城乡结合部交通与枢纽规划研究

文章从城乡结合部和交通与枢纽的基本内涵出发,指出了城乡结合部存在的一些交通问题,同时针对城乡结合部已成为交通瓶颈,阻碍城市的对外经济联系和乡村经济发展等问题,提出了结合部交通与枢纽规划的优化设想。     

大城市中心区实施拥堵收费的影响评价研究

文章在分析大城市中心区拥堵收费实施条件与方式的基础上,定性分析实施拥堵收费对居民出行行为以及中心区动静态交通的影响,最后提出基于模糊评价理论的中心区拥堵收费影响评价模型.     

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.     

山区高速公路隧道典型安全因素分析及其对策研究

文章针对西部多条山区高速公路隧道运营安全情况,分析山区高速公路隧道的典型安全事故及原因,提出了基于车速限制、车道限制、照明控制与交通安全设施的安全对策,为山区高速公路隧道的安全运营提供参考。     

寒冷地区沥青混合料弯拉特性分析

文章针对寒冷地区气侯的特殊性,运用沥青混合料弯曲试验方法,分析了温度、公称最大粒径、油石比、沥青种类等因素对寒冷地区沥青混合料弯拉特性的影响及作用规律。研究结果表明：混合料油石比在6.0%～7.0%范围内取值较适宜;SBR改性沥青混合料低温性能优势显著;多年冻土地区路面设计应该重视-10℃下沥青混合料的弯曲特性,提高沥青混合料的低温性能。     

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.     

采空区地层变形产生的隧道附加荷载及变形容许值计算与分析

文章分别计算了采空区上方地层弯曲时对隧道产生的附加荷载q1、q2及地层水平应变时对隧道产生的附加荷载qH,并推导出了采空区地层容许弯曲变形和水平应变数值.结果表明,现行交通部《采空区公路设计与施工技术细则》规定的采空区地基容许变形值较为笼统且偏大,结构偏于不安全.最后,文章根据计算结果对比,对不同围岩级别提出了更严格、更安全、更有针对性和可操作性的隧道对采空区地层变形限制要求,以供广大隧道工作者参考使用.     

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.