Modeling traffic operation at signalized intersections without explicit left‐turn yielding rules with an enhanced cell transmission model

This paper presents an enhanced cell transmission model (CTM) to capture traffic operation at signalized intersections without explicit permissive left‐turn yielding rules (i.e. aggressive permissive left‐turn maneuvers may not necessarily yield to opposing through traffic), which can be widely observed in many developing countries. Different from previous studies that focus on traffic dynamics on approaching links, this study contributes to modeling traffic operations within the intersection. A novel cell transmission framework with various types of virtual cells is proposed to model the dynamics of traffic movements from approach to exit. The unique phenomenon of competitive occupying of the conflict point between the left turn and opposing through movements is modeled. The cell state indicating its blockage is proposed to capture the dynamic queue formulation and dissipation and to evaluate the operational traffic performance at the intersection. Field validation results show that the proposed model can capture the operation of traffic at signalized intersections without explicit permissive left‐turn yielding rules with significantly higher level of accuracy than traditional traffic flow models. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Transport resilience and vulnerability: The role of connectivity

This paper aims to adopt a critical stance on the relevance and interpretation of the recently emerging concepts of resilience and vulnerability in transportation studies. It makes a clear distinction between engineering and ecological interpretations of these concepts and offers a systematic typology of various studies in this field. A core element in the study is the linkage between the aforementioned concepts and connectivity/accessibility in transport networks. The methodological findings in the study are put in perspective by addressing also such concepts as robustness, reliability and friability of transport systems.     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.     

威海市公路网发展规模预测及适应性评价

通过对威海市现有公路网总体布局、公路等级结构分析，在充分考虑威海市制造业基地的发展基础上，对社会经济发展水平进行预测，同时分析了公路网规模与国民经济、人口的相互关系，采用国土系数法、人口密度法定量预测未来规划年的威海市公路网合理规模，结合专家意见等定性：方法，给出了满足威海市发展需求的路网规模，并从公路网技术指标，宏观经济和社会效益等方面：鞋行适应性分析评判。     

直接甲醇燃料电池在汽车上的应用前景分析

介绍了直接甲醇燃料电池的结构、工作原理和特性,对影响其应用于汽车的两个主要因素———燃料转换效率和功率密度进行了详细地分析,对目前面临的主要技术难题和解决方法进行了阐述,如催化剂和甲醇渗透等,最后得出了一些相应的结论。     

基于人工神经网络的柴油机故障诊断

故障诊断是计算机模式识别领域的一个活跃课题。文中提出了基于人工神经网络的柴油机故障诊断方法，设计了适合该诊断系统的BP网络结构，并给出了一种基于黄金分割法改进的BP算法，用来自适应调整网络学习速率。仿真结果表明：该算法具有很快的学习速度和较高的学习精度，完全适用于柴油机故障诊断系统。     

质子交换膜燃料电池车载系统智能控制算法研究

介绍了质子交换膜燃料电池(PEMFC)车载系统的智能控制算法。由于汽车行驶过程中对功率需求的随机性,使得该系统成为一个变输入变输出系统,对控制的实时性有较高的要求。为此,该系统引入仿人智能控制,并取得了良好的控制效果。     

基于加权Kendall相关系数和序贯代理模型的有限元模型修正

为了提高桥梁结构有限元模型修正的效率和效果,提出基于加权Kendall相关系数和序贯代理模型的有限元模型修正方法.首先,建立基于目标响应误差和待修正设计参数灵敏度的加权Kendall相关系数指标,并采用凝聚层次聚类算法,合理确定待修正设计参数的数量和位置,保证待修正设计参数对目标响应具有合适的解耦能力;其次,为解决传统...