路径选择偏好对路网交通流的影响

近年来,城市路网的大规模建设并没有解决交通拥堵问题,而且有些道路资源还未得到很好的利用,从而造成了资源浪费。由于人们出行总是具有一定的偏好,即选择自己习惯的路径出行,因此路网中的交通流分布存在一定的规律,一些路段到达饱和,而另一些路径没有到达饱和状态,甚至交通量很少。因此选取了时间、距离、出行经验和经济因素4个指标作为影响出行者路径偏好的因素,并采用层次分析法确定4个指标的权重,通过构建比较矩阵并进行一致性检验,最后认为:时间因素对出行者的路径选择影响最大,其次是出行距离。在此基础上建立基于累积前景理论的路径选择模型,分析出行者路径选择对路网中交通流的影响,通过算例得出结论:原本距离最短的路径决策权重值与前景值均较低,反之起讫点间距离长的路径前景值最高。但该结论与一般情况下行人选择距离较短路径出行的行为偏好相悖。因此,出行者路径选择偏好是造成交通拥堵的一个重要原因。     

突发事件下城市快速路网分流模型研究

通过对城市快速路网中突发事件路段的研究,建立了事件路段的分流模型,根据事件延误与绕行延误的关系,决定是否诱导出行者使用最短路径进行分流,从而有助于出行者选取最佳分流路径,减少因路网事件而造成的交通延误。     

救援车辆通行畅通可靠度模糊综合评价方法研究

突发事件发生后救援车辆必须能尽快赶赴现场施救,但路网交通流的随机变化特性往往会影响救援车辆的通行效率,引入可靠性理论对路网交通流影响下的救援车辆畅通通行可靠性进行分析。考虑到救援车辆出行高效率要求以及优先通行特性,依据实地调研结果将救援车辆通行畅通程度划分为五个等级,在此基础上提出救援车辆通行畅通可靠度的概念,并依据道路网络拓扑结构将道路网络分为道路单元、路径以及路网三级,依次构造救援车辆通行畅通可靠度的模糊综合评价模型。首次考虑路网交通供需随机变化对救援车辆出行的影响,其研究成果的可操作性和实用性较强,为救援管理部门对救援管理系统进行科学评价和合理规划提供良好的借鉴和参考。     

浅析几种基本路段行程时间预测算法

近年来,短时行程时间预测研究取得了一些成果,研究者在各自的领域内运用不同的方法建立了各种行程预测算法。这些预测算法按照所选参数的多少分为综合模型和基于路段上的时间序列预测模型,综合模型适应性强,但需要的交通参数较多,时间序列预测模型适应性较差,但模型简单。按照预测效果及用途分为实时性预测以及估计行预测,实时性预测要求预测的实时性能高,用来做交通控制、交通流诱导和线路引导。估计性预测其精确度要求不高,实时性能要求也不高,主要用来为出行者提供信息,本文将介绍几种已有的路段行程时间预测算法。     

单变量控制提高交通安全可靠度方法探讨

为探讨现有公路路段交通安全系统可靠度达不到预期要求的问题,利用事故故障树分析方法建立了道路交通安全系统模型,并以其为基础建立了交通安全系统的可靠度分配机制,通过提高少数变量可靠度的故障树方法,达到提高系统总体可靠度,并以道路交通事故发生火灾为例对可靠度进行了分配与计算,结果表明:少数子系统可靠度的提高实现了整个安全系统的可靠度提高,需要提高可靠度的子系统为整个系统中可靠性较低的子系统,在设计上,方法简单、易于操作控制,具有可行性,能够很好的在实际事故安全系统中加以运用。     

出口道短车道对信号控制交叉口通行能力影响

研究出口道短车道在不同交通条件和道路几何条件下对道路交通能力的影响,可以进一步完善交叉口通行能力计算理论体系,对工程设计给出理论依据。介绍了相关研究背景,交叉口出口道短车道对信号控制交叉口通行能力的修正。最后从短车道长度、有效绿灯时间、路段与进口道饱和流量差、交通流基本参数关系模型等因素,分析了出口道短车道对通行能力的影响程度。     

占道施工影响区车流均衡诱导模型的建立与应用

根据占道施工影响区边界节点OD分布,采用虚拟路径简化交叉口流向,构建了节点间路径行程时间动态可靠度函数;并引入多元Logit概率分布模型,结合剩余通行能力敏感度,建立车流均衡诱导模型;采用该模型计算占道施工影响区路网中各路径的诱导交通量,以实现占道施工影响区路网车流的均衡诱导,重新均衡施工影响区的交通流。对重庆市某施工影响区的案例分析表明,采用车流均衡诱导模型可有效缓解占道施工导致的非均衡交通拥堵问题,提高占道施工影响区的交通运行效率。     

考虑路段关联失效的路网行程时间可靠性估计

通过放松不同路段状态完全相互独立这一假设,在分析路段失效影响因素过程中摒弃常用的故障树分析法,建立路段相关、多状态条件下路网行程时间可靠性的计算方法.该方法首先确定网络最有可能发生的状态及其发生概率,计算状态空间对应的路段、路径行程时间可靠度,最后获得路段相关、多状态路网行程时间可靠度指标值.该方法的计算结果与假设路段交通状态互相独立的计算结果作比较,其结果表明,对于路网行程时间可靠度计算,相关性假设比独立性假设更为准确,路段关联失效假设,更能提高行程时间发布可信度.     

