无信控路段人行横道处行人过街建模与仿真

为精细化描述无信控路段人行横道处行人过街决策及微观运动行为,本文在行人过街行为分析的基础上,基于战术层-操作层构建行人过街双层模型。其中,在战术层面,针对行人分步决策特性,利用二元Logistic回归模型构建路侧和路中行人过街决策模型;在操作层面,针对传统社会力模型在描述行人过街时表现出的局限性,引入行人主动避让力和人行横道对行人的作用力,构建行人过街微观运动模型。最后,通过AnyLogic仿真平台实现行人过街双层模型仿真,并根据实际数据和仿真结果分析模型有效性。结果表明：相较于路侧决策模型,行人性别、停车视距内的车辆数和行人距潜在冲突点的距离这3个因素对行人路中决策结果影响更显著,引入行人主动避让力和人行横道对行人作用力的改进社会力模型,能更好地反映无信控路段行人过街时的行为特征。无信控路段人行横道处行人过街行为仿真模型的有效建立,能为后续行人过街安全改善方案提供决策支持。     

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.     

就地冷再生技术在高速公路养护工程中的应用研究

就地冷再生技术具有施工便捷、污染小、旧料回收利用率高、节约成本等优势,在公路养护中得到了利用.以S49新扬高速为例,从适用条件、混合料设计、路用性能验证以及社会经济效益等方面,对就地冷再生技术进行系统研究,结果表明就地冷再生技术在高速公路养护工程中应用效果理想,可进行推广运用.     

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.     

Managing corporate social responsibility strategies of airports: The case of Taiwan’s Taoyuan International Airport Corporation

Airports need to manage corporate social responsibility (CSR) strategies for sustainable development. This paper develops a new structured approach for airports to evaluate, prioritize and categorize CSR strategies, using Taiwan’s Taoyuan International Airport Corporation (TIAC) as an example. Based on TIAC’s CSR-related activities, 18 CSR strategies grouped into 5 CSR goals (corporate governance and finance, green airport and environmental management, service quality and social relationship, employee and work environment management, and safety and security) are identified using the CSR value chain and diamond framework. The pairwise comparison method used in analytic hierarchy process and the decision-making trial and evaluation laboratory method are used respectively to evaluate the relative importance, feasibility and achievability of these 18 strategies and to analyze their causal relationships via expert questionnaire surveys. A new method is developed to plan and manage the implementation of CSR strategies by incorporating the viewpoints of both internal and external stakeholders, thus reflecting the practical effects and strategic implications of the CSR implementation. The result suggests that TIAC’s CSR strategies in relation to airport safety and security, service quality and corporate governance are most significant and have a high implementation priority. This paper contributes to the airport industry and CSR research by proposing a proactive mechanism for quantitatively evaluating, prioritizing and categorizing CSR strategies.     

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。     

土木规划的规划环境与规划政策研究

研究目的：土木规划是以社会公共基础设施为对象，本着统筹、系统的思想，利用土木工程技术与系统科学方法进行的规划。但就我国目前土木规划领域的现状来看，无论是在“规划环境”方面还是“规划政策”方面，都存在着一定的问题。本文的目的在于通过对我国土木规划的规划环境与规划政策的研究，促进土木规划在我国的科学化、体系化发展。研究方法：本文聚焦于我国的交通基础设施，系统地整理和分析了我国土木规划的历史与现状，包括政府与市场的角色定位、规划体制、法律法规的情况，同时介绍了国外的经验，揭示了存在的问题。研究结果：提出我国要走科学的“土木规划”道路，在规划环境上应改革规划体制，完善立法，建立多层次的基础设施体系；在规划政策上应努力拓宽资金渠道，加强综合规划等。研究结论：揭示了在我国开展土木规划研究的重要性和必要性，土木规划相关研究的不断深入，必将促进我国的土木规划水平的提高。