This paper discusses the problem of optimizing the use of scanning resources for route flow estimation in traffic networks. Three problems are considered: (1) Minimize the number of cameras to be used for estimating a given subset of route flows; (2) identify the subsets of links to be scanned for a given number of available cameras, assuming that several runs can be done; and (3) solve the previous problems with consideration of scanning errors and error recovery. In addition to the main objective, which consists of minimizing the number of cameras, minimizing the total number of links to be scanned in all runs and maximizing the number of determined route flows are also considered as secondary and tertiary objectives, respectively. A simple and a medium-size real network are used to illustrate the proposed methods and prove the feasibility of the proposed methodology.

We deal with the problem of observability of a given subset V ₁ of flows in terms of another subset V ₂ , no matter which type of flows [link, origin-destination (OD), route, node, plate scanned, etc.] they contain or whether they are mixed types. Two problems are stated: The first consists of determining which subsets of flows in V ₁ can be calculated in terms of the observed flows V ₂ . The second consists of determining which subset of flows V ₂ needs to be observed to calculate a given subset V ₁ . A theorem providing necessary and sufficient conditions for observability is provided and used in the proposed methods to solve the two problems. Two theorems, one lemma, and one corollary provide the bases for optimizing the numerical procedures to solve these problems. Some examples of applications are used to illustrate the proposed methods.

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, ...

It is demonstrated that the minimum number of sensors required to know all link flows in a traffic network can be determined only if path information is available. However, not all paths need to be enumerated but, at most, a small subset defining the rank rw of the link-path incidence matrix W. If this rank for a reduced subset of paths is already m - n , where m and n are the number of links and noncentroid nodes, respectively, we can conclude that m - n sensors are sufficient. It is also shown that the formulas providing the dependent link flows in terms of the independent link flows can be obtained by the node-based or path-based approaches with the same results only when rw = m - n . Finally, an algorithm to obtain the small subsets of linearly independent path vectors is given. The methods are shown by a parallel network example and the Ciudad Real and Cuenca networks, for which the savings in link counts with respect to the m - n bound are larger than 16%. The corresponding savings in path enumeration are larger than 80%.