An energy-efficient train trajectory describing the motion of a single train can be used as an input to a driver guidance system or to an automatic train control system. The solution for the best trajectory is subject to certain operational, geographic, and physical constraints. There are two types of strategies commonly applied to obtain the energy-efficient trajectory. One is to allow the train to coast, thus using its available time margin to save energy. The other one is to control the speed dynamically while maintaining the required journey time. This paper proposes a distance-based train trajectory searching model, upon which three optimization algorithms are applied to search for the optimum train speed trajectory. Instead of searching for a detailed complicated control input for the train traction system, this model tries to obtain the speed level at each preset position along the journey. Three commonly adopted algorithms are extensively studied in a comparative style. It is found that the ant colony optimization (ACO) algorithm obtains better balance between stability and the quality of the results, in comparison with the genetic algorithm (GA). For offline applications, the additional computational effort required by dynamic programming (DP) is outweighed by the quality of the solution. It is recommended that multiple algorithms should be used to identify the optimum single-train trajectory and to improve the robustness of searched results.

Rails are systematically inspected for internal and surface defects using various non-destructive evaluation (NDE) techniques. During the manufacturing process, rails are inspected using automated optical cameras and eddy current sensing systems for any surface damage, while the presence of internal defects is assessed through ultrasonic inspection. Similarly, ultrasonic transducers and magnetic induction sensors have been extensively used by the rail industry for the inspection of rails in-service. More recently, automated vision techniques and hybrid systems based on the simultaneous use of pulsed eddy current probes and conventional ultrasonic probes have been introduced for the high-speed inspection of rail tracks. Other NDE techniques, such as electromagnetic acoustic transducers, laser ultrasonics, guided waves, and alternating current field measurement probes, are also under development for application in the rail industry. This paper comprehensively reviews NDE methodologies in use around Europe and North America for rail defect detection. This includes a detailed overview of the background theory and the techniques used to incorporate condition data into maintenance procedures. It also presents a review of the current state-of-the-art in NDE of railways coupled with a discussion of future developments and novel inspection methodologies in the field.

This paper presents a view of the current state of monitoring track geometry condition from in-service vehicles. It considers technology used to provide condition monitoring; some issues of processing and the determination of location; how things have evolved over the past decade; and what is being, or could/should be done in future research. Monitoring railway track geometry from an in-service vehicle is an attractive proposition that has become a reality in the past decade. However, this is only the beginning. Seeing the same track over and over again provides an opportunity for observing track geometry degradation that can potentially be used to inform maintenance decisions. Furthermore, it is possible to extend the use of track condition information to identify if maintenance is effective, and to monitor the degradation of individual faults such as dipped joints. There are full unattended track geometry measurement systems running on in-service vehicles in the UK and elsewhere around the world, feeding their geometry measurements into large databases. These data can be retrieved, but little is currently done with the data other than the generation of reports of track geometry that exceeds predefined thresholds. There are examples of simpler systems that measure some track geometry parameters more or less directly and accurately, but forego parameters such as gauge. Additionally, there are experimental systems that use mathematics and models to infer track geometry using data from sensors placed on an in-service vehicle. Finally, there are systems that do not claim to measure track geometry, but monitor some other quantity such as ride quality or bogie acceleration to infer poor track geometry without explicitly measuring it.

Energy saving on electrified railways has been studied for many years and the technical solution is usually provided by a combination of driving strategy (e.g. coasting), regenerative braking and energy storage systems. An alternative approach is for the driver (or automatic train operation system if fitted) to manage energy consumption more efficiently. A formal method for optimising traction energy consumption during a single-train journey by trading-off reductions in energy against increases in running time has been demonstrated. The balance between saving energy and running faster has been investigated by designing a fitness function with variable weightings. Energy savings were found, both qualitatively and quantitatively, to be affected by acceleration and braking rates, and, by running a series of simulations in parallel with a genetic algorithm search method, the fitness function was used to identify optimal train trajectories. The influence of the fitness function representation on the search results was also explored.

Condition monitoring of track geometry from sensors mounted on an in-service vehicle offers continual monitoring of track geometry that can aid track maintenance strategies. Mounting and maintaining a full track geometry recording system on an in-service vehicle is an expensive proposition as the commonly used optical sensors are difficult to keep working in the dirty railway environment. A simpler and more cost-effective alternative is to estimate track geometry using a small number of robust sensors such as accelerometers and rate gyroscopes, from which a worthwhile proportion of geometric quality measures and specific irregularities can be identified. This paper describes the theory and practical results of using a bogie-mounted pitchrate gyro to obtain mean vertical alignment, conditional on the secondary vertical damper geometry. Left and right axlebox-mounted accelerometers can be added to provide short wavelength irregularity, if required. Results from trials on Tyne and Wear Metro vehicles and on a Class 175 mainline vehicle demonstrate effective vertical irregularity monitoring, in particular the ability to monitor vertical irregularity over a wide range of vehicle speeds down to about 1 ms^-1, where vertically sensing accelerometers combined with displacement transducers are unable to function correctly.