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.     

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.     

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.     

Elman递归神经网络在结构分析中的应用

给出了Elman动态递归神经网络的网络结构和基本原理。基于Elman递归神经网络能够逼近任意非线性函数的特点,提出了一种基于Elman递归神经网络建立结构分析模型的方法。利用Elman递归神经网络对桁架进行建模,真实地反映了桁架结构的动态特性。     

有限元分析中三维体元和梁元的非节点连接

在对三维体元任一点移动和转动分析的基础上,导出了体元和梁元恰当连接应满足的多点约束方程,通过直接代入梁元的单元分析进行自由度替换实现了体梁的良好连接.本文方法对体元和梁元的连接形式具有较强的适应能力,应用方便,算例表明具有较高的数值精度.     

An Intelligent Response Surface Methodology for Modeling of Domain Level Constraints

An effective modeling method of domain level constraints in the constraint network for concurrent engineering （CE） was developed. The domain level constraints were analyzed and the framework of modeling of domain level constraints based on simulation and approximate technology was given. An intelligent response surface methodology （IRSM） was proposed, in which artificial intelligence technologies are introduced into the optimization process. The design of crank and connecting rod in the V6 engine as example was given to show the validity of the modeling method.     

The effect of plastic constraint on the initiation of ductile tears in shipbuilding structural steels

In this paper, the effect of plastic constraint on the initiation of ductile tears in four different shipbuilding structural steels has been experimentally studied by measuring the J-integral and crack opening displacement COD at initiation in three-point bend specimens with deep and shallow notches. Experimental results of seven groups of different strength alloy steels show that both Si and Ji values of ductile tear from the shallow crack specimens which have less constraint flow field are significantly higher than those of deeply notched specimens. Slip-line-field analysis shows that, for shallow crack, the hydrostatic stress is lower than that from standard deeply cracked bend specimen, which develops a high level of crack tip constraint, provides a lower bound estimate of toughness, and will ensure an unduly conservative approach when applied to structural defects, especially if initiation values of COD and J-integral are used.     

周期运行图编制模型与算法研究

在周期运行的运输组织模式下,所有列车在车站到发都是周期循环发生的。将安排列车运行线的问题看作周期事件安排问题,并借助周期约束图及周期势差模型,可以建立周期运行图网络模型。模型充分考虑到列车不同情况下的停站时间、到发安全间隔等各项周期约束,并将列车的总停留时间最小作为目标函数。当约束图顶点和弧的数量众多时,模型的求解将比较困难。通过选择合适的约束图生成树,找到变量的合理取值范围,并对模型进行一些预先简化处理,可以降低模型的求解难度。最后求解一个区段不同列车开行方案的周期运行图,验证模型的可行性。