Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California

Using latent class cluster analysis, this paper investigates the spatial, social, demographic, and economic determinants of immigrants’ joint distribution among travel time, mode choice, and departure time for work using the 2000 Census long form data. Through a latent tree structure analysis, age, residential location, immigration stage, gender, personal income, and race are found to be the primary determinants in the workplace commute decision-making process. By defining several relatively homogeneous population segments, the likelihood of falling into each segment is found to differ across age groups and geography, with different indicators affecting each group differentially. This analysis complements past studies that used regression models to investigate socio-demographic indicators and their impact on travel behavior in two distinct ways: (a) analysis is done by considering travel time, mode choice, and departure time for work simultaneously, and (b) heterogeneity in behavior is accounted for using methods that identify different groups of behavior and then their determinants. Conclusively the method here is richer than many other methods used to study the ethnically diverse population of California and shows the addition of geographic location and latent segment identification to greatly improve our understanding of specific behaviors. It also provides evidence that immigrants are as diverse as the non-immigrant population and transportation policies need to be defined accordingly.

Activity patterns in space and time: calculating representative Hagerstrand trajectories

Daily activity diaries can be recorded as sequences of characters representing events and their contexts as they unfold during the day. Dynamic programming algorithms as used in bioinformatics have been used by a number of researchers to measure the similarities and differences between travel patterns on the basis of temporal sequencing of events, activity transition, and total activity time. The resultant similarity matrices have been shown to be more effective in classifying sequential patterns than classifications based on alternative similarity indices. The basic algorithms can be amended to include the geographic coordinates of events by a suitable amendment to the definition of distance. This permits quantitative classification of Hagerstrand-type activity trajectories on the basis of both activity and spatial similarity. Such a classification can be used to group similar trajectories and to identify representative trajectories that are analogous to measures of central tendency in univariate statistics, giving more concrete meaning to the concept of the activity pattern than any other method now available. The paper illustrates the effect of considering both events and locations in the classification of daily activity patterns using activity diary data gathered in the town of Reading. The algorithm has been implemented in the Clustal_TXY alignment software package.

航班延误恢复调度的混合粒子群算法

为了优化航班延误恢复调度,考虑了航班延误的经济效益、社会影响和经济损失构成,定义了航线影响因子,构建了一种新的航班延误恢复调度模型,将局部搜索方法引入到粒子群算法中,提出了求解航班延误恢复调度问题的混合粒子群算法。计算结果表明:与先来先服务调度方法相比,混合粒子群算法可以减少航班延误损失4.2%,与基本粒子群算法和进化策略算法相比,混合粒子群算法平均可减少航班延误损失2.0%,随着航班延误恢复规模的增大,算法优势会更明显。     

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简单输入输出模型与冲击波分析是研究交通瓶颈问题的两种常用方法,本文着重分析两者的一致性。在数学推导中考虑到达需求与流出率的变化时,两者对于排队和延误的预测结果是一致的。在不同的交通和边界条件下,两者在拥堵形成至消散的任意时刻所得排队长度,拥堵时间等结果均是一致的。以往认为两种方法不一致的研究忽略了一些与背景交通密切相关的基本因素。实例分析说明正是这些基本因素使得这两种分析方法在不同流量－密度关系下都具有一致性。     

A framework for travel time variability analysis using urban traffic incident data

This study aims to develop a framework to estimate travel time variability caused by traffic incidents using integrated traffic, road geometry, incident, and weather data. We develop a series of robust regression models based on the data from a stretch in California's highway system during a two-year period. The models estimate highway clearance time and percent changes in speed for both downstream and upstream sections of the incident bottleneck. The results indicate that highway shoulder and lane width factor adversely impact downstream highway clearance time. Next, travel time variability is estimated based on the proposed speed change models. The results of the split-sample validation show the effectiveness of the proposed models in estimating the travel time variability. Application of the model is examined using a micro-simulation, which demonstrates that equipping travelers with the estimated travel time variability in case of an incident can improve the total travel time by almost 60%. The contribution of this research is to bring several datasets together, which can be advantageous to Traffic Incident Management.     

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。     

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为了描述无信号交叉口混合车流的等待延误特性，论文建立了由大小两种车型构成的混合车流的等待延误公式。本文在分析了目前无信号交叉口延误研究方法存在某些不足的基础之上，以可接受间隙理论为基础，建立了无信号交叉口大小两种车型构成的次要车流的等待延误公式。通过选取适当的参数数据，分析了次要车流等待延误与主要车流流量、次要车流不同车型比例构成的关系，结果表明该公式较为符合无信号交叉口实际情况。     

基于希尔伯特变换的应力波波速估计的研究

研究基于希尔伯特变换的时间延迟估计方法及其算法,算法通过MATLAB语言编程实现。制作了4块混凝土板并在其上开展基于冲击反射法的双传感器波速测量实验,将互相关时延估计和希尔伯特变换时延估计应用到波速估计中,两者均能解决直接观察法中的人为误差的影响和噪声对测量精度的影响,取得比较满意的结果,希尔伯特变换时延估计得到的波速精度比互相关时延估计得到的波速估计精度更高。     

考虑排放因素的公交优先信号控制优化方法研究

以4相位单交叉口为研究对象,保持信号周期不变的情况下,以减少人的总延误和交叉口区域尾气排放量为目标,通过改变各相位绿灯时间来建立优化模型。运用粒子群算法对模型求解。仿真算例表明,此方法在降低交叉口区域人总延误的同时,减少了该区域内机动车尾气排放量,在保障车辆正常通行的情况下改善了环境质量。     

An investigation of the effects of pneumatic actuator design on slip control for heavy vehicles

Progress in reducing actuator delays in pneumatic brake systems is opening the door for advanced anti-lock braking algorithms to be used on heavy goods vehicles. However, little has been published on slip controllers for air-braked heavy vehicles, or the effects of slow pneumatic actuation on their design and performance. This paper introduces a sliding mode slip controller for air-braked heavy vehicles. The effects of pneumatic actuator delays and flow rates on stopping performance and air (energy) consumption are presented through vehicle simulations. Finally, the simulations are validated with experiments using a hardware-in-the-loop rig. It is shown that for each wheel, pneumatic valves with delays smaller than 3 ms and orifice diameters around 8 mm provide the best performance.