道路交通系统的安全可靠度模型构建

采用系统的观点,通过分析组成道路交通系统的四个因素——交通参与者,车辆,道路,环境,分别建立安全可靠度评价模型和道路交通系统安全可靠度模型,从而实现了对道路交通系统安全性进行评价,为今后道路交通安全的研究与保障奠定了一定的理论基础。     

考虑地质成因的土坡可靠度分析

 不同地质成因的边坡在土体参数统计变异性相同的情况下,由于空间变异性不同,其稳定状态并不一定相同,传统的边坡稳定性确定性分析和基于随机变量模型的边坡可靠度分析方法无法考虑坡体的特定地质成因。由地质成因估计土体参数的波动范围,从而通过土体参数波动范围这一指标把地质成因纳入边坡稳定性分析框架,提出考虑地质成因的边坡可靠度分析方法,介绍具体的分析流程,采用一维随机场模拟和验算点法完成边坡可靠度分析。算例计算结果显示岩土参数空间变异性对边坡稳定性的影响很大,由地质成因估计土体参数的波动范围,为在边坡可靠度分析中合理考虑土体参数空间变异性提供一条现实的解决途径,值得在工程应用中推广。     

随机路网的行程时间可靠性

分析了随机路网中行程时间的随机变动 ,并重新定义了行程时间可靠性。将OD交通量和路段通行能力作为离散随机变量 ,基于用户平衡分配模型 ,用近似算法求解行程时间可靠性。用一个简单的例子对算法进行了说明     

Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network

One of the major requirements of advanced traveler information systems (ATISs) is a mechanism to estimate link travel times. This article examines the use of an artificial neural network (ANN) for predicting freeway link travel times for one through five time periods into the future. Actual freeway link travel times from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system were used as a test bed. It was found that when predicting one or two time periods into the future, the ANN model that only considered previous travel times from the target link gave the best results. However, when predicting three to five time periods into the future, the ANN model that employed travel times from upstream and downstream links in addition to the target link gave superior results. The ANN model also gave the best overall results compared with existing link travel time forecasting techniques.     

交通意外事件紧急疏导配流方案生成方法研究

城市交通意外事件易造成交通拥挤、诱发新的交通意外事件,严重影响城市道路网的正常运行,首要之策是分析评估交通意外事件造成的影响范围,实施交通紧急控制与诱导。因此,本文提出交通意外事件紧急疏导配流方案生成方法。该方法由交通意外事件疏导范围确定和多目标交通疏导配流模型两部分组成,通过寻找合理的交通区域进行交通流的二次分配,根据事发前后区域出行总时间的变化寻找合理的疏导范围;在确定的疏导范围内,应用多目标交通疏导配流模型对交通流实施动态分配。该方法在方案生成过程中,兼顾疏导区域、总行驶时间、道路交通流量、道路交通流增量等诸多因素,形成交通意外事件紧急疏导配流方案,保障疏导范围内配流方案的可行性、平稳性和安全性。     

环境因素下城市交通离散网络设计问题中确定最大OD需求量的优化模型及求解算法

在考虑环境因素的情况下将广义备用能力的概念与城市交通离散网络设计问题结合在一起,一方面要求整个交通网络可以容纳的OD需求量与车辆尾气排放总量之差最大;另一方面,通过在交通网络中添加新的路段来提高整个交通网络的OD需求量。文中给出了环境因素下城市交通离散网络设计问题中确定最大OD需求量的优化模型。在分支定界思想的基础上设计了求解该模型的启发式算法。最后,通过简单的算例,说明该算法是可行并且有效的。     

A Dynamic Route Choice Model Considering Uncertain Capacities

Abstract: The standard assumption in (dynamic) traffic assignment models is that route choice is solely determined by a (perceived) deterministic travel time. However, recently, there is a growing interest in (dynamic) equilibrium route choice models in which travelers not only select their paths based on an estimated deterministic travel time, but also based on travel time reliability, in this article defined as the probability that the actual travel time deviates from the anticipated value. We extend the linear programming cell transmission model‐based dynamic traffic assignment (LP CTM‐DTA) model to account for travelers’ consideration of uncertainty regarding saturation flow rates (in this article referred to as capacities). It is shown that these reliability considerations can be accounted for by simply reducing the road capacities appearing in the constraint set of the classical LP CTM‐DTA model. More importantly, we provide results on the amount of capacity reduction necessary to ensure a certain reliability level. Although in the proposed model any probability distribution can be used to model the uncertainty, the selection of a specific probability distribution can potentially be burdensome for the modeler. To this end, we also present results on the class of symmetric probability distributions that has been particularly popular in the robust optimization literature. Properties for this broad class of distributions will be derived within the context of the introduced model. In numerical case studies, the model predicts that travel patterns can be significantly different when accounting for travelers’ reliability considerations.     

