Climate change behaviors related to purchase and use of personal cars: Development and validation of eco-socially conscious consumer behavior scale

A constantly changing environment and global warming are issues that are recognized at all global forums. One of the major reasons for global warming is the emission of greenhouse gasses which is primarily caused by use of personal cars as means of transport. This study reports on the development of an eco-socially conscious consumer behavior (ESCCB) scale specific to purchase and use of personal cars, based on samples of actual automobile customers in Pakistan. Using mixed method approaches, the results of 3 studies yield a 9-item three-dimensional scale (eco-social conservation, eco-social use, and eco-social purchase) with satisfactory reliability, construct validity and nomological validity. Second-order factor analysis revealed that eco-social purchase was the most important dimension, followed by eco-conservation and eco-social use. A test of nomological behavior shows that the scale is positively associated with a related construct: environmental concern. This study advances the literature on pro-environmental behaviors by introducing a conceptual definition of ESCCB related to personal car purchase and use, developing a measure for the ESCCB concept and validating the scale in the context of an emerging economy, Pakistan. The scale provides important insights for marketers in the automobile industry for remodelling marketing plans, as well as for environmentalists focusing on strategies to bring change in consumer behavior.     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.     

Formal scientific research of traffic collision data utilizing GIS

This paper explores the tenuous link between speeding behavior and accident causation, one that has not been well established in the international literature to date. Taking advantage of established engineering conventions and formulae, we were able to set up an a priori hypothesis suitable for testing. Utilizing this formal scientific method (which GIS researchers have been criticised for not using) we establish a statistical link for this relationship. Our methodology can be used to support all police intervention strategies, including the controversial photo radar systems. The results from our research have been entered into a GIS in order to create a map for spatial display. This map illustrates the relative probability or risk of collision occurrence resulting from speeding at all intersections and interchanges within the scope of the study. It is suggested that this methodology could easily be maintained with periodic updates of data, thus creating a dynamic model from which to monitor traffic safety within the city. Furthermore, this model can be utilized to study specific strategies, allowing for the scrutiny of before, during and after effects. The study area is the entire city of Calgary, Alberta, Canada, and includes all traffic collisions occurring during the year of 1994.     

基于GOOSE机制的城市轨道交通中压环网继电保护方案优化

针对目前城市轨道交通中压环网继电保护存在的问题,提出了基于GOOSE(面向通用对象的变电站事件)机制的中压环网的继电保护优化方案,有效地解决了传统过流保护级差配合困难的问题.该方案是以GOOSE光纤以太网为基础,以闭锁逻辑为核心的数字通信电流保护方案.将GOOSE信号进行分类,并对中压供电环网系统各种可能出现的故障进行动作行为分析.最后,通过RTDS(动态数字仿真系统)仿真试验,证明该方案在各种故障情况下,都能保证继电保护的选择性、速动性、灵敏性和可靠性.     

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.     

水中悬浮隧道交通荷载模拟方法研究

为研究水中悬浮隧道交通荷载的合理模拟方法以及交通荷载对隧道结构的影响,借鉴铁路、公路交通荷载的模拟方法,综合考虑车辆轮载、路面不平度、行车速度以及外部激励荷载等影响因素的共同作用,提出水中悬浮隧道交通荷载的模拟表达式。通过数值模拟计算,分析悬浮隧道交通荷载的变化特征,并研究不同交通荷载模拟方法对悬浮隧道结构振动位移响应的影响。结果表明:文章提出的交通荷载模拟方法计算结果符合移动振动荷载的波动性和周期性特征。在对结构振动位移响应影响方面,固定均布荷载相当于静载,移动集中荷载和移动振动荷载时的位移变化幅值相对固定均布荷载时的大且影响相似,但移动振动荷载时的振幅稍大,体现了交通荷载中动荷载部分对结构振动位移响应的影响,更适合用来模拟悬浮隧道中的交通荷载。     

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。     

面向控制的SCR催化转化器温度模型

为了建立面向控制的SCR催化器温度模型,根据能量守恒和质量守恒方程并考虑热平衡中所涉及的换热过程不同,建立了4种SCR催化器温度的数学模型,并利用Matlab/Simulink构建图形化的计算模型。采用柴油机欧洲稳态循环(ESC)和欧洲瞬态循环(ETC)条件下的台架实测数据对模型进行检验,基于最大离散程度、拟合度和计算时间3个指标对模型进行评价和比较。结果表明,考虑催化器与废气和周围环境对流换热的温度模型具有良好的预测精度和实时性,更适合用于SCR控制策略。     

基于ARENA的双车道超车视距设计风险评价

通过计算机仿真软件ARENA模拟双向两车道路段上不同的超车视距长度下的超车行为,并建立风险指标。风险指标主要根据超车动作结束时,超越车与对向车辆间的最短时间距离确定。一共确定了六个风险等级,用于评价不同设计时速下的各种PSD长度的风险。利用此方法可以很直接的评价超车视距设计的优劣。     

基于灰色残差GM（1，1）模型的道路交通量预测的研究

道路交通体系是一个多因素、多层次、多目标的复杂系统。其中交通量信息系统具有明显的层次复杂性,结构关系的模糊性,动态变化的随机性,指标数据的不完全和不确定性。由于技术方法、人为因素、自然环境变化的影响,造成各种数据误差、短缺甚至虚假现象,系统的作用机制不明确,系统的状态、结构、边界关系难以精确描述,属于典型的灰色系统。在作量化、模型化、实体化研究时,能作为反映系统主要动态特征的数据是很少的。由于环境对系统的干扰,系统信息中原始数据序列往往呈现离乱情况,离乱数列即为灰色数列或称灰色过程,灰色理论利用那些较少的或不确切的表示系统行为特征的原始数据序列作生成变换后建立微分方程,对灰色过程建立的模型称为灰色模型（Greymodel）,简称GM模型。本文从理论上介绍了GM（1,1）模型和灰色残差GM（1,1）模型建立的一般过程,然后将其应用于交通量预测的实际例子中。预测结果表明,该方法是可行的。