南京市2000~2014年城市建设用地变化及驱动因子研究

基于土地利用模型测度2000~2014年南京市城市建设用地的动态变化,再运用典型相关分析、主成分分析及逐步回归分析组合的偏最小二乘回归法对南京市城市建设用地的驱动因子进行分析。结果表明：（1）2000~2014年南京市城市建设用地面积总体上呈上升趋势,年均增长率为11.73%;（2）南京市用地结构信息熵呈波动下降趋势,城市建设用地系统结构性增强,但是用地扩展的综合效益趋降;（3）人口增长、道路扩展、投资水平提高及文化产业发展等能够较好的解释南京市城市建设用地的增长,这4类因子对城市建设用地变化具有正向作用;（4）逐步线性回归结果表明,园林绿地面积的增加相对其他因子对南京市建设用地规模增长的作用最大,其次是道路长度和公路里程。     

家庭部门生活能源消费碳排放:测度与驱动因素研究

本文运用IPCC的二氧化碳排放量测算方法,在省际层面测度了我国家庭部门直接能源消费碳排放,并基于扩展的STIRPAT和Kaya模型,构建家庭部门直接能源消费碳排放影响因子动态面板数据模型,对我国2003-2012年分省面板数据样本及城乡子样本进行系统GMM估计。本文研究表明,第一,我国城乡家庭部门碳排放总量和人均碳排放在近十年都呈快速上升趋势,家庭部门碳排放的空间分布具有明显的地域差异特征,高碳排放地区主要集中在东、中部地区,西部地区的碳排放水平较低;第二,城乡家庭部门生活能源消费的上期碳排放量对本期碳排放产生重要的正向影响,这反映出我国家庭部门碳排放具有显著的惯性特征和路径依赖性,是一种动态自适应机制;人口规模、居民消费水平、能源消费结构、碳排放强度、能源消费强度和城镇化因素,都对我国居民能源消费碳排放总量及人均碳排放具有显著的影响,城乡之间的家庭能源消费碳排放驱动因素存在差异。本文研究得到如下启示及政策含义:家庭部门碳减排将是一个有步骤、分区域的渐进过程,我国碳减排政策应当兼顾消费升级和碳排放的双重目标,努力构建分层次碳减排的适应性预期机制。具体而言,一方面应着眼于引导和激励居民低碳消费,缓解人口规模增加、消费水平提升和城镇化对家庭部门碳减排的压力;另一方面要通过能源价格改革、财政政策和环境规制政策等优化我国能源消费结构,不断降低煤炭消费比重,提高清洁能源的消费。同时,通过技术创新、设备改造等科技手段提高煤炭利用效率,降低碳排放强度,这些政策将更有利于城镇家庭部门碳减排。     

南方丘陵地区河谷城市用地时空演变与驱动分析——以上饶市城区为例

以揭示中国南方丘陵地区河谷城市的空间演变过程和特征为目的,以上饶市城区为例,在遥感与GIS技术的支持下,对1988~2013年间上饶市城区城市用地的时空演变过程、特征及驱动影响因素进行了研究。结果表明,1988~2013年的25 a间,上饶市城区城市用地呈现出快速增长的态势,城市形态的演变具有明显的时空特征:时间序列上,不论扩展幅度还是扩展速度,2001~2013年间的值均高于1988~2001年间的值,前后两个时段的扩展特征呈现出明显的差异性;空间格局上,上饶市城区以老城区的中心广场为中心向四周扩展,先后经历了放射性扩展、组团式分散扩展两个阶段,同时呈现出明显的方向分异性,西南和西为其主要扩展方向,而其东南方向的扩展速度最为缓慢。自然环境中的地貌对上饶市城区城市用地的扩展起到很大的限制作用,经济社会的发展、政策规划等因素成为城市用地扩张与时空演化的外在驱动因素。     

模拟车用工况下5kW燃料电池尾气净化系统

模拟了车用工况下燃料电池发动机脉冲式排氢的特点,设计开发了一套质子交换膜燃料电池的尾气净化系统,并以5 kW燃料电池的尾气排放为例,研究其净化效果。研究了电池阳极排放氢气缓冲前后尾气中氢气浓度的变化,以及不同空速条件下氢气的去除效果。结果表明：电堆阳极排放氢气经过缓冲处理后,尾气中氢气浓度趋于平稳,大部分工况下处在2%以下,达到了安全处理的要求;尾气中氢气的去除效果与空速密切相关。对自制的整体式催化剂而言,当空速低于20000 h^-1时,氢气的去除率能达到约95%,而当空速达到39270 h^-1时,氢气的去除率仅为10.9%。鉴于这一问题,提出通过采用尾排空气的分流手段调整催化燃烧反应器对空速的要求,以提高尾气中氢气的去除率。     

