汉巴南铁路兰渝联络线电分相设置方案研究

联络线电分相设置方案影响因素众多,且其设置与否对工程和运营的影响也较大。本文对汉巴南铁路兰渝联络线电分相设置方案进行研究,提出高速铁路设置关节式电分相联络线最短长度;对联络线电分相不同设置方案进行比选研究,并结合各方案的土建工程、信号系统、供电系统及限制因素提出布局优化措施,以期为联络线电分相合理设置与优化布局提供新思路。     

重载货车摩擦缓冲器阻抗特性的数值模拟

为预测摩擦缓冲器的实际工作状态,从几何特征和作用原理的角度,建立详细的MT-2型缓冲器理论模型。首先,通过对缓冲器内部各摩擦元件的运动学和静力学分析,推导出缓冲器在准静态下的阻抗特性;其次,引入附加摩擦系数量化各摩擦元件之间动静态摩擦过渡时的黏滞补偿,并模拟出缓冲器在动态下的阻抗特性;最后,利用C80型货车冲击试验数据对该缓冲器的理论模型进行验证。验证结果表明:总体上,数值模拟和现场试验下的缓冲器示功曲线基本吻合,说明模型的正确性;局部上数值模拟中缓冲器从加载Ⅰ阶段过渡至加载Ⅱ阶段的突变现象在冲击试验中表现的并不明显,还有待进一步完善。     

Does transport help people to gain employment? A systematic review and meta-analysis of the empirical evidence

ABSTRACT

The role of transport in providing access to employment has received considerable attention. Since transport policies may be motivated by assumed effects on employment probability outcomes, it is important to establish the nature of the relationship between transport and employment outcomes. While the majority of the empirical evidence suggests a positive association, it is not conclusive or consistent and often shows mixed results. To address this confusion, our study has systematically reviewed this evidence base and synthesised it through meta-analysis. We first identified 93 studies that quantitatively assessed the impact of transport on employment outcomes. By systematically merging the empirical evidence, this study establishes a positive association between transport and employment outcomes, with varying effects for four identified categories of transport measures (or combinations thereof): car ownership, public transport access, commute times, and job accessibility levels. This positive association persists in studies that control for endogeneity between transport and employment, but a larger evidence base is needed to establish a more robust relationship, in particular for cities and smaller (rural) areas outside the US-context and with regard to public transport. We then selected 20 methodologically comparable studies for inclusion in the meta-analysis. Our meta-regression models clearly demonstrate that car ownership significantly increases individual employment probabilities, in particular among welfare recipients. Young drivers benefit from access to household cars when these are not in use by their parents, and they are more sensitive to the time and cost implications of longer commutes. While our systematic review suggests that better access to public transport and higher levels of job accessibility increases employment probabilities, meta-regression analysis requires more consistent transport measures. The findings in this study are important for policymakers in that they imply that job seekers may benefit from public policies targeted at improving their access to public transport, in particular for people without access to cars and in areas with fewer job opportunities.     

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SSRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

基于PLC的模糊频载调节器设计

介绍可编程序控制器(PLC)实现船舶电站模糊调频调载的软硬件实现方法,采用离线计算、在线查表的方法在PLC上实现模糊控制,利用硬件在环仿真进行试验研究,结果表明,智能频载控制器具有较好的稳、动态性能。     

柴油机减压-排气复合缓速匹配试验研究

通过对柴油机独立减压缓速、排气缓速性能进行研究,获取了二者在循环工作过程中的作用区域和性能提升的影响因素,并以此为基础,组合二者最优方案形成了复合缓速系统,进一步分析独立缓速性能改进方法在复合缓速中的适用性,通过试验验证了改进策略的有效性。研究结果表明,复合缓速是一种有效的辅助缓速装置,合理匹配后可在柴油机运转转速范围内获得与柴油机标定功率相等的制动功率,同时柴油机排温仍然可控制在限值范围内。     

QoS awared power and admission controls based on non-cooperative game theory in wireless networks

In order to better accommodate heterogeneous quality of service (QoS) in wireless networks, an algorithm called QeS-aware power and admission controls (QAPAC) is proposed. The system is modeled as u non- cooperative game where the users adjust their transmit powers to maximize the utility, thus restraining the interferences. By using adaptive utility functions and tunable pricing parameters according to QoS levels, this algorithm can well meet different QoS reqniremcnts and improve system capacity compared with those that ignore the QoS differ- ences.     

也论柴油轿车在国内市场的发展前景

研究了世界范围内柴油机在乘用车领域内的应用现状,阐述了柴油机作为车用动力的优势。根据国内的政策环境与市场环境,分析了排放法规、柴油品质对市场的影响。根据对柴油轿车的性能要求,包括振动和噪声、排放等,分析了更适合我国轿车柴油机发展的技术路线,并重点对若干关键技术和难点进行了剖析。柴油机技术进步和市场化应用表明,柴油轿车在我国具有广阔的应用前景,政府和企业应做好政策和技术上的准备。     

基于北斗导航定位系统的机车动力资源定位系统研究

针对当前铁路局机车定位管理技术现状,提出了基于北斗导航定位系统的机车动力资源定位系统体系结构、基于GIS的实时显示机车位置坐标点与铁路电子地图上铁路标记点路段匹配及盲区数据融合位置推算算法的解决理论与方案;现场试验验证表明,所提出的基于北斗Ⅰ+Ⅱ机车载终端机系统结构和解决方案是可行的.