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.     

The role of transportation sectors in the Korean national economy: An input-output analysis

The transportation industry has been playing an important role in the economic development of Korea and, thus, has become a critical factor in sustaining the well-being of the Korean people. This paper attempts to analyze the economic impacts of four transportation modes using input-output (I-O) analysis, with specific application to Korea. To this end, we apply the I-O models to the Korean I-O tables generated by the Bank of Korea, paying particular attention to the four transportation sectors in Korea (rail, road, water, and air transportations), considering them as exogenous, and then determining their impacts. Specifically, the production-inducing effects, supply shortage effects, sectoral price effects, forward linkage effects, and backward linkage effects of the four transportation modes are quantitatively derived over the period 2000–2010. For example, the production-inducing effect of a KRW 1.0 production or investment in transportation is larger in the petroleum and transportation equipment sectors than in other sectors. Furthermore, the rail and road transportation sectors have greater supply shortage effects than the other transportation sectors. Finally, the potential uses of the results of this analysis are presented from the perspective of policy instruments, and policy implications are discussed.     

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.     

Environmental invitingness for transport-related cycling in middle-aged adults: A proof of concept study using photographs

IntroductionCurrent evidence on associations between modifiable environmental characteristics and transport-related cycling remains inconsistent. Most studies on these associations used questionnaires to determine environmental perceptions, but such tools may be subject to bias due to unreliable recall. Moreover, questionnaires only measure separate environmental characteristics, while real environments are a combination of different characteristics. To overcome these limitations, the present proof of concept study used panoramic photographs of cycling environments to capture direct responses to the physical environment. We examined which depicted environmental characteristics were associated to environments’ invitingness for transportation cycling. Furthermore, interactions with gender and participants’ cycling behavior were examined.MethodsFifty-nine middle-aged adults were recruited through purposeful convenience sampling. During a home visit, participants took part in a structured interview assessing demographics and PA during the preceding seven days, followed by an intuitive choice task and a (cognitive) rating task, which both measured 40 photographed environments’ invitingness to cycle along. Multi-level cross-classified analyses were conducted using MLwiN 2.26.ResultsBoth tasks’ multivariate results showed that presence of vegetation was identified as the most important environmental characteristic to invite people for engaging in transportation cycling, even when the amount of vegetation was relatively small. In the bivariate analyzes, some differences between results of the cognitive rating task and the intuitive choice task were found, showing that invitingness measured by the rating task was associated with environmental maintenance and cycling infrastructure, whereas invitingness determined by the choice task was associated with more traffic-oriented characteristics. Moreover, only for the choice task’s results, moderating effects of gender and participants’ cycling behavior in the preceding week were observed.ConclusionThe present study provides proof of concept that capturing people’s less cognitive, more intuitive responses to an environment’s invitingness for transport-related cycling may be important for revealing environment-behavior associations. If replicated in future studies using larger samples, results of our innovative measurements with photographs, especially those on vegetation, can complete the existing knowledge on which environmental characteristics are important for transportation cycling in adults and could form a basis to inform health promoters and local policy makers. However, future studies replicating our study method in larger samples and other population subgroups are highly encouraged. Moreover, causal relationships should be explored.     

集装箱多式联运公共信息平台建设研究

针对我国集装箱多式联运发展滞后,铁路、公路、航空、水运等各为独立系统,条块分割,导致各种运输方式之间缺乏紧密衔接和有效联动的实际,研究建立以集装箱铁路运输为骨干的、有机整合各种运输方式的集装箱多式联运公共信息平台。通过信息技术创新带动机制创新,通过信息化对物流、信息流、资金流进行有效整合,大力发展集装箱多式联运,促进我国物流业整体发展水平的提升。     

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。     

Public perceptions of the use of dynamic message signs

Intelligent transportation systems have been promoted as a means to improve both the efficiency and safety of the road network. The effectiveness of advanced technologies in improving road safety has been an area of research which has thus far yielded mixed results. In order to ensure that advanced technologies deliver on their intended outcomes, more research has to be devoted to understanding road users' perceptions and reactions to these systems. This study examines drivers' perceptions of the use of dynamic message signs and their self‐reported reactions to the messages displayed. In general, drivers support the use of highway electronic boards for traffic incident reports and weather information which have an impact on traffic delays and level of service. They also think that it is a good idea to display road safety messages and to remind drivers to drive safely and be courteous on the roads. Moreover, most drivers reported that they do read and think about the messages displayed and react positively to some of the road safety messages.     

Energy-aware load-balaning fault-tolerant routing scheme for ad hoc sensor networks

Most routing protocols for sensor networks try to extend network lifetime by minimizing the energy consumption, but have not taken the network reliability into account. An energy-aware, load-balancing and fault-tolerant routing scheme, termed as ELFR was propsed to adapt to the harsh environment. First a network robustness model was presented. Based on this model, the route discovery phase was designed to make the sensors to construct into a hop-leveled network which is mesh structure. A cross-layer design was adopted to measure the transmission delay so as to detect the failed nodes. The routing scheme works with acknowledge （ACK） feedback mechanism to transfer control messages to avoid producing extra control overhead messages. When nodes fail, the new healthy paths will be selected locally without rerouting. Simulation results show that our scheme is much robust, and it achieves better energy efficiency, load balancing and maintains good end-to-end delay.     

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鉴于模糊神经网络具有良好的非线性特性、学习能力、自适应能力和抗干扰能力,本文将模糊神经网络技术引入到高速公路入口匝道控制中。提出一种基于GA和BP算法的模糊神经网络控制器,并对控制器进行了详细设计。设计过程主要分为三部分：输入输出参数的选择、模糊神经网络的结构设计以及基于GA-BP的学习算法设计。最后,使用MATLAB软件对其进行了仿真。仿真结果表明,本文提出的方法是有效的,较之基于BP的模糊神经网络控制和ALINEA控制,能更好地稳定主线交通流密度。