Dynamic model for ride comfort evaluations of the rubber-tired light rail vehicle

The dynamic model was developed to evaluate vibration accelerations and ride comforts during the running of the Korean-standardised rubber-tired light rail vehicle. Ride comfort indexes were analysed and tested in accordance with UIC 513R by using the dynamic model and the actual vehicle in the test track. Based on the comparisons between analysis results and test results, the validity of the developed dynamic model was evaluated. It was verified whether or not the developed Korean-standardised rubber-tired light rail vehicle met the specified target specification on ride comfort. In addition, the influence of the wearing of guide wheels on ride comfort was estimated.     

高速公路左侧路肩设置必要性研究

尽管<公路工程技术标准>(JTG B01-2003)中规定八车道的高速公路应当设置2.5 m的左侧硬路肩,但国内几条刚刚改建完成的八车道高速公路中没有一条设置了左侧硬路肩,甚至没有宽于0.75 m的路缘带.研究运用可接受间隙理论计算了不同流量和车道数条件下车辆从最内侧车道穿插变道停驶到右侧路肩这一过程的概率和所需距离,并通过交通事故数据调研,分析了左侧路肩设置对提高交通安全的影响,从运行效率和交通安全两个方面定性和定量分析了左侧路肩设置的必要性.     

车用发动机扭转振动的分析与控制

基于扭转振动的基本原理,对发动机两种类型的扭转振动减振器的设计计算做深入的陈述.结合两款有针对性的发动机,对所要计算的基本参数及该参数所要限定的范围作了具体说明.最后用本公司自主开发的发动机扭振分析软件对一款发动机进行模拟计算,并与试验测量结果进行对比分析,并证明计算的结果是准确可靠的.     

Investigation of the effects of sleeper-passing impacts on the high-speed train

The sleeper-passing impact has always been considered negligible in normal conditions, while the experimental data obtained from a High-speed train in a cold weather expressed significant sleeper-passing impacts on the axle box, bogie frame and car body. Therefore, in this study, a vertical coupled vehicle/track dynamic model was developed to investigate the sleeper-passing impacts and its effects on the dynamic performance of the high-speed train. In the model, the dynamic model of vehicle is established with 10 degrees of freedom. The track model is formulated with two rails supported on the discrete supports through the finite element method. The contact forces between the wheel and rail are estimated using the non-linear Hertz contact theory. The parametric studies are conducted to analyse effects of both the vehicle speeds and the discrete support stiffness on the sleeper-passing impacts. The results show that the sleeper-passing impacts become extremely significant with the increased support stiffness of track, especially when the frequencies of sleeper-passing impacts approach to the resonance frequencies of wheel/track system. The damping of primary suspension can effectively lower the magnitude of impacts in the resonance speed ranges, but has little effect on other speed ranges. Finally, a more comprehensively coupled vehicle/track dynamic model integrating with a flexible wheel set is developed to discuss the sleeper-passing-induced flexible vibration of wheel set.     

Options for reducing noise from roads and railway lines

The fundamental noise generation mechanisms of road and rail vehicles are discussed with attention to noise abatement measures. Based on an evaluation of publicly available tire noise data and the European road traffic noise emission model CNOSSOS, it is shown that on the road side there is a significant noise reduction potential in the usage of low-noise tires. From a three months measurement campaign a noise model was derived to predict the maximal sound pressure level of heavy duty vehicles during a pass-by in 7.5 m distance with the parameters vehicle speed and number of axles. With help of recently published information about external costs caused by heavy duty vehicles and the noise prediction tool, a model was developed to derive a money equivalent that can be used as a bonus/malus in a heavy duty vehicle fee. As a measure at the infrastructure, the installation of low-noise pavements is an effective, durable and economically attractive measure. Recent experiences with different technologies from all over the world are compiled and evaluated. On the rail side, an overview of the possible noise reduction strategies is given, followed by a discussion of the current policy and legislation in the EU and on the national level of different European countries.     

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.     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.     

An efficient and exact event-based algorithm for solving simplified first order dynamic network loading problems in continuous time

In this paper a novel solution algorithm is proposed for exactly solving simplified first order dynamic network loading (DNL) problems for any generalised network. This DNL solution algorithm, termed eLTM (event-based Link Transmission Model), is based on the seminal Lighthill–Witham–Richards (LWR) model, adopts a triangular fundamental diagram and includes a generalised first order node model formulation. Unlike virtually all DNL solution algorithms, eLTM does not rely on time discretisation, but instead adopts an event based approach. The main advantage of this approach is the possibility of yielding exact results. Furthermore, an approximate version of the same algorithm is introduced. The user can configure an a-priori threshold that dictates the approximation error (measurable a-posteriori). Using this approximation the computational effort required decreases significantly, making it especially suitable for large scale applications. The computational complexity is investigated and results are demonstrated via theoretical and real world case studies. Fixed periods of stationary demands are included adopting a matrix demand profile to mimic basic departure time demand fluctuations. Finally, the information loss of the approximate solution is assessed under different configurations.