Relationships among service quality,corporate image,customer satisfaction,and behavioral intention for the elderly in high speed rail services

Since the Taiwan High Speed Rail operations, Taiwan's transportation market entered into a new era. Because of its competitive service of speed, convenience, environmental concerns and comfort, the High Speed Rail has not only made significant changes but has played a significant role in Taiwan's transportation. However, Taiwan now is an aging society. Due to the physical constraints among the elderly, demands to redesign the traffic system and maintain transportation safety are essential considerations. In the current market, Taiwan's transportation construction is facing fewer barriers; however, it must still improve, especially considering the health of the elderly. Thus, this study investigates elderly passengers' demands and further examined the relationships among service quality, corporate image, customer satisfaction, and behavioral intention. According to empirical analytical results based on structural equation modeling (N = 341), satisfaction directly affected travel behaviors, while service quality and corporate image played indirect roles. In addition, service quality plays a significant role on the effect of satisfaction. This study provides empirical evidence to indicate the quality of the accessible environment affects not only the effectiveness and efficiency of service quality, but also, the corporate image. The results provide valuable references for critically managing the elderly's usage of the high speed rail transportation service. Copyright © 2011 John Wiley & Sons, Ltd.     

论言外行为在大学英语教学中的运用

在英语教学中,学生往往只理解句子的表面意思,却不理解句子的言外之意,不能通过言内和言外的连贯作用把文章的深层含义理解透彻;而老师由于时间关系,可能无法详细讲解言外行为在英语中的作用,从而师生都忽视它的作用.本文从理论和实践相结合的角度分析言外行为在英语教学中的作用.     

面向混合交通流的智能网联车鸣笛意图识别模型

为使混合交通流(Mixed Traffic Stream，MTS)下智能网联车(Intelligent Connected Vehicle，ICV)实现鸣笛意图(Horn’s Intention，HI)识别，更好地遵循常规车辆(Manual Vehicle， MV)的驾驶意图，提出ICV 对MV 鸣笛声的“ 感知(Perception) — 定位(Location) — 识别 (Recognition)”模型(简称HI-PLR)，采用深度卷积—循环神经网络(Deep Convolution Recurrent Neural Network, DCRNN)算法感知鸣笛车辆(Horning Vehicles, HV)的鸣笛声；采用到达时差 (Time Difference of Arrival, TDOA)算法定位HV；再基于运动时间窗(Motion Time Window, MTW)的支持向量机(Support Vector Machine, SVM)算法识别HI.实验结果表明，HI-PLR可使 ICV 对混流中车辆的鸣笛声感知准确率达90.4%，定位角度估计误差小于5°，HI 识别率达 82.5%，为ICV在MTS中的智能驾驶决策提供依据.     

基于驾驶意图识别的插电式混合动力汽车智能算法研究

对基于规则类的定参数控制算法中的控制参数进行优化,建立驾驶意图的模糊识别模型,制定智能算法,并进行仿真分析.结果表明:智能算法使得整车的需求转矩更加准确,车辆对驾驶员的适应性更强,燃油经济性更好.     

企业采购成本控制的措施

采购成本的降低对于提高企业经济效益,增强企业核心竞争力十分重要.文章提出,通过提高采购部门的成本意识、采购人员的业务培训、采购监督约束机制的完善、采购模式的合理选择等降低企业采购过程中的整体成本,从根本上解决企业采购成本居高不下的问题.     

Investigating UAVs applications and intention to use in the maritime shipping in Taiwan

ABSTRACT

This study empirically evaluated the effects of unmanned aerial vehicles (UAVs) applications and acceptable cost on intention to use UAVs in maritime shipping contexts. Factor analysis was employed to identify key UAVs applications (i.e. pollution forensics, supervision and service, search and rescue, humanitarian and emergency delivery, and safety and security), acceptable UAV cost, and intention to use UAVs. Survey data were collected from 201 respondents working for a maritime port corporation, shipping company, agency, and stevedoring company, and then hierarchical regression analysis was conducted to test research hypotheses and examine the effects of UAVs applications and acceptable cost dimensions on intention to use UAVs. The results suggested that the pollution forensics, supervision and service, search and rescue, humanitarian and emergency delivery, and safety and security dimensions positively affected intention to use UAVs. The study findings also revealed negative associations among seniority, turnover, and intention to use UAVs. Theoretical contributions and managerial implications are proposed to assist maritime port bureaus, corporations, and operating practice design in remaining competitive and efficient.     

面向安全预警的机动车驾驶意图研究现状与展望

简述当前开展面向安全预警的机动车驾驶意图研究的目的和意义.分析国内外研究现状,得出从驾驶员行为及驾驶动作序列角度开展驾驶意图研究的可行性和有效性,同时介绍了 2种基于概率与数理统计的机动车驾驶意图建模方法.结合驾驶员在直线封闭路段实施驾驶行为特征,阐述使用隐马尔科夫模型(HMM)理论建立驾驶意图模型的步骤以及模型参数学习和系统在线优化算法等内容.对驾驶意图模型网络结构、动态性能方面相关研究方向进行展望.     

解读劳伦斯诗歌《灵船》中"死亡"之意象

劳伦斯一生创作的诗歌颇多,在诗集《最后的诗歌》中他对“死亡”意象的专注尤其引起后人的重视．在前人研究的基础上对《最后的诗歌》中颇具代表性的“死亡”主题的诗作《灵船》做一些具体分析,主要从语言层面、意象层面和意蕴层面全面而系统地解读劳伦斯诗歌《灵船》中“死亡”意象与宗教和社会现实等的内在关系,以期能更加全面地了解劳伦斯诗歌创作的整体思想和意图．     

面向前车的驾驶行为感知与意图识别算法研究

感知周围车辆的驾驶行为并识别其意图将成为新一代高级驾驶辅助系统的重要组成部分。针对现有方法只考虑单一驾驶行为且可扩展性和可伸缩性差,提出一种基于稀疏表示理论的驾驶行为感知字典模型(Driving Behavior Perception Dictionary Model, DBPDM)。将车辆行驶状态视为时间序列,设计基于自回归积分移动平均(Autoregressive Integrated Moving Average,ARIMA)结合在线梯度下降(Online Gradient Descent, OGD)优化器的在线预测模型,提出基于驾驶行为预测的意图识别构架(Intention Recognition Framework, IRF)。首先,采用图Lasso方法估计典型驾驶行为的稀疏逆协方差矩阵构建驾驶行为字典库,并采用Logdet散度方法计算各逆协方差矩阵的差异获得行为感知字典模型。然后,基于在线预测模型对目标车辆的行驶轨迹和运动状态进行预测,结合主车车辆的行驶状态作为稀疏表示的观测信号,以获取预测时域内的目标车辆意图。最后,采用NGSIM (Next Generation SIMulation)真实驾驶数据对模型进行开发和测试。研究结果表明:所提出的行为感知模型能对6种典型驾驶行为构建行为字典,在分类准确率上与现有方法相比有明显提升,对换道和转向行为样本的平均识别准确率分别达到99.1%和92.9%;该模型能够在相对早期阶段准确地识别出车辆行为;在线预测算法能较好预测出目标车辆的行驶轨迹和运动状态,从而间接地反映出其在预测时域内的驾驶意图;IRF可在换道和转向行为开始前的1.5 s较为准确地识别出目标车辆的意图,平均识别准确率超过80%。