施工沥青路面红外热图像拼接方法

通过结合BFGS拟牛顿优化算法和变尺度拼接算法提出了一种拼接方法,实现了沥青路面红外热图像的拼接。最后通过仿真实验验证了提出方法的可行性。     

基于遗传算法的BP神经网络短时交通流预测

对BP神经网络模型、方法及遗传算法的基本原理进行了分析,将遗传算法与标准BP神经网络算法相结合构建了基于遗传算法的神经网络算法;建立了基于BP神经网络的短时交通流预测模型,对BP神经网络算法及基于遗传算法的BP神经网络算法进行设计,并将其应用于短时交通流预测模型的仿真计算,仿真结果表明基于遗传算法的BP神经网络算法具有更快的计算速度及更好的适应能力,该方法可以较好地应用于短时交通流预测。     

基于改进Dijkstra算法的路径优化仿真研究

文章针对传统Dijkstra算法在路径优化过程中存在的不足,从路网拓扑关系存储和快速搜索技术两方面对其进行了改进,得到了一种改进的Dijkstra算法,并通过实例仿真分析,验证了该算法的有效性。     

基于蚁群算法和群决策理论的动态路径优化研究

文章针对城市交通信号控制中的动态路径优化问题,综合考虑相邻交叉口间距和路段交通饱和度两个参数的影响,提出了一种基于蚁群算法和群决策理论的动态路径优化算法模型,并通过仿真实验,对比分析了该算法模型的有效性。     

多点定位算法研究

对多点定位的Chan和Taylor算法,进行了深入的分析,把到达时间差定位技术应用于多算法协同定位,从而在理论上得到了较优解。通过仿真验证,可以提高对目标定位估计的准确性。     

基于多种群遗传BP的城市物流需求预测

城市物流需求预测是城市物流规划的重要前提,为了提高物流需求预测的精度,本文首先通过分析影响城市物流需求的经济活动,选取城市物流需求预测的指标。随后,为了改善BP算法预测效果受到网络初始权值与阈值影响的缺陷,提出多种群遗传BP算法进行改进。通过仿真表明,该方法与BP预测模型以及单种群遗传BP预测模型相比,具有较高的预测精度。     

非均质边坡动力稳定性分析

文章根据土质边坡安全系数计算原理和可靠度JC迭代计算方法,通过建立弹塑性本构模型,对某非均质边坡的稳定性进行了数值模拟分析。结果表明,该边坡处于稳定状态,该数值分析精度满足规范要求。     

Modelling lane changing and merging in microscopic traffic simulation

This paper introduces Simulation of Intelligent TRAnsport Systems (SITRAS), a massive multi-agent simulation system in which driver-vehicle objects are modelled as autonomous agents. The simulation outputs can be used for the evaluation of Intelligent Transport Systems applications such as congestion and incident management, public transport priority and dynamic route guidance. The model concepts and specifications, and the first applications of the model in the area of incident modelling in urban arterial networks were described in previous publications. This paper presents the details of the lane changing and merging algorithms developed for the SITRAS model. These models incorporate procedures for ‘forced’ and ‘co-operative’ lane changing which are essential for lane changing under congested (and incident-affected) traffic conditions. The paper describes the algorithms and presents simulation examples to demonstrate the effects of the implemented models. The results indicate that only the forced and cooperative lane changing models can produce realistic flow-speed relationships during congested conditions.     

An eigenmodel for iterative line planning,timetabling and vehicle scheduling in public transportation

Planning a public transportation system is a multi-objective problem which includes among others line planning, timetabling, and vehicle scheduling. For each of these planning stages, models are known and advanced solution techniques exist. Some of the models focus on costs, others on passengers’ convenience. Setting up a transportation system is usually done by optimizing each of these stages sequentially.In this paper we argue that instead of optimizing each single step further and further it would be more beneficial to consider the whole process in an integrated way. To this end, we develop and discuss a generic, bi-objective model for integrating line planning, timetabling, and vehicle scheduling. We furthermore propose an eigenmodel which we apply for these three planning stages and show how it can be used for the design of iterative algorithms as heuristics for the integrated problem. The convergence of the resulting iterative approaches is analyzed from a theoretical point of view. Moreover, we propose an agenda for further research in this field.     

Analysis of subway station capacity with the use of queueing theory

A new concept of subway station capacity (SSC) is defined according to the gathering and scattering process. A queuing network analytical model of station is created for calculating SSC, which is built by M/G/C/C state dependent queuing network and discrete time Markov chain (DTMC). Based on the definition and the analytical queuing network, a SSC optimization model is developed, whose objective function is to optimize SSC with a satisfactory rate of remaining passengers. Besides, a solution to the model is proposed integrating response surface methodology with iterative generalized expansion method (IGEM) and DTMC. A case study of Beijing Station in Beijing subway line 2 is implemented to verify the validity and practicability of the proposed methods by comparison with simulation model in different experiments. Finally, some sensitivity analysis results are provided to identify the nodes that have the greatest impact on SSC.     

