The rail vehicle industry wants to produce vehicles with higher speeds, to maintain and increase its market share. However, when the speed of the vehicle increases, it may have an undesirable effect on ride comfort, in terms of ride dynamics. Recent developments towards lighter and faster vehicles make the problem of ride comfort at higher speeds increasingly important. Focusing on the behavior of flexible rather than rigid body behavior should not be neglected when designing long and light car bodies. There are several approaches to incorporate body flexibility in multibody simulations and they have some superiorities and weaknesses. In this study, an efficient and accurate vertical dynamic model for the ride comfort analysis is developed and implemented in a commercial object-oriented modeling (OOM) software Dymola (2015 FD01) which uses the open-source code Modelica. This model includes car body flexibility with the assembling of a rigid body approach. The developed model is compared to a three-dimensional vehicle model in the commercial Vampire software (Pro V5.50) at different velocities. For the vertical ride comfort analysis, the ISO 2631-1 standard was used for both the developed model and the three-dimensional model. The results are presented as acceleration history and $a^{wrms}$—weighted r.m.s (root mean square) of accelerations—as required by the standard. The developed model has shown its feasibility in terms of its efficiency and accuracy for the vertical ride comfort analysis. The accuracy of the model is evidenced by the fact that the car body vibration level at high speeds shows minor differences compared to the results of the Vampire, which is a validated commercial software in the area of rail vehicle dynamics. The approach involving the assembly of rigid bodies is applied for the first time for high-speed trains in dynamical modelling, with flexible car bodies for ride comfort analysis. Furthermore, it can be used for parametrical studies focusing on ride comfort, thereby offering a quite beneficial framework for addressing the challenges of ride comfort analysis in high-speed rail vehicles. Improvements for and analyses of other aspects are also possible, since the optimization and other useful libraries are readily available in Dymola/Modelica.

This paper describes the conception and implementation of the research and control center VeLABi for the development of automation functions up to a fully autonomous inland vessel. A high-tech simulator was designed and built as the central component of VeLABi. It allows simulative developments and demonstrations of automation functions. Furthermore, ongoing research activities aim at transforming the simulator into a remote operators’ stand to remotely control inland vessels and, in case of an autonomous vessel, to provide corresponding monitoring and emergency intervention functions.

This paper explores the potential for connected public-transport (PT) mobility as an alternative to motorized private transport (MPT) in medium-sized cities. Despite the high demand for MPT, it occupies a lot of space and contributes to conflicts and reduced livability. The more sustainable mobility solution of PT, however, is often considered slow, unreliable, and uncomfortable. To overcome these issues, the authors investigate the state-of-the-art research of connected PT mobility, including ways to quantify mobility behavior, micro- and macro-simulations of traffic flow, and the potential of not-yet-established modes of transport such as Mobility on Demand (MoD) for last-mile transportation. MoD could reduce the drawbacks of PT and provide sufficient and sustainable mobility to all citizens, including those in rural areas. To achieve this, precise information on individual traffic flows is needed, including origin–destination (OD) relations of all trips per day. The paper outlines a two-step approach involving the expansion of OD relations to include all modes of transport and diurnal variation, followed by microscopic traffic simulations and macroscopic optimization to determine potentials for on-demand offers within inner-city traffic. The paper concludes by calling for critical questioning of the approach to validate and verify its effectiveness.

This paper presents the establishment of a relationship between coil spring fatigue life and automotive vertical vibration using neural network. During an automotive suspension design process, the suspension components are designed with the consideration of structure strength and fatigue life as well as the effects toward automotive ride. Hence, it is important to have a functional mathematical model to predict the fatigue life and automotive life simultaneously. To build the mathematical model, a multibody kinematic quarter model of suspension system was constructed to simulate force and acceleration time histories from the suspension system and the sprung mass of the vehicle model. The force time histories were used to predict the fatigue life of the coil spring while the acceleration time histories were converted into ISO vertical vibration index. A neural network model was created and used to fit the spring fatigue life and vehicle vertical vibration into a mathematical function. The neural network with 1 hidden layer and 2 neurons has shown a good fitting of the data with coefficient of determination as high as 0.88, 0.98, 0.96 for training, validation and testing, respectively. This constructed neural network serves to predict the vehicle vertical vibration using the spring fatigue life and suspension natural frequencies as input, and hence reduce the automotive suspension design process.