因子分析法和聚类分析法在服刑人员行为矫正评估中的应用

文章针对服刑人员行为矫正评估中测试指标多、计算量大导致的不易观测问题,提出了采用因子分析和聚类分析法对不同测题进行有分类抽题和组题,利用聚类分析对测评成绩的合理归类,进而对数据进行综合分析的评定方法,并以图表方式实例化展示了分析结果。     

孕鼠噪声暴露对仔代行为发育的影响

Ｗｉｓｔａｒ孕鼠２４只，随机等分为３组，于妊娠５天～１８天分别接受１００ｄＢ（Ａ）、８５ｄＢ（Ａ）和＜５０ｄＢ（Ａ）（对照组）的稳态噪声刺激，观察仔代出生后生长发育情况。结果：１００ｄＢ（Ａ）组仔鼠出生体重、身长显著低于对照组仔鼠，嗅觉、神经肌肉发育延迟，紧张性情感较对照组高，学习记忆能力低于对照组仔鼠。提示：１００ｄＢ（Ａ）的孕期噪声暴露，可对仔鼠行为发育产生一定影响。     

Electric vehicles’ energy consumption measurement and estimation

Use of electric vehicles (EVs) has been viewed by many as a way to significantly reduce oil dependence, operate vehicles more efficiently, and reduce carbon emissions. Due to the potential benefits of EVs, the federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve efficiency for EV transportation system? This research takes the first step in tackling this challenge by addressing a fundamental issue, i.e. how to measure and estimate EVs’ energy consumption. In detail, this paper first presents a system which can collect in-use EV data and vehicle driving data. This system then has been installed in an EV conversion vehicle built in this research as a test vehicle. Approximately 5 months of EV data have been collected and these data have been used to analyze both EV performance and driver behaviors. The analysis shows that the EV is more efficient when driving on in-city routes than driving on freeway routes. Further investigation of this particular EV driver’s route choice behavior indicates that the EV user tries to balance the trade-off between travel time and energy consumption. Although more data are needed in order to generalize this finding, this observation could be important and might bring changes to the traffic assignment for future transportation system with a significant share of EVs. Additionally, this research analyzes the relationships among the EV’s power, the vehicle’s velocity, acceleration, and the roadway grade. Based on the analysis results, this paper further proposes an analytical EV power estimation model. The evaluation results using the test vehicle show that the proposed model can successfully estimate EV’s instantaneous power and trip energy consumption. Future research will focus on applying the proposed EV power estimation model to improve EVs’ energy efficiency.     

Q235钢焊接熔合区在混合菌共同作用下的腐蚀行为

通过观察腐蚀形貌,测量极化曲线和阻抗谱,研究了Q235钢熔合区在SRB和V.natriegens共同作用下的腐蚀行为。研究表明,熔合区的腐蚀程度,单一菌溶液中,SRB大于V.natriegens,混合菌溶液大于单一菌;混合菌溶液中SRB和V.natriegens表现出协同作用,加速试样的腐蚀;试样的耐腐蚀性能,熔合区小于焊缝区小于母材区。     

相邻隧道扩挖施工影响分析

采用现场实测和数值计算方法,对乌鞘岭志留系地层板岩夹千枚岩地段左线隧道扩挖施工引起右线隧道的围岩状况和既有支护结构的受力变化进行分析。结果表明,两相邻隧道在不同施工阶段进行导洞开挖和扩挖前后存在一定程度的群洞效应作用,扩挖施工引起右线隧道向左线隧道产生了较小的侧移;在左线隧道扩挖后,右线隧道既有衬砌的接触压力、结构轴力和弯矩均有明显增大,但衬砌结构的安全系数均满足规范要求,处于安全状态。     

公路平面交叉口驾驶行为研究

面对目前公路平面交叉口交通事故严重的现状,为了公路平面交叉口交通事故致因分析和加深对交叉口驾驶行为的了解程度,探讨了公路平面交叉口驾驶行为理论,总结了目前道路交通系统交通事故原因及其导致事故所占比例,分析了交叉口交通系统,给出了影响交叉口驾驶行为的具体因素。在驾驶行为三阶段基础上,研究了交叉口驾驶行为模型,分析了交叉口驾驶行为特征和交叉口驾驶行为与交通事故发生的一般关系。并针对驾驶失误和不安全驾驶行为给出了防止措施,以减少交叉口交通事故。     

交叉口混合交通流跟驰行为研究

为了研究机动车流和非机动车流在交叉口交通信号灯影响下的过街跟驰情况,文章对西安市南稍门十字以及小寨十字进行了观测与记录,采用录制视频的方式,观测了在一个交通信号灯周期内车流的过街跟驰情况,采集并分析数据后,针对机动车流的跟驰行为构建了基于安全距离的跟驰模型,通过分析可以看出,在交叉口处机动车和非机动车过街行为受行人和机动车流影响较大,转向车流与直行的车流也会互相影响。     

Mode choice models' ability to express intention to change travel behaviour considering non-compensatory rules and latent variables

Disaggregate behaviour choice models have been improved in many aspects, but they are rarely evaluated from the viewpoint of their ability to express intention to change travel behaviour. This study compared various models, including objective and latent models and compensatory and non-compensatory decision-making models. Latent models contain latent factors calculated using the LISREL (linear structural relations) model. Non-compensatory models are based on a lexicographic-semiorder heuristic. This paper proposes ‘probability increment’ and ‘joint probability increment’ as indicators for evaluating the ability of these models to express intention to change travel behaviour. The application to commuting travel data in the Chukyo metropolitan area in Japan showed that the appropriate non-compensatory and latent models outperform other models.     

Experimental Study of Effects of Travel Time Distribution Information on Dynamic Route Choice Behavior

This study focuses on the possibility that providing travelers with information on travel time distribution, along with the existing travel time information, might affect their decision making and enhance traffic control. As an initial step to confirm the effectiveness of travel time distribution information, we use a laboratory experiment to obtain panel data of route choices when information is provided. The rationality of the behavior of the respondents and the characteristics of the route choice are first analyzed by aggregation analysis and then statistically examined by specifying a mixed logit route choice model. As a result, it is revealed that both travel time information and maximum travel time information significantly affect route choice behavior when a penalty is imposed for late arrival.     

Using a stages of change approach to explore opportunities for increasing bicycle commuting

Given the potential benefits of bicycling to the environment, the economy, and public health, many U.S. cities have set ambitious goals for increasing the bicycle share of commute trips. The Transtheoretical Model of Behavior Change, which seeks to describe how positive and permanent change can be fostered in individuals, may shed light on how cities can most effectively increase bicycle commuting. We use the model’s “stages of change” framework to explore the potential for increased bicycle commuting to the UC Davis campus in Davis, California. Our analysis uses data from the 2012 to 2013 UC Davis Campus Travel Survey, an annual online survey that is randomly administered to students and employees at UC Davis. Based on their responses to questions about current commute mode and contemplation of bicycle commuting, respondents are divided into five stages of change: Pre-contemplation, Contemplation, Preparation, Action, and Maintenance. We construct a Bayesian multilevel ordinal logistic regression model to understand how differences in socio-demographic characteristics, travel attributes, and travel attitudes between individuals explain their membership in different stages of change. In addition, we use this model to explore the potential of various intervention strategies to move individuals through the stages of change toward becoming regular bicycle commuters. Our results indicate that travel attitudes matter more to progression toward regular commute bicycling than travel attributes, tentatively supporting the efficacy of “soft” policies focused on changing travel attitudes.