Dynamic origin‐destination flow estimation using automatic vehicle identification data: A 3D convolutional neural network approach

Dynamic origin‐destination (OD) flow estimation is one of the most fundamental problems in traffic engineering. Despite numerous existing studies, the OD flow estimation problem remains challenging, as there is large dimensional difference between the unknown values to be estimated and the known traffic observations. To meet the needs of active traffic management and control, accurate time‐dependent OD flows are required to understand time‐of‐day traffic flow patterns. In this work, we propose a three‐dimensional (3D) convolution‐based deep neural network, “Res3D,” to learn the high‐dimensional correlations between local traffic patterns presented by automatic vehicle identification observations and OD flows. In this paper, a practical framework combining simulation‐based model training and few‐shot transfer learning is introduced to enhance the applicability of the proposed model, as continuously observing OD flows could be expensive. The proposed model is extensively tested based on a realistic road network, and the results show that for significant OD flows, the relative errors are stable around 5%, outperforming several other models, including prevalent neural networks as well as existing estimation models. Meanwhile, corrupted and out‐of‐distribution samples are generated as real‐world samples to validate Res3D's transferability, and the results indicated a 60% improvement with few‐shot transfer learning. Therefore, this proposed framework could help to bridge the gaps between traffic simulations and empirical cases.     

Real-Time OD Estimation Using Automatic Vehicle Identification and Traffic Count Data

A key input to many advanced traffic management operations strategies are origin–destination (OD) matricies. In order to examine the possibility of estimating OD matricies in real-time, two constrained OD estimators, based on generalized least squares and Kalman filtering, were developed and tested. A one-at-a-time processing method was introduced to provide an efficient organized framework for incorporating observations from multiple data sources in real-time. The estimators were tested under different conditions based on the type of prior OD information available, the type of assignment available, and the type of link volume model used. The performance of the Kalman filter estimators also was compared to that of the generalized least squares estimator to provide insight regarding their performance characteristics relative to one another for given scenarios. Automatic vehicle identification (AVI) tag counts were used so that observed and estimated OD parameters could be compared. While the approach was motivated using AVI data, the methodology can be generalized to any situation where traffic counts are available and origin volumes can be estimated reliably. The primary means by which AVI data was utilized was through the incorporation of prior observed OD information as measurements, the inclusion of a deterministic link volume component that makes use of OD data extracted from the latest time interval from which all trips have been completed, and through the use of link choice proportions estimated based on link travel time data. It was found that utilizing prior observed OD data along with link counts improves estimator accuracy relative to OD estimation based exclusively on link counts.     

Genetic Algorithm-Simulation Methodology for Pavement Maintenance Scheduling

Abstract:   Pavement maintenance activities often involve lane closures, leading to traffic congestion and causing increases in road users' travel times. Scheduling of such activities should minimize the increases in travel times to all the travelers at network level. This article presents a hybrid methodology for scheduling of pavement maintenance activities involving lane closure in a network consisting of freeways and arterials, using genetic algorithm (GA) as an optimization technique, coupled with a traffic-simulation model to estimate the total travel time of road users in the road network. The application of this scheduling method is demonstrated through a hypothetical problem consisting of assigning three maintenance teams to handle 10 job requests in a network in 1 day. After 10 generations of genetic evolution with a population size of four, the hybrid GA-simulation model recommended a schedule that reduced the network total travel time by 5.1%, compared to the initial solution.     

考虑相关性的地下管网系统抗震可靠度分析方法

城市供水、供气、供热等地下管网是重要的生命线工程系统,由于管网中不同管线的结构属性、场地环境、地震动荷载等参数的相关性,管线结构抗震失效事件存在相关性。文章建立相关失效管网系统抗震可靠度问题的解析模型,给出随机变量相关系数在管线可靠度至管网系统可靠度的传递关系。利用基于Nataf变换和正交变换的一次二阶矩法,建立含相关随机变量的管线结构抗震可靠度解析模型;基于结构体系可靠度理论,求解管线结构失效相关系数;采用网络可靠度算法,计算相关失效管网系统节点连通可靠度。同时,给出基于相关随机变量抽样的Monte Carlo随机模拟解法。算例分析结果表明,在管段结构功能函数起控制作用的随机变量的强相关性,对管网系统可靠度影响较大;与传统的管线失效独立假定相比,管线失效事件的强相关性可使并联管网系统可靠度减小15%以上;管网系统拓扑结构的并联冗余性越高,管线失效相关性对系统可靠度的影响越大。