A three-dimensional vehicle-track coupled dynamics model, with the inclusion of the axle-box bearings, has been developed, which considers the coupling effects between the bearing and the vehicle components. Some non-linear factors, such as time-varying bearing stiffness, bearing clearance, wheel-polygonal wear and the non-linear wheel-rail contact forces, are also considered. Dynamic analysis has been implemented through numerical simulations that consider different amplitudes and harmonic orders of measured and idealised polygonal wheel wear. Both model analysis and validation were supported by field tests. Results show that the roller-raceway contact forces increases with vehicle speed and the amplitude of polygonal wear. However, the influence of wear upon the axle-box bearing is found to be small in the low-speed range, but at higher operating speeds, polygonal wear leads to a rapid increase in bearing contact forces and the potential for degradation. During vehicle operations, both high order (17th to 20th order) and lower order (1st to 4th order) wheel-polygonal wear patterns are generated. The former's influence on axle-box bearing forces is more significant. Hence, the identification and rectification of this order of wear should form part of maintenance practice and potentially also be considered in the design of axle-box bearings for high-speed trains.

Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10—focusing on their efficacy in vehicle detection across multiple classes such as bicycles, buses, cars, motorcycles, and trucks. Using a range of performance metrics, including precision, recall, F1 score, and detailed confusion matrices, we evaluate the performance characteristics of each model.The findings reveal that YOLOv10 generally outperformed YOLOv8, particularly in detecting smaller and more complex vehicles like bicycles and trucks, which can be attributed to its architectural enhancements. Conversely, YOLOv8 showed a slight advantage in car detection, underscoring subtle differences in feature processing between the models. The performance for detecting buses and motorcycles was comparable, indicating robust features in both YOLO versions. This research contributes to the field by delineating the strengths and limitations of these models and providing insights into their practical applications in real-world scenarios. It enhances understanding of how different YOLO architectures can be optimized for specific vehicle detection tasks, thus supporting the development of more efficient and precise detection systems.

A three-dimensional, vehicle–track coupled dynamics model has been developed that accurately reflects the dynamic performance of a high-speed train during operation. The model includes novel elements that allow detailed consideration of the transmission system and axle box bearings with gear time-varying mesh stiffness, bearing stiffness and bearing clearance. And then, it was extensively validated by comparing the results of the simulation with those of experimental tests. The temperature characteristics of the axle box bearing of the motor car and the trailer were analysed and the mathematical model was subsequently validated through long-term experimental field tests. The results indicated that the loads acting on the axle box bearing closest to the gearbox of the motor car (bearing 1) are significantly higher than those distant to the gearbox (bearing 2), the cause being differences in structural differences, which also lead to higher local temperatures. Conversely, when considering the trailer car, the dynamic performance and hence vibration and temperature of bearing 1 and bearing 2 were almost identical, this being a result of similarities in their structure and mounting arrangement. In summary, the temperature and vibration characteristics of axle box bearing can be used for its related assessment, design and maintenance.

This review addresses efforts made within the field of vehicle dynamics to contribute to the energy efficient operation of road and rail vehicles. Selected investigations of the research community to develop technology solutions to reduce energy consumption are presented. The study explores the impact and potential of relatively mature technologies such as regenerative braking, but also recent research directions that are seeking to develop solutions such as regenerative suspensions, a concept common to both transport modes. Specifically for road vehicles, the study includes rear wheel steering, camber control and torque vectoring, and for rail vehicles active steering and light-weighting. Operationally, there would appear to be great potential for rail vehicles in the wider adoption of connected driver advisory systems, and automatic train control systems, including the application of machine learning techniques to optimise train speed trajectories across a route. Similarly, for road vehicles, predictive and cooperative eco-driving strategies show potential for significant energy savings for connected autonomous vehicles.

The tangential contact forces that arise at the interface between the wheel of a railway vehicle and the rail provide all the traction, braking, and guidance required by the vehicle. These forces are the result of microslip or creepage and can become unstable exciting vibration of the wheel, particularly at frequencies corresponding to the wheel's axial (and radial) modes. Although theories exist for predicting these creep forces and their relationship to creepages, most lack experimental verification in the characterization of the falling friction coefficient during unsteady squealing. This paper presents some new results from a project which aims to develop a complete, validated model of curve squeal noise generation accounting for friction characteristics, excitation due to unstable forces between the wheel and rail and vehicle dynamic behaviour. The model includes wheel and track dynamic response and acoustic radiation. As part of the project, a twin disc rig has been modified to provide experimental data for the validation of the model and measurements were made of the lateral force and dynamic response of the rollers due to varying amounts of lateral creepage during squealing. The main feature of the twin disc rig compared with previous research [1] is that the contact force measuring system measures the contact forces at the web of the rollers and therefore close to the contact patch and through a slipring arrangement enables the lateral vibration of both rollers in relation to squeal be measured with relative ease. In this paper, detailed descriptions of the twin disc rig and the test method developed are given. An outline of the squeal model is also presented. Results from the tests have been compared with the prediction from the squeal model and with available theories and showed good agreement.