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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.

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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.

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