复杂地基条件下轨道基础的研究及应用

重庆主城港区东港作业区一期工程陆域场区采用开挖和回填形成,最大回填厚度达32 m,场地地基条件复杂。堆场轨道基础在回填区域采用现浇U型轨道板基础,通过调整轨枕满足使用要求;在开挖区域采用无渣轨道板基础,使门 机运行稳定,节省投资。     

综合港区集疏运系统路网结构建模与分析*

基于复杂系统理论的网络分析方法,建立了港口集疏运道路系统的网络模型,依据路网结构的拓扑关系图,分析网络节点的度分布等结构参数,结合实际案例对综合港区的集疏运系统路网结构进行仿真试验研究.研究成果对于港口的规划设计具有参考价值.     

适用于模拟复杂地质条件的微型TBM试验系统研制及应用

为研究TBM掘进隧道复杂地质的演化过程及破坏特征,研制了适用于复杂地质条件的微型TBM模型试验系统,主要由微型掘进装置、多功能岩箱、微型掘进机工位平移装置、四联液压系统以及微型掘进机掘进控制系统组成。该系统可实现推进速度0~50 mm/min可调、刀盘转速0~10 r/min可调、刀盘最大转矩可达1 000 N·m、刀盘最大安全掘进距离可达1 100 mm; 可以进行半断面可视化掘进和全断面高地应力模拟掘进,并提前了解TBM掘进复杂地质时隧道应力的变化规律,为现场高地应力大埋深复杂地质下TBM掘进提供可靠的参考资料。     

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.     

复杂环境下基坑上跨运营地铁隧道的保护方案研究

为解决复杂环境下基坑开挖时下方地铁隧道正常运营的难题，依托郑州某市政管廊上跨地铁区间隧道项目，采用三维数值模拟计算及施工监测数据分析的方法。得出如下结论： 1）通过选取合理的基坑围护方案，可减小基坑围护结构施工对地铁区间隧道的扰动影响; 2）对于工程地质情况较好的地区通过细化上跨基坑开挖方式，采用基底加固+抽条施工的方案可保证地铁区间隧道的正常运营。     

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.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

基于复杂网络的机场群航线网络动态特征分析

随着航空运输和区域经济一体化的快速发展,我国已形成京津冀、长三角和珠三角三大典型机场群。文章以三大典型机场群为研究对象,构建基于滑动时间窗的航线网络拓扑结构,运用复杂网络理论分析机场群航线网络动态特征和同构性。研究结果表明,机场群航线网络具有无标度和小世界网络特征,并表现出显著的波峰和波谷动态性,并且长三角机场群的航班集中度分布明显高于其他两大机场群。文章首次从航线网络视角,运用Dice相似系数衡量了机场群内部各机场之间航线网络同构性程度。     

融合D-S理论的锅炉人因贝叶斯网络安全性定量分析

针对船用锅炉人因安全性分析中存在的知识不确定性,采用D-S证据理论对多专家信息进行融合并建立考虑人因的贝叶斯网络,得到节点条件概率的区间表示形式.经加权平均后代入贝叶斯网络计算,与面向对象贝叶斯网络和FTA等方法的对比显示,该方法能够更加有效地融合不同专家信息,也更为符合工程实际.     

Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses

Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.