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

Novel efficient technologies in Europe for axle bearing condition monitoring – the MAXBE project

Axle bearing damage with possible catastrophic failures can cause severe disruptions or even dangerous derailments, potentially causing loss of human life and leading to significant costs for railway infrastructure managers and rolling stock operators. Consequently the axle bearing damage process has safety and economic implications on the exploitation of railways systems. Therefore it has been the object of intense attention by railway authorities as proved by the selection of this topic by the European Commission in calls for research proposals. The MAXBE Project (http://www.maxbeproject.eu/), an EU-funded project, appears in this context and its main goal is to develop and to demonstrate innovative and efficient technologies which can be used for the onboard and wayside condition monitoring of axle bearings. The MAXBE (interoperable monitoring, diagnosis and maintenance strategies for axle bearings) project focuses on detecting axle bearing failure modes at an early stage by combining new and existing monitoring techniques and on characterizing the axle bearing degradation process. The consortium for the MAXBE project comprises 18 partners from 8 member states, representing operators, railway administrations, axle bearing manufactures, key players in the railway community and experts in the field of monitoring, maintenance and rolling stock. The University of Porto is coordinating this research project that kicked-off in November 2012 and it is completed on October 2015. Both on-board and wayside systems are explored in the project since there is a need for defining the requirement for the onboard equipment and the range of working temperatures of the axle bearing for the wayside systems. The developed monitoring systems consider strain gauges, high frequency accelerometers, temperature sensors and acoustic emission. To get a robust technology to support the decision making of the responsible stakeholders synchronized measurements from onboard and wayside monitoring systems are integrated into a platform. Also extensive laboratory tests were performed to correlate the in situ measurements to the status of the axle bearing life. With the MAXBE project concept it will be possible: to contribute to detect at an early stage axle bearing failures; to create conditions for the operational and technical integration of axle bearing monitoring and maintenance in different European railway networks; to contribute to the standardization of the requirements for the axle bearing monitoring, diagnosis and maintenance. Demonstration of the developed condition monitoring systems was performed in Portugal in the Northern Railway Line with freight and passenger traffic with a maximum speed of 220 km/h, in Belgium in a tram line and in the UK. Still within the project, a tool for optimal maintenance scheduling and a smart diagnostic tool were developed. This paper presents a synthesis of the most relevant results attained in the project. The successful of the project and the developed solutions have positive impact on the reliability, availability, maintainability and safety of rolling stock and infrastructure with main focus on the axle bearing health

Pattern Recognition for Defect Detection in Uncontrolled Environment Railway Applications

The rise in prominence of safety and maintenance cost saving related issues in railway systems is becoming more and more an important driver in the design and deployment of sophisticated Wayside Train Monitoring Systems (WTMS). In the last 20 years computer vision based WTMS have evolved from simple Hot Axle Bearing Detectors (HABD) to sophisticated Video Monitoring Systems (VMS)

A multi-criteria methodology to evaluate the optimal location of a multifunctional railway portal on the railway network

The installation of a multifunctional railway portal (or TCCS - Train Conformity Check system) can contribute to improve the safety of a railway infrastructure. The TCCS can detect the conformity of the trains traveling along the tracks, and can transfer the status information to a main traffic control center. This paper proposes a methodological approach based on Analytic Hierarchy Process (AHP) to evaluate the optimal locations to install a TCCS on a railway section. The eligibility and ranking of the potential sites have been defined with respect to constraints related to the rail line track layout and geometry, the TCCS technological features, and the required safety distance allowing the train to stop. The proposed approach has been applied to a real case study on the Italian railway

Towards an intelligent and automated platform for railway Asset Management

This paper presents the objectives and the main expected results from IN2SMART Project, funded by the SHIFT2RAIL Joint Undertaking and the European Commission, within the SHIFT2RAIL Research Programme. This project contributes to the development of an intelligent and automated platform for Asset Management decision-making, focused on the planning of predictive, condition and risk-based Asset Management activities. Based on a framework for Asset Management aligned with international standards, the platform receives inputs from tools and models for predictive analytics that are able to extract information on current and future asset condition, using heterogeneous data from the field. In particular, nowcasting and forecasting methodologies, diagnostics and anomaly detection techniques and indicators derived from Risk, RAMS and LCC analysis are used to support decision-making. Finally, real-world business cases are presented to show the expected applicability of the proposed automated platform and the usefulness of the relevant methodology

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

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

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

Resonant electromagnetic vibration harvesters feeding sensor nodes for real-time diagnostics and monitoring in railway vehicles for goods transportation: A numerical-experimental analysis

In this paper, the results of a combined numericalexperimental analysis on a system, part of a wireless sensor network, composed by a resonant electromagnetic energy harvester, a suitable power electronic interface and a sensor node are presented and discussed. Such a system is to be used onboard for real-time diagnostics and monitoring in railway vehicles for goods transportation

Experimental analysis of mechanical vibrations and wind speed for a rail vehicle WSN fed by energy harvesters

Real-time diagnostic and monitoring on railway vehicles for goods transportation is considered. The results of a preliminary experimental activity aimed to model mechanical vibrations and wind speed for autonomous monitoring and diagnostic wireless sensor network fed by means of energy harvesters are reported and commented