Delay prediction system for large-scale railway networks based on big data analytics

State-of-the-art train delay prediction systems do not exploit historical train movements data collected by the railway information systems, but they rely on static rules built by expert of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven train delay prediction system for largescale railway networks which exploits the most recent Big Data technologies and learning algorithms. In particular, we propose a fast learning algorithm for predicting train delays based on the Extreme Learning Machine that fully exploits the recent in-memory large-scale data processing technologies. Our system is able to rapidly extract nontrivial information from the large amount of data available in order to make accurate predictions about different future states of the railway network. Results on real world data coming from the Italian railway network show that our proposal is able to improve the current state-of-the-art train delay prediction systems

Natural language processing for aviation safety : Extracting knowledge from publicly-available loss of separation reports

Background: The air traffic management (ATM) system has historicallycoped with a global increase in traffic demand ultimately leading toincreased operational complexity.When dealing with the impact of this increasing complexity on systemsafety it is crucial to automatically analyse the losses of separation(LoSs) using tools able to extract meaningful and actionableinformation from safety reports.Current research in this field mainly exploits natural languageprocessing (NLP) to categorise the reports,with the limitations that theconsidered categories need to be manually annotated by experts andthat general taxonomies are seldom exploited.Methods: To address the current gaps,authors propose to performexploratory data analysis on safety reports combining state-of-the-arttechniques like topic modelling and clustering and then to develop analgorithm able to extract the Toolkit for ATM Occurrence Investigation(TOKAI) taxonomy factors from the free-text safety reports based onsyntactic analysis.TOKAI is a tool for investigation developed by EUROCONTROL and itstaxonomy is intended to become a standard and harmonisedapproach to future investigations.Results: Leveraging on the LoS events reported in the publicdatabases of the Comisi n de Estudio y An lisis de Notificaciones deIncidentes de Tr nsito A reo and the United Kingdom AirproxBoard,authors show how their proposal is able to automaticallyextract meaningful and actionable information from safetyreports,other than to classify their content according to the TOKAItaxonomy.The quality of the approach is also indirectly validated by checking theconnection between the identified factors and the main contributor ofthe incidents.Conclusions: Authors' results are a promising first step toward the fullautomation of a general analysis of LoS reports supported by resultson real-world data coming from two different sources.In the future,authors' proposal could be extended to othertaxonomies or tailored to identify factors to be included in the safetytaxonomies.KeywordsATM, Safety, Resilience, Natural Language Processing, Losses ofSeparation, Safety Reports, TOKA

Use of Advanced Video Surveillance and Communication Technologies for Remote Monitoring of Protected Sites

The provision of broadband multimedia services to residential users belonging to the last network mile is one of the most interesting technological challenges of this end of millennium. Some current R&D topics concerning this technological field (e.g. IV and V CEC Framework Programmes) are focusing their attention on the provision of services involving social benefits in terms of increased quality of the life for citizens residing in local communities (e.g. towns and municipalities)

A coupled MRF model for SAR image restoration and edge-extraction

A coupled stochastic image model for restoration of Synthetic Aperture Radar (SAR) images affected by speckle and for extraction of related intensity discontinuities is presented. The two problems are ill-posed [2], as defined by Hadamard [7], and require the use of regularization methods. It is shown that the coupled Gibbs-Markov Random Fields technique for piecewise constant image reconstruction [10] can be utilized by means of the SAR-image observation model proposed in [5]. Simulation results on test images are reported

Validation of data analytics and optimization algorithms within an Intelligent Asset Management System for rail signalling [Validazione di algoritmi di analisi dati e di ottimizzazione nell\u2019ambito di un sistema intelligente di Asset Management per il segnalamento ferroviario]

This paper describes the implementation phase of the Intelligent Asset Management System (IAMS), developed within the Shift2Rail IN2SMART project, under test and validation within its follow-up project, IN2SMART2. The innovative approach to Asset Management is based on techniques and models for data analysis and decision support, able to take into account the knowledge on asset status, the operational constraints of the rail sector and the main target for rail operators. The aim is to move towards an automated decision process and a reduction of human effort in decision-making. The IAMS prototype development and validation is described. In addition, the in-field implementation to the signalling system of an Italian metro line is presented, describing the designed additional functionalities of the system, as well as the considered Key Performance Indicators for its test and the benefits of the approach

