试论中国经济转型与社会稳定

我国处在社会经济转型与持续调整增长阶段，社会非稳定因素与日俱增，社会经济转型给整个社会带来进步与繁荣，也给社会带来冲突与动荡，但是，中国的社会经济顺利转型又必须在长期稳定的社会环境中才能进行，因此，保持政治稳定，社会安定是实现社会经济转型的前提。     

基于融合技术的计算机网络安全

网络攻击手段的多样化，不仅给网络安全与管理提出了新的挑战，而且对网络创新安全理论，整合各种安全解决方案，构建综合的动态网络提出了更高的要求。重点讨论了不同安全产品的融合和安全产品与网络设备的融合。     

轨道交通闭路电视监视系统图像高清化特点

阐述轨道交通闭路电视监视系统方案的演 变,以及高清标准、编码方式和系统结构,阐明轨道交 通闭路电视监视系统网络带宽、存储容量、存储方式、 显示格式、显示距离、摄像机接入方式、硬件要求、软件 要求、系统网络化等特点,对图像高清化引起的一系列 问题进行论述,针对每个特点进行分析,特别对摄像机 接入方式及软件要求进行深入探讨。     

1885-1945年缅甸闽籍华侨的社会结构

早期缅甸闽侨以年轻男子居多,由于华侨社会内部性别比失衡,他们大多与当地人通婚.1885年后缅甸全境沦为英国殖民地,仰曼铁路向北延伸,缅甸闽侨的地域结构特征逐渐由"二元"转为"一体".同期,缅甸闽侨大多经商为生,并创立了各类社团组织.1930年后英属缅甸社会动荡不安,但远离当地政治的闽侨所受影响较小.第一代闽侨总体文化水平不高,但大多以自身中华文化特征为荣.     

基于专利数据的城市轨道交通技术发展现状与趋势分析

城市轨道交通是城市公共交通的重要组成部分,基于专利数据的城市轨道交通技术分析旨在发现城市轨道交通技术的发展现状与趋势,为城市轨道交通行业发展提供导向参考,为该领域研究人员提供情报支撑。基于世界知识产权组织的国内专利数据,综合利用专利计量、文本挖掘、关键词共现网络、专利地图方法,对国内城市轨道交通专利进行分析,结果表明国内城市轨道交通正处于稳中有升的成长期。     

轨道交通大客流换乘站客流组织仿真分析——以深圳会展中心站为例

为有效应对轨道交通网络化运营初期可能出现的客流拥挤等问题,运用行人仿真方法进行客流组织方案的动态检验和调整。以深圳会展中心站为例,选取适当的仿真参数和评价指标,对车站客流组织进行防真,分析客流拥堵原因,提出客流组织调整方案,并对调整方案进行仿真及评阶。总结了车站行人仿真在指导车站客流组织方面的普遍适用性。     

从运营角度分析城市轨道交通建设阶段需重视的问题

分析了城市轨道交通网络化运营面临的主要问题,从运营角度提出了建设阶段必须关注的总体设计、标准化设计、国产化和信息化等问题。分析了忽视这些问题对网络化运营的安全性、可靠性和效率的影响,阐述了总体设计、标准化设计、国产化设计和信息化设计在建设阶段应关注的重点。     

Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations

On the grounds that individuals heavily rely on the information that they receive from their peers when evaluating adoption of a radical innovation, this paper proposes a new approach to forecast long-term adoption of connected autonomous vehicles (CAVs). The concept of resistance is employed to explain why individuals typically tend to defer the adoption of an innovation. We assume that there exists a social network among individuals through which they communicate based on certain frequencies. In addition, individuals can be subject to media advertisement based on certain frequencies. An individual’s perceptions are dynamic and change over time as the individual is exposed to advertisement and communicates with satisfied and dissatisfied adopters. We also explicitly allow willingness-to-pay (WTP) to change as a result of peer-to-peer communication. An individual decides to adopt when (i) there is a need for a new vehicles; (ii) his/her WTP is greater than CAV price; and (iii) his/her overall impression about CAVs reaches a cutoff value. Applicability of the proposed approach is shown using a survey of employees of the University of Memphis. Our results show that the automobile fleet will be near homogenous in about 2050 only if CAV prices decrease at an annual rate of 15% or 20%. We find that a 6-month pre-introduction marketing campaign may have no significant impact on adoption trend. Marketing is shown to ignite CAV diffusion but its effect is capped. CAV market share will be close to 100% only if all adopters are satisfied with their purchases; therefore, the probability that an individual becomes a satisfied adopter plays an important role in the trend of adoption. The effect of the latter probability is more pronounced as time goes by and is also more prominent when CAV price reduces at greater rates. Some caveats may be inserted when considering the study results as the findings are subject to sample bias and data limitations.     

Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.