逐日路径演化中出行者信息偏好的实验分析

在逐日出行过程中,出行者对各类信息的偏好是一个动态变化的过程,不同出行者对同类信息的偏好也不同.本研究搭建不同信息条件下逐日路径选择实验场景,用信息偏好系数表征不同类别信息对出行者感知时间影响的相对权重,研究出行者信息偏好的演化规律与分布特征.实验结果表明,出行个体信息偏好的演化存在3种类型,出行群体对感知时间的信息偏好系数先震荡增加后逐步稳定,出行个体对感知时间的信息偏好系数在出行群体中成正态分布.与只提供完全历史信息相比,同时提供基于指数平滑法的系统预测信息会降低出行者对感知时间的偏好.     

Autonomous cars: The tension between occupant experience and intersection capacity

Systems that enable high levels of vehicle-automation are now beginning to enter the commercial marketplace. Road vehicles capable of operating independently of real-time human control under an increasing set of circumstances will likely become more widely available in the near future. Such vehicles are expected to bring a variety of benefits. Two such anticipated advantages (relative to human-driver vehicle control) are said to be increased road network capacity and the freeing up of the driver-occupant’s time to engage in their choice of leisurely or economically-productive (non-driving) tasks.In this study we investigate the implications for intersection capacity and level-of-service of providing occupants of automated (without real-time human control), autonomously-operating (without vehicle-to-X communication) cars with ride quality that is equivalent (in terms of maximum rates of longitudinal and lateral acceleration) to two types of rail systems: [urban] light rail transit and [inter-urban] high-speed rail. The literature suggests that car passengers start experiencing discomfort at lower rates of acceleration than car drivers; it is therefore plausible that occupants of an autonomously-operating vehicle may wish to instruct their vehicle to maneuver in a way that provides them greater ride comfort than if the vehicle-control algorithm simply mimicked human-driving-operation.On the basis of traffic microsimulation analysis, we found that restricting the dynamics of autonomous cars to the acceleration/deceleration characteristics of both rail systems leads to reductions in a signalized intersection’s vehicle-processing capacity and increases in delay. The impacts were found to be larger when constraining the autonomous cars’ dynamics to the more-restrictive acceleration/deceleration profile of high-speed rail. The scenarios we analyzed must be viewed as boundary conditions, because autonomous cars’ dynamics were by definition never allowed to exceed the acceleration/deceleration constraints of the rail systems. Appropriate evidence regarding motorists’ preferences does not exist at present; establishing these preferences is an important item for the future research agenda.This paper concludes with a brief discussion of research needs to advance this line of inquiry.     

Recursive estimation of average vehicle time headway using single inductive loop detector data

Vehicle time headway is an important traffic parameter. It affects roadway safety, capacity, and level of service. Single inductive loop detectors are widely deployed in road networks, supplying a wealth of information on the current status of traffic flow. In this paper, we perform Bayesian analysis to online estimate average vehicle time headway using the data collected from a single inductive loop detector. We consider three different scenarios, i.e. light, congested, and disturbed traffic conditions, and have developed a set of unified recursive estimation equations that can be applied to all three scenarios. The computational overhead of updating the estimate is kept to a minimum. The developed recursive method provides an efficient way for the online monitoring of roadway safety and level of service. The method is illustrated using a simulation study and real traffic data.     

A stochastic model of traffic flow: Theoretical foundations

In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.     

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

Time use in travel surveys and time use surveys – Two sides of the same coin?