Territorial analysis by fusion of LANDSAT and SAR data

A highly informative content makes visible and infrared images the most used remotely sensed data (generally speaking) in earth resource and environmental analysis. On the other hand, sensitivity to surface roughness, water content, and independence of weather conditions and sunlight are the features that justify the growing interest and use of microwave radar data. The previous considerations clearly indicate data fusion as a key point for remote-sensing image classification. In this paper, a knowledge-based system to exploit such numerous and diverse sources of information is proposed. The authors started with the problem of fusing Landsat- MSS and Seasat-SAR images for terrain classification in order to increase the reliability of results with respect to single-sensor analysis. A new approach to the fusion of 2-D images, called the 'region overlapping' technique, is employed, and its advantages for terrain classification are shown. Experimental results are presented and discussed to show the interest of the approach

Train Delay Prediction Systems: A Big Data Analytics Perspective

Current train delay prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Instead, they rely on static rules built by experts of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven Train Delay Prediction System (TDPS) for large-scale railway networks which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs

Dynamic delay predictions for large-scale railway networks: Deep and shallow extreme learning machines tuned via thresholdout

Current train delay (TD) prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of endogenous (i.e., generated by the railway system itself) and exogenous (i.e., related to railway operation but generated by external phenomena) data available. Additionally, they are not designed in order to deal with the intrinsic time varying nature of the problem (e.g., regular changes in the nominal timetable, etc.). The purpose of this paper is to build a dynamic data-driven TD prediction system that exploits the most recent tools and techniques in the field of time varying big data analysis. In particular, we map the TD prediction problem into a time varying multivariate regression problem that allows exploiting both historical data about the train movements and exogenous data about the weather provided by the national weather services. The performance of these methods have been tuned through the state-of-the-art thresholdout technique, a very powerful procedure which relies on the differential privacy theory. Finally, the performance of two efficient implementations of shallow and deep extreme learning machines that fully exploit the recent in-memory large-scale data processing technologies have been compared with the current state-of-the-art TD prediction systems. Results on real-world data coming from the Italian railway network show that the proposal of this paper is able to remarkably improve the state-of-the-art systems

Advanced analytics for train delay prediction systems by including exogenous weather data

State-of-The-Art train delay prediction systems neither exploit historical data about train movements, nor exogenous data about phenomena that can affect railway operations. They rely, instead, on static rules built by experts of the railway infrastructure based on classical univariate statistics. The purpose of this paper is to build a data-driven train delay prediction system that exploits the most recent analytics tools. The train delay prediction problem has been mapped into a multivariate regression problem and the performance of kernel methods, ensemble methods and feed-forward neural networks have been compared. Firstly, it is shown that it is possible to build a reliable and robust data-driven model based only on the historical data about the train movements. Additionally, the model can be further improved by including data coming from exogenous sources, in particular the weather information provided by national weather services. Results on real world data coming from the Italian railway network show that the proposal of this paper is able to remarkably improve the current state-of-The-Art train delay prediction systems. Moreover, the performed simulations show that the inclusion of weather data into the model has a significant positive impact on its performance

Patients with Hereditary Hemorrhagic Telangectasia (HHT) exhibit a deficit of polymorphonuclear cell and monocyte oxidative burst and phagocytosis: A possible correlation with altered adaptive immune responsiveness in HHT

Hereditary Hemorrhagic Telangiectasia (HHT) is a rare genetic disease characterized by mutations occurring in the endoglin and ALK-1, two receptors of transforming growth factor-beta 1. From a pathogenic point of view, a possible involvement of the immune system in HHT has been suggested since a mononuclear cell infiltrate was found around the area of telangiectascs. Up until now, no information has been available about the role played by leukocytes in HHT and the mechanisms elicited by secretion of their mediators. However, the fact that a deficit of adaptive immunity in HHT has been reported in a companion paper in this issue will represent a great contribution to the understanding of HHT pathogenesis. The purpose of this study was to evaluate whether patients with HHT manifest also alterations in the innate immune response. Therefore, the phenotype of T, B and natural killer lymphocytes, serum immunoglobulin levels, phagocytosis and oxidative burst activity exerted by polymorphonuclear cells (PMN) and monocytes (MO) were analyzed in 22 patients. Twenty individuals demonstrated single or multiple deficits of PMN and MO functions, while the immunophenotype of lymphocytes and serum concentrations of immunoglobulins were normal. To the best of our knowledge, this is the first demonstration of a reduction in IIMN and MO functions in HHT, thus suggesting a higher susceptibility to infectious complications in these patients. The relationship between innate immune deficits and T helper 1 and monocyte-derived cytokine dysfunction in HHT, as previously reported, is discussed