The article provides a new Markovian-Bayesian network model to evaluate the probability of accident associated with the circulation of trains along a given high speed or conventional railway line with special consideration to human error. This probability increases as trains pass throughout the different elements encountered along the line. A Bayesian network, made up of a sequence of several connected Bayesian subnetworks, is used. A subnetwork is associated with each element in the line that implies a concentrated risk of accident or produces a change in the driver's attention, such as signals, tunnel, or viaduct entries or exits, etc. Bayesian subnetworks are also used to reproduce segments without signals where some elements add continuous risks, such as rolling stock failures, falling materials, slope slides in cuttings and embankments, etc. All subnetworks are connected with the previous one and some of them are multi-connected because some consequences are dependent on previous errors. Because driver's attention plays a crucial role, its degradation with driving time and the changes due to seeing light signals or receiving acoustic signals is taken into consideration. The model updates the driver's attention level and accumulates the probability of accident associated with the different elements encountered along the line. This permits us to generate a continuously increasing risk graph that includes continuous and sudden changes indicating where the main risks appear and whether or not an action must be taken by the infrastructure manager. Sensitivity analysis allows the relevant and irrelevant parameters to be identified avoiding wastes of time and money by concentrating safety improvement actions only on the relevant ones. Finally, some examples are used to illustrate the model. In particular, the case of the Orense-Santiago de Compostela line, where a terrible accident took place in 2013.

The criteria currently employed for the design of railway infrastructure on ballasted track are based on the elements of the superstructure, traffic, and bearing capacity of the subgrade. In high-speed railway, the elements of the superstructure are relatively well defined because they are based on existing experience. The category of traffic is a variable that has an influence from the point of view of the number of axles, weight per axle, and speed; i.e., it is determined a priori, depending on the operating characteristics of the line in question. The third and last point refers to the quality of the subgrade that is based on the values of the California bearing ratio test. The quality of the subgrade has a considerable influence on the behavior of the track that manifests itself in the value the deformation and settlement takes, which is caused to the rail on the passage of traffic. This is attributable to the relationship between the vertical track stiffness and the quality of the subgrade. In turn, ...

In this paper, we deal with the observability problem in traffic networks and the optimal location of counting and scanning devices. After explaining what we mean by observability, the problems of what to observe, how to observe traffic data and how to incorporate prior or obsolete information together with the cases of genuine and pseudo-samples of flow data are discussed. Plate scanning information is dealt with and the flow amount of information measure of information corresponding to a subset of scanned links is analysed. Some pivoting and matrix techniques are given for solving the most common problems of observability of traffic flows in a network. Finally, the problem of optimal location of counters and plate scanning cameras is analysed and several examples are given.