Estimation of a time‐dependent origin‐destination matrix for congested highway networks

This paper presents a time‐dependent origin‐destination (O‐D) matrix estimation procedure embedded with a dynamic traffic assignment model, in which the predictive dynamic user optimal conditions in congested networks are maintained. Two solution algorithms are proposed, namely: an iterative (ITR) scheme and a method of successive averages (MSA) scheme. It is found that the MSA scheme outperforms the ITR scheme. As a prior O‐D matrix is an important input for the problem, its quality is essential for the reliability of the matrix estimation procedure. Empirical constraints are set in relation to the quality of the prior O‐D matrix for the estimation procedure. Numerical examples are used to demonstrate the effectiveness of the proposed methodology.     

Inter-city bus routing and timetable setting under stochastic demands

Vehicle fleet routing and timetable setting are essential to the enhancement of an inter-city bus carrier’s operating cost, profit, level of service and competitiveness in the market. In past research the average passenger demand has usually served as input in the production of the final fleet routes and timetables, meaning that stochastic disturbances arising from variations in daily passenger demand in actual operations are neglected. To incorporate the stochastic disturbances of daily passenger demands that occur in actual operations, in this research, we established a stochastic-demand scheduling model. We applied a simulation technique, coupled with link-based and path-based routing strategies, to develop two heuristic algorithms to solve the model. To evaluate the performance of the proposed model and the two solution algorithms, we developed an evaluation method. The test results, regarding a major Taiwan inter-city bus operation, were good, showing that the model and the solution algorithms could be useful in practice.     

Direct formulation and algorithms for the probit-based stochastic user equilibrium traffic assignment problem

This paper proposes simple and direct formulation and algorithms for the probit-based stochastic user equilibrium traffic assignment problem. It is only necessary to account for random variables independent of link flows by performing a simple transformation of the perceived link travel time with a normal distribution. At every iteration of a Monte-Carlo simulation procedure, the values of the random variables are sampled based on their probability distributions, and then a regular deterministic user equilibrium assignment is carried out to produce link flows. The link flows produced at each iteration of the Monte-Carlo simulation are averaged to yield the final flow pattern. Two test networks demonstrate that the proposed algorithms and the traditional algorithm (the Method of Successive Averages) produce similar results and that the proposed algorithms can be extended to the computation of the case in which the random error term depends on measured travel time.     

基于视频技术的交通场景背景生成方法研究

文章通过对交通视频场景特点的分析,提出了基于视频技术的交通场景背景生成及其更新方法——背景相异像素迭代法。并通过试验及与其他多种算法进行比较,证明了该算法背景生成和更新的可行性和先进性,解决了在交通流密度比较大的情况下背景难于生成和更新的问题。     

Time-dependent fuzzy random location-scheduling programming for hazardous materials transportation

The tremendous use of hazardous materials has promoted the economic development, which also brings about a growing risk causing a widespread concern. In this work, we consider a location-scheduling problem on hazardous materials transportation under the assumption that transportation risks are time-dependent fuzzy random variables. First, we formulate a scheduling optimization model and design a fuzzy random simulation based genetic algorithm to optimize the departure time and dwell times for each depot–customer pair. Then we establish an expected value model and design a modified particle swarm optimization algorithm to minimize the en route risks and site risks. Finally, numerical examples are given to illustrate the effectiveness of the proposed models and algorithms.     

A GA-based household scheduler

One way of making activity-based travel analysis operational for transport planning is multi-agent micro-simulation. Modelling activity and trip generation based on individual and social characteristics are central steps in this method. The model presented here generates complete daily activity schedules based on the structure of a household and its members’ activity calendars. The model assumes that the household is another basic decision-making unit for travel demand aside from individual mobility needs. Results of the model are schedules containing complete information about activity type and sequence, locations, and means of transportation, as well as activity start times and durations. The generated schedules are the outcome of a probabilistic optimisation using genetic algorithms. This iterative method improves solutions found in a random search according to the specification of a fitness criterion, which equals utility here. It contains behavioural assumptions about individuals as well as the household level. Individual utility is derived from the number of activities and their respective durations. It is reduced by costs of travelling and penalties for late, respectively early arrival. The household level is represented directly by the utility of joint activities, and indirectly by allocation of activities and means of transportation to household members. The paper presents initial tests with a three-person household, detailing resulting schedules, and discussing run-time experiences. A sensitivity analysis of the joint utility parameter impact is also included.