The Need for Scale Sensitive Approaches in Spatially Explicit Land Use Change Modeling

This paper introduces some of the issues that are relevant to the spatially explicit modeling of land use systems. A short overview is given of the ways and means in which a number of different land use change models describe the land use system. Specific attention is given to the empirical modeling approach used in the CLUE (Conversion of Land Use and its Effects) modeling framework. This approach is demonstrated for three case studies in China, Ecuador and the Atlantic Zone of Costa Rica. These case-studies illustrate the methodology for multi-scale analysis of land use driving factors and their application in spatially explicit modeling exercises. Model functioning, performance and limitations are discussed. The presented case-studies indicate that empirical modeling results can contribute to a better theoretic imbedding of land use change research in scale sensitive and integrated theories.     

基于加速度干扰和AHP的车辆行驶安全性评价模型

关于行驶中的车辆在交通系统各因素作用下的行驶安全性评价,在现有交通系统四大要素的基础上加入车辆行驶状态,构成"人-车-环境-路-车辆行驶状态"5要素。并对各要素评价指标的选定做了细致的筛选,建立了车辆行驶安全性评价指标体系。其中"人-车-环境-路"因素采用打分的形式确定其参数的分值,并利用层次分析法(AHP)计算各参数权重来构建模型的;"车辆行驶状态"因素则是引入加速度干扰的定义,建立基于道路结构的加速度干扰模型。最后,运用层级分析法建立了总的车辆行驶安全评价模型。     

Teaching adolescents safe driving and passenger behaviors: effectiveness of the You Hold the Key Teen Driving Countermeasure

PROBLEM: Unsafe driving and passenger behaviors place teens at increased risk for fatal and nonfatal crashes. This study evaluated the short-and long-term efficacy of the You Hold the Key (YHTK) Teen Driving Countermeasure. METHOD: A two-page survey was completed by high school students at pretest, posttest, and long-term (6-month) posttest. RESULTS: YHTK was associated with significant immediate and long-term improvements in teen seatbelt use, safe driving, and perceived confidence in preventing drunk driving. Compared to pretests, students at immediate and long-term posttest more frequently wore seatbelts when driving or riding, required passengers to wear seatbelts, and limited the number of passengers to the number of seatbelts in the vehicle. Students were more likely at both posttests to avoid drinking and driving and to say no to riding with a friend who had been drinking. SUMMARY: YHTK was associated with increases in safe teen driving and passenger behaviors. IMPACT ON INDUSTRY: Success of YHTK is most notably due to its comprehensive nature. Future programs should consider comprehensive strategies when attempting to modify teen behaviors.     

基于情感思维的安全管理模式研究

为促进安全管理,提高效率与效果,对基于情感思维的安全管理进行研究,将情感思维融于安全管理,基于情感思维的定义、内涵及功能,具体分析安全管理中情感思维的功能及其功能综合作用,建立安全管理中情感思维功能作用理论模型;根据该理论模型分析情感思维驱动力及强化作用及其对安全目标实现的影响;建立基于情感思维的安全管理设计模式,最后提出优化安全管理的措施与方法,以促进安全管理的实施。研究表明:基于情感思维的安全管理研究,能较好地体现安全管理人本原理,发挥情感的能动作用,将理性融入其中与情感进行平衡,实现安全管理的优化。     

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.

Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.

Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.

Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.     

Are older drivers’ driving patterns during an on-road driving task representative of their real-world driving patterns?

Abstract

Objective: The current study investigated whether older drivers’ driving patterns during a customized on-road driving task were representative of their real-world driving patterns.

Methods: Two hundred and eight participants (male: 68.80%; mean age?=?81.52 years, SD?=?3.37 years, range?=?76.00–96.00 years) completed a customized on-road driving task that commenced from their home and was conducted in their own vehicle. Participants’ real-world driving patterns for the preceding 4-month period were also collected via an in-car recording device (ICRD) that was installed in each participant’s vehicle.

Results: During the 4-month period prior to completing the on-road driving task, participants’ median real-world driving trip distance was 2.66?km (interquartile range [IQR]?=?1.14–5.79?km) and their median on-road driving task trip distance was 4.41?km (IQR?=?2.83–6.35?km). Most participants’ on-road driving task trip distances were classified as representative of their real-world driving trip distances (95.2%, n?=?198).

Conclusions: These findings suggest that most older drivers were able to devise a driving route that was representative of their real-world driving trip distance. Future research will examine whether additional aspects of the on-road driving task (e.g., average speed, proportion of trips in different speed zones) are representative of participants’ real-world driving patterns.