大风室接力通风在长斜井隧道施工中的应用

为提高隧道施工通风效果,改善隧道施工环境,实现快速施工的目的,以西秦岭特长隧道斜井进双正洞无轨运输施工通风为例,从方案比选、通风设计、效果监测等方面系统阐述了罗家理斜井施工通风技术,并对大风室接力通风在罗家理斜井的成功应用进行详细说明。所采用的通风技术工艺简单、可靠、经济、高效,通风效果较好,有效地控制了空气中的尘毒污染,保证了施工的顺利、快速进行。     

工业无线遥控系统在天车上的应用

介绍一种无线遥控天车控制的硬件系统,整个系统电气线路接线简单、操作方便、系统稳定,在恶劣的环境中具有较高的应用价值。     

无标度网下传播拥塞量与时间关系的研究

文章以复杂网络知识为基础,通过对无标度网络及度分布平均场的介绍,利用网络中度与集群系数的联系,研究因特网信息包传播过程中的一般拥塞模型,并解析无标度网络中的拥塞量与时间的关系。这样就能够预测拥塞量,以便及时有效执行相关措施     

格形钢板桩抗倾覆稳定性计算新方法*

通过对现行主要格型钢板桩抗倾覆稳定性分析方法进行分析研究的基础上,提出了一种新的分析计算方法,即“修正分离法”.假设格内填料的自重不直接参与抗倾,而是通过它对格壁产生侧向土压力和摩擦力来体现其参与抗倾工作的机制和程度,从而对传统的TVA分离法进行修正,给出了更为优化的计算方法.并通过实例计算,与现行5种方法的计算结果进行比较,验证了该计算方法的合理性和实用性.     

舰船电力系统自适应保护技术研究

随着舰船电力系统的不断发展,系统配置、网络结构和运行模式等方面较传统舰船电力系统都发生了较大的改变,以离线整定为特征的三段式电流保护方法已不能满足要求.在全面分析传统继电保护特性的基础上,提出舰船电力系统自适应保护策略,利用网络结构和系统状态自动在线调节保护整定值,可以有效地提高保护的灵敏性和选择性.     

基于元胞自动机的高速公路瓶颈交通演化仿真

为分析高速公路中道路瓶颈造成的堵塞现象,本文改进KKW (Kerner-Klenov-Wolf) 模型, 建立跟驰规则;综合考虑车间距和车速对车辆换道的影响,建立自由换道和强制性换道规则;并对高速公路中不同车流量条件下,道路瓶颈上游的堵塞区域分布、换道行为特征和车道上交通参数的变化情况进行仿真研究。结果表明：在给定的交通量条件下,汇流车道的拥堵区域长度处于动态平衡状态,不会随时间而变化,且道路瓶颈前的汇流行为会导致目标车道上严重的速度下降,汇流车道和目标车道上车辆速度变化趋同;从换道集群特征来看,道路瓶颈前因高交通流量形成的低速汇流车辆倾向于以小集团的方式统一进行换道,造成目标车道上剧烈的交通震荡;瓶颈消失后,交通恢复时间随进口交通流量的上升而线性增长。     

基于PFV策略的连续型元胞自动机交通流模型

科学合理的微观交通流仿真模型是研究交通控制手段有效性的关键.本文结合一种考虑驾驶员视野内前后多车影响的跟驰策略，建立了连续型元胞自动机交通流模型.仿真实验表明，仿真数据与实测数据有较好的拟合性，仿真得到的 K-Q、K-V和 Q-V图能较好地反映实际道路交通流的失稳现象，所建立的模型具有适应不同场景的兼容性与灵活性，能够作为研究我国实际道路交通流问题的仿真工具.     

Asynchronous n-step Q-learning adaptive traffic signal control

Ensuring transportation systems are efficient is a priority for modern society. Intersection traffic signal control can be modeled as a sequential decision-making problem. To learn how to make the best decisions, we apply reinforcement learning techniques with function approximation to train an adaptive traffic signal controller. We use the asynchronous n-step Q-learning algorithm with a two hidden layer artificial neural network as our reinforcement learning agent. A dynamic, stochastic rush hour simulation is developed to test the agent’s performance. Compared against traditional loop detector actuated and linear Q-learning traffic signal control methods, our reinforcement learning model develops a superior control policy, reducing mean total delay by up 40% without compromising throughput. However, we find our proposed model slightly increases delay for left turning vehicles compared to the actuated controller, as a consequence of the reward function, highlighting the need for an appropriate reward function which truly develops the desired policy.     

Social networks and joint/solo activity–travel behavior

The social dimension of activity–travel behavior has recently received much research attention. This paper aims to make a contribution to this growing literature by investigating individuals’ engagements in joint activities and activity companion choices. Using activity–travel diary data collected in Hong Kong in 2010, this study examines the impact of social network attributes on the decisions between solo and joint activities, and for joint activities, the choices of companions. Chi-square difference tests are used to assess the importance of social network variables in explaining joint activity behavior. We find that the inclusion of social network attributes significantly improves the goodness-of-fit of the model with only socioeconomic variables. Specifically, individuals receiving emotional support and social companionship from family members/relatives are found to more likely undertake joint activities with their family members/relatives; the size of personal social networks is found to be a significant determinant of companion choices for joint activities; and activity companions are found to be significant determinants of travel companions. The findings of this study improve the understanding about activity–travel, especially joint activity–travel decisions.