The paper discusses the current state of research concerning railway Network timetabling and traffic management. Timetable effectiveness is governed by frequency, regularity, accurate running, recovery and layover times, as well as minimal headway, buffer times and waiting times. Analytic (queuing) models and stochastic microsimulation are predominantly used for estimation of waiting times and capacity consumption anlong corridors and in stations, while combinatorial models and stability analysis are suitable for Network timetable optimisation. Efficient traffic management can be achieved by real-time monitoring, fusion, analysis and rescheduling of railway traffic in case of disturbances. Real-time simulation, optimisation and impact evaluation of dispatching measures can improve the effectiveness of rescheduling and traffic management. The display of Dynamic signal and track occupancy data in driver cabins, as Route Lint developed by ProRail, can support anticipative actions of the driver in order to reduce knock-on delays and increase throughput.

This paper deals with the problem of optimizing two-stage structure decision making units (DMUs) where the activity and the performance of two-stage DMU in one period e ect on its efficiency in the next period. To evaluate such systems the e ect of activities in one period on ones in the next term must be considered. To do so, here a Dynamic DEA approach presented to measure the performance of such Network units. According to the results of proposed Dynamic model the inefficiencies of DMUs improve considerably. Additionally, in models which measure efficiency score, undesirable outputs are mostly treated as inputs, which do not reect the true production process. This paper proposes an alternative method in dealing with bad outputs. Statistical analysis of sub-efficiencies, i.e. efficiency score of each stage, during all periods represents useful information about the total performance of the stage over all periods.

Social Network monitoring (SNM) can play a significant role in everyone’ s life. Recent studies show the importance and increasing interests in the subject by modeling and monitoring the communications between Network members over time by treating the collected observations as longitudinal data. Typically, the tendency for modeling social Networks, considering the dependency of an outcome variable on the covariates, is growing recently. However, these studies fail to incorporate the possible correlation between responses in the proposed models. In this paper, we use generalized linear mixed models (GLMMs), also referred to as random effects models, to model a social Network according to the attributes of nodes in which the nodes take a role of random effect or hidden effect in modeling. In order to estimate the regression parameters, Monte Carlo expectation maximization (MCEM) algorithm is used to maximize the likelihood function. In our simulation studies, we applied root mean square error (RMSE) and standard deviation criteria to select an appropriate model for the simulated data. Results indicate zero inflated Poisson mixed as an appropriate model for the data. In addition, compared to the other studies, our simulation study demonstrates an improvement in the average run length (ARL).