Path-constrained traffic assignment: A trip chain analysis under range anxiety

This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows. The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles. This need arises from the facts that electric vehicles suffer from the “range anxiety” issue caused by the unavailability or insufficiency of public electricity-charging infrastructures and the far-below-expectation battery capacity. It is suggested that if range anxiety makes any impact on travel behaviors, it more likely occurs on the trip chain level rather than the trip level, where a trip chain here is defined as a series of trips between two possible charging opportunities (Tamor et al., 2013). The focus of this paper is thus given to the development of the modeling and solution methods for the proposed traffic assignment problem. In this modeling paradigm, given that trip chains are the basic modeling unit for individual decision making, any traveler’s combined travel route and activity location choices under the distance limit results in a distance-constrained, node-sequenced shortest path problem. A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions. The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium.     

直流感应电治疗焦虑症的随机对照研究

目的探讨直流感应电治疗焦虑症的效果。方法选取本院住院焦虑症患者69例,随机分为对照组和研究组。对照组给予常规的药物治疗,研究组在常规药物治疗的同时给予直流感应电治疗,1次/d,20min/次,10d为1疗程。分别于入组时及治疗10、20d后,用汉密尔顿焦虑量表(HAMA)测评。结果研究组和对照组的总有效率分别为88.57%和64.71%,研究组优于对照组,差异有统计学意义(P<0.05)。治疗10、20d时研究组的减分率均高于同期对照组,且差异有统计学意义(P<0.05)。结论在药物治疗基础上配合直流感应电治疗焦虑症,能够有效缓解患者的躯体症状,缩短起效时间。     

焦虑对非英语专业大学生英语口语学习的影响及对策

如今越来越多的人认识到情感因素对语言学习的影响。语言焦虑是语言学习中所特有的一种复杂的心理现象,被认为是最关键的情感因素之一。本文分析了语言焦虑对非英语专业大学生口语学习的影响,指出了课堂中会导致学生焦虑的一些情况,并根据克拉申的情感过滤假说,提出多种降低焦虑感的教学策略以促进大学英语口语教学。     

无目标语环境下的英语课堂焦虑和互动模式

在无目标语环境下,学生的课堂参与是目的语口头输出和操练的主要途径.焦虑是影响学生课堂口头参与的主要因素之一.本文通过问卷调查和课堂观察等实证方法,研究了学生课堂焦虑水平对其在小组活动、全班活动、和教师引导活动等三种互动模式中参与频率的影响,并给出了教学启示.     

大学生焦虑和抑郁的研究

了解高职学生个性特征与焦虑、抑郁状况的关系,以便为开展有针对性的心理卫生工作提供依据。采用分层抽样的方法,抽取某高职学生639名,进行焦虑自评量表(SAS),抑郁自评量表(SDS)和艾森克个性问卷(EPQ)的测评。高职生的焦虑、抑郁状况与人格特征之间显著相关。塑造健康的个性是降低焦虑、抑郁,减少心理障碍的有效途径。     

新能源汽车续驶里程焦虑及维修技术研究

续驶里程焦虑是新能源汽车售后市场抱怨最多的问题。文章从售后角度分析了导致续驶里程焦虑的原因及维修排查技术,提出了一些建议性措施。旨在为售后相关人员提供一定的理论指导,从而推动售后市场更好的发展。     

电动汽车能耗预测的研究

随着电动汽车的数量日趋增多,人们对于电动汽车能耗的关注度也逐渐增加。电动汽车受到电池性能和容量的约束,同时受到充电桩的位置、数量和充电时间的限制,容易使得驾驶员产生"里程焦虑"。因此续驶里程成为驾驶员关注的重要指标之一。准确的能耗预测能够给予驾驶员出行指导,减缓"里程焦虑"。围绕电动汽车能量消耗的影响因素进行了归纳总结,介绍了近年来能耗预测技术的研究现状和不足,并提出未来研究方向的发展趋势。     

Gas anxiety and the charging choices of plug-in hybrid electric vehicle drivers

Plug-in hybrid electric vehicles (PHEVs) can provide many of the benefits of battery electric vehicles (BEVs), such as reduced petroleum consumption and greenhouse gas emissions, without the “range anxiety” that can accompany driving a vehicle with limited range when there are few charging opportunities. However, evidence indicates that PHEVs are often plugged in more frequently than BEVs in practice. This is somewhat paradoxical: drivers for whom plugging in is optional tend to do so more frequently than those for whom it is necessary. This has led to the coining of a new term – “gas anxiety” – to describe the apparent desire of PHEV drivers to avoid using gasoline. In this paper, we analyze the variables influencing the charging choices of PHEV owners, testing whether drivers express preferences consistent with the concept of gas anxiety. We analyze data collected in a web-based stated preference survey using a latent class logit model. The results reveal two classes of decision-making patterns among the survey respondents: (1) those who weight the cost of gasoline and the cost of recharging approximately equally (the cost-minimizing class), and (2) those who weight the cost gasoline more heavily than the cost of recharging (the gas anxiety class). Respondents in the gas anxiety class expressed a willingness to recharge at a charging station even when doing so would cost approximately four times as much as the cost of the gasoline avoided. While the gas anxiety class represents the majority of our sample, more recent PHEV adopters are more likely to be in the cost-minimizing class. Looking forward, this suggests that public charging station operators may need to price charging competitively with gasoline on a per-mile basis to attract PHEV owners.     

Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data

This paper studies electric vehicle charger location problems and analyzes the impact of public charging infrastructure deployment on increasing electric miles traveled, thus promoting battery electric vehicle (BEV) market penetration. An activity-based assessment method is proposed to evaluate BEV feasibility for the heterogeneous traveling population in the real world driving context. Genetic algorithm is applied to find (sub)optimal locations for siting public charging stations. A case study using the GPS-based travel survey data collected in the greater Seattle metropolitan area shows that electric miles and trips could be significantly increased by installing public chargers at popular destinations, with a reasonable infrastructure investment.     

Network user equilibrium of battery electric vehicles considering flow-dependent electricity consumption

This paper addresses the equilibrium traffic assignment problem involving battery electric vehicles (BEVs) with flow-dependent electricity consumption. Due to the limited driving range and the costly/time-consuming recharging process required by current BEVs, as well as the scarce availability of battery charging/swapping stations, BEV drivers usually experience fear that their batteries may run out of power en route. Therefore, when choosing routes, BEV drivers not only try to minimize their travel costs, but also have to consider the feasibility of their routes. Moreover, considering the potential impact of traffic congestion on the electricity consumption of BEVs, the feasibility of routes may be determined endogenously rather than exogenously. A set of user equilibrium (UE) conditions from the literature is first presented to describe the route choice behaviors of BEV drivers considering flow-dependent electricity consumption. The UE conditions are then formulated as a nonlinear complementarity model. The model is further formulated as a variational inequality (VI) model and is solved using an iterative solution procedure. Numerical examples are provided to demonstrate the proposed models and solution algorithms. Discussions of how to evaluate and improve the system performance with non-unique link flow distribution are offered. A robust congestion pricing model is formulated to obtain a pricing scheme that minimizes the system travel cost under the worst-case tolled flow distribution. Finally, a further extension of the mathematical formulation for the UE conditions is provided.