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。     

加强领导落实责任认真做好青藏铁路建设用地管理工作

铁路建设用地验收交接的技术性、专业性、政策性较强,涉及面广。青藏铁路公司建设管理部采取“结合实际、突出重点、全面指导、整体推进”的做法,加强与地方政府土地管理部门的联系,参加建设用地的征用和施工用地的管理工作;主动与建设单位沟通,共商建设用地管理的对策;统一竣工资料的标准,保证竣工资料的齐全、规范;牢固树立为运输生产服务的理念,依法、规范、科学地管理铁路建设用地,保证建设用地交接完整、准确、规范地进行。     

铁道信号系统安全保障研究

在信号系统的设计、制造、施工、调试等阶段进行安全保障很重要。重点介绍按照欧洲铁路安全标准对铁道信号系统进行交叉接收以及安全管理。     

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.     

HX_D1B型机车车轴磁粉探伤工序验收

机车车轴磁粉探伤工序是制造过程中一个非常重要的工序,对该工序的验收是当前机车验收工作一项不可缺少的内容。文章详细介绍了HXD1B型机车车轴磁粉探伤工序的验收。     

关于铁路光传输工程色度色散指标的研究

针对铁路光传输系统,阐述了光纤色散指标对传输系统技术性能的影响,建议修改现行光缆线路工程验收标准的有关规定,增加光纤色度色散指标验收测试项目,有效保证高速铁路传输系统的质量要求。     

A cross-country study of fairness and infringement on freedom as determinants of car owners' acceptance of road pricing

Jakobsson et al. (2000) found that in Sweden public acceptance of road pricing decreases if it is perceived to be unfair and to infringe on freedom. The present study reports a survey investigating whether the same effects are found in the Asian countries of Japan and Taiwan. The results indicate that fairness plays the same role. However, income had a direct effect on acceptance in Taiwan but not in Japan or Sweden.     

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.