An in-depth understanding of travel behaviour determinants, including the relationship to non-travel activities, is the foundation for modelling and policy making. National Travel Surveys (NTS) and time use surveys (TUS) are two major data sources for travel behaviour and activity participation. The aim of this paper is to systematically compare both survey types regarding travel activities and non-travel activities. The analyses are based on the German National Travel Survey and the German National Time Use Survey from 2002.The number of trips and daily travel time for mobile respondents were computed as the main travel estimates. The number of trips per person is higher in the German TUS when changes in location without a trip are included. Location changes without a trip are consecutive non-trip activities with different locations but without a trip in-between. The daily travel time is consistently higher in the German TUS. The main reason for this difference is the 10-min interval used. Differences in travel estimates between the German TUS and NTS result from several interaction effects. Activity time in NTS is comparable with TUS for subsistence activities.Our analyses confirm that both survey types have advantages and disadvantages. TUS provide reliable travel estimates. The number of trips even seems preferable to NTS if missed trips are properly identified and considered. Daily travel times are somewhat exaggerated due to the 10-min interval. The fixed time interval is the most important limitation of TUS data. The result is that trip times in TUS do not represent actual trip times very well and should be treated with caution.We can use NTS activity data for subsistence activities between the first trip and the last trip. This can potentially benefit activity-based approaches since most activities before the first trip and after the last trip are typical home-based activities which are rarely substituted by out-of-home activities.     

大跨斜拉桥施工与成桥状态风致抖振响应分析

为研究大跨斜拉桥成桥与施工状态的风致抖振响应,分别采用时域和频域方法对一座典型大跨斜拉桥的成桥状态、施工最大双悬臂与最大单悬臂状态进行了数值计算.采用改进的谐波合成法模拟桥梁结构的随机脉动风场,基于有限元编程,实现了考虑自激力的斜拉桥抖振时域分析,使用多模态耦合分析方法进行斜拉桥的频域抖振分析.分析结果表明:在主梁设计基准风速下,成桥状态和施工状态的横桥向和扭转角抖振位移均较小,施工最大双悬臂中跨悬臂端点竖桥向抖振位移较大,在施工中应妥善处理;成桥与施工状态下的主塔塔顶抖振位移均较小,施工过程中可以不考虑主塔顶部的位移控制;基于合理模拟风场的时域计算方法,能够考虑各种非线性因素,能够较好地反映斜拉桥的抖振响应;不考虑气动导纳的频域计算会夸大斜拉桥的抖振响应,考虑Sears函数作为气动导纳的频域计算方法会低估斜拉桥的抖振响应.     

基于有效绿灯时间利用率的自适应控制策略研究

为了优化单点交叉口信号控制方案,使其适应各个进口道方向交通流动态变化,提高交叉口通行效率,根据交叉口进口道排队车辆数建立有效绿灯利用率模型,提出了一种交叉口自适应控制策略.有效绿灯时间利用率模型以交叉口通行能力最大为控制参数,实时优化确定出最佳相位放行方案以及最优相位切换方案,根据进口道排队车辆最大流向的排队车辆数和车辆到达预测确定相位放行绿灯时间.利用VISSIM交通仿真软件对该自适应控制策略仿真运行,与定时控制以及感应控制对比,评价分析不同车辆到达情况下交叉口通行情况.结果表明:该自适应控制策略能有效降低车均延误,提高交叉口服务水平.     

快速公交发车间隔优化研究

合理的发车间隔对于快速公交车辆发挥其高效、经济和环保的优势具有重要的意义.首先以乘客出行成本和快速公交运营成本最小化为目标,考虑发车时间约束、车辆台数约束,建立了快速公交发车间隔优化模型.然后采用二进制编码,运用单点交叉和基本位变异的遗传算法求解该优化模型.最后以兰州市首条快速公交线路为例进行了实证研究,得到了不同时段下快速公交的发车间隔.实例研究结果表明,该发车间隔优化模型及遗传算法可行,对实现快速公交科学调度具有一定的参考意义.     

基于 M/G/1模型的停车场出口排队延误

通过对大型停车场高峰时段出口车辆行驶特性的调查研究,选取了车辆排队的出口车头时距以及出口服务时间两个指标。基于两个指标实测特性和 M/G/1排队模型的分析,计算了停车场出口理论平均排队时间。通过实测车辆平均排队时间,对理论模型计算的平均排队时间进行了修正。实例分析表明：排队时间修正模型能够准确地估计排队时间。提出了基于驾驶员容忍度的排队延误